Final year IEEE 2016-2017 PROJECTS TITLES (IEEE 2016 papers) For ME,M.Tech,BE,B.Tech (.net,java,matlab,vlsi,ns2,mech,power elc & power sys)

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CSE IEEE PROJECTS -
2016-2017
S3I_DN16_001 - Dynamic and Public Auditing with Fair Arbitration for Cloud
Data
Cloud users no longer physically possess their data, so how to ensure the
integrity of their outsourced data becomes a challenging task. Recently proposed
schemes such as “provable data possession” and “proofs of retrievability” are
designed to address this problem, but they are designed to audit static archive data and
therefore lack of data dynamics support. Moreover, threat models in these schemes
usually assume an honest data owner and focus on detecting a dishonest cloud service
provider despite the fact that clients may also misbehave. This paper proposes a public
auditing scheme with data dynamics support and fairness arbitration of potential
disputes. In particular, we design an index switcher to eliminate the limitation of
index usage in tag computation in current schemes and achieve efficient handling of
data dynamics. To address the fairness problem so that no party can misbehave
without being detected, we further extend existing threat models and adopt signature
exchange idea to design fair arbitration protocols, so that any possible dispute can be
fairly settled. The security analysis shows our scheme is provably secure, and the
performance evaluation demonstrates the overhead of data dynamics and dispute
arbitration are reasonable.
S3I_DN16_002 - Enabling Cloud Storage Auditing with Verifiable Outsourcing
of Key Updates
Key-exposure resistance has always been an important issue for in-depth cyber
defence in many security applications. Recently, how to deal with the key exposure
problem in the settings of cloud storage auditing has been proposed and studied. To
address the challenge, existing solutions all require the client to update his secret keys
in every time period, which may inevitably bring in new local burdens to the client,
especially those with limited computation resources, such as mobile phones. In this
paper, we focus on how to make the key updates as transparent as possible for the
client and propose a new paradigm called cloud storage auditing with verifiable
outsourcing of key updates. In this paradigm, key updates can be safely outsourced to
some authorized party, and thus the key-update burden on the client will be kept
minimal. In particular, we leverage the third party auditor (TPA) in many existing
public auditing designs, let it play the role of authorized party in our case, and make it

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in charge of both the storage auditing and the secure key updates for key-exposure
resistance. In our design, TPA only needs to hold an encrypted version of the client's
secret key while doing all these burdensome tasks on behalf of the client. The client
only needs to download the encrypted secret key from the TPA when uploading new
files to cloud. Besides, our design also equips the client with capability to further
verify the validity of the encrypted secret keys provided by the TPA. All these salient
features are carefully designed to make the whole auditing procedure with key
exposure resistance as transparent as possible for the client. We formalize the
definition and the security model of this paradigm. The security proof and the
performance simulation show that our detailed design instantiations are secure and
efficient.
S3I_DN16_003 - Providing User Security Guarantees in Public Infrastructure
Clouds
The infrastructure cloud (IaaS) service model offers improved resource
flexibility and availability, where tenants – insulated from the minutiae of hardware
maintenance – rent computing resources to deploy and operate complex systems.
Large-scale services running on IaaS platforms demonstrate the viability of this
model; nevertheless, many organizations operating on sensitive data avoid migrating
operations to IaaS platforms due to security concerns. In this paper, we describe a
framework for data and operation security in IaaS, consisting of protocols for a trusted
launch of virtual machines and domain-based storage protection. We continue with an
extensive theoretical analysis with proofs about protocol resistance against attacks in
the defined threat model. The protocols allow trust to be established by remotely
attesting host platform configuration prior to launching guest virtual machines and
ensure confidentiality of data in remote storage, with encryption keys maintained
outside of the IaaS domain. Presented experimental results demonstrate the validity
and efficiency of the proposed protocols. The framework prototype was implemented
on a test bed operating a public electronic health record system, showing that the
proposed protocols can be integrated into existing cloud environments.
S3I_DN16_004 - Service Usage Classification with Encrypted Internet Traffic in
Mobile Messaging Apps
The rapid adoption of mobile messaging Apps has enabled us to collect massive
amount of encrypted Internet traffic of mobile messaging. The classification of this
traffic into different types of in-App service usages can help for intelligent network
management, such as managing network bandwidth budget and providing quality of
services. Traditional approaches for classification of Internet traffic rely on packet
inspection, such as parsing HTTP headers. However, messaging Apps are increasingly
using secure protocols, such as HTTPS and SSL, to transmit data. This imposes

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significant challenges on the performances of service usage classification by packet
inspection. To this end, in this paper, we investigate how to exploit encrypted Internet
traffic for classifying in-App usages. Specifically, we develop a system, named
CUMMA, for classifying service usages of mobile messaging Apps by jointly
modeling user behavioral patterns, network traffic characteristics and temporal
dependencies. Along this line, we first segment Internet traffic from traffic-flows into
sessions with a number of dialogs in a hierarchical way. Also, we extract the
discriminative features of traffic data from two perspectives: (i) packet length and (ii)
time delay. Next, we learn a service usage predictor to classify these segmented
dialogs into single-type usages or outliers. In addition, we design a clustering Hidden
Markov Model (HMM) based method to detect mixed dialogs from outliers and
decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA
enables mobile analysts to identify service usages and analyze end-user in-App
behaviors even for encrypted Internet traffic. Finally, the extensive experiments on
real-world messaging data demonstrate the effectiveness and efficiency of the
proposed method for service usage classification.
S3I_DN16_005 - Text Mining the Contributors to Rail Accidents
Rail accidents represent an important safety concern for the transportation
industry in many countries. In the 11 years from 2001 to 2012, the U.S. had more than
40 000 rail accidents that cost more than $45 million. While most of the accidents
during this period had very little cost, about 5200 had damages in excess of $141 500.
To better understand the contributors to these extreme accidents, the Federal Railroad
Administration has required the railroads involved in accidents to submit reports that
contain both fixed field entries and narratives that describe the characteristics of the
accident. While a number of studies have looked at the fixed fields, none have done
an extensive analysis of the narratives. This paper describes the use of text mining
with a combination of techniques to automatically discover accident characteristics
that can inform a better understanding of the contributors to the accidents. The study
evaluates the efficacy of text mining of accident narratives by assessing predictive
performance for the costs of extreme accidents. The results show that predictive
accuracy for accident costs significantly improves through the use of features found
by text mining and predictive accuracy further improves through the use of modern
ensemble methods. Importantly, this study also shows through case examples how the
findings from text mining of the narratives can improve understanding of the
contributors to rail accidents in ways not possible through only fixed field analysis of
the accident reports.
S3I_DN16_006 - MMBcloud-tree: Authenticated Index for Verifiable Cloud
Service Selection

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Cloud brokers have been recently introduced as an additional computational
layer to facilitate cloud selection and service management tasks for cloud consumers.
However, existing brokerage schemes on cloud service selection typically assume that
brokers are completely trusted, and do not provide any guarantee over the correctness
of the service recommendations. It is then possible for a compromised or dishonest
broker to easily take advantage of the limited capabilities of the clients and provide
incorrect or incomplete responses. To address this problem, we propose an innovative
Cloud Service Selection Verification (CSSV) scheme and index structures
(MMBcloud-tree) to enable cloud clients to detect misbehavior of the cloud brokers
during the service selection process. We demonstrate correctness and efficiency of our
approaches both theoretically and empirically.
S3I_DN16_007 - Identity-Based Proxy-Oriented Data Uploading and Remote
Data Integrity Checking in Public Cloud
More and more clients would like to store their data to public cloud servers
(PCSs) along with the rapid development of cloud computing. New security problems
have to be solved in order to help more clients process their data in public cloud.
When the client is restricted to access PCS, he will delegate its proxy to process his
data and upload them. On the other hand, remote data integrity checking is also an
important security problem in public cloud storage. It makes the clients check whether
their outsourced data are kept intact without downloading the whole data. From the
security problems, we propose a novel proxy-oriented data uploading and remote data
integrity checking model in identity-based public key cryptography: identity-based
proxy-oriented data uploading and remote data integrity checking in public cloud (ID-
PUIC). We give the formal definition, system model, and security model. Then, a
concrete ID-PUIC protocol is designed using the bilinear pairings. The proposed ID-
PUIC protocol is provably secure based on the hardness of computational Diffie-
Hellman problem. Our ID-PUIC protocol is also efficient and flexible. Based on the
original client's authorization, the proposed ID-PUIC protocol can realize private
remote data integrity checking, delegated remote data integrity checking, and public
remote data integrity checking.
S3I_DN16_008 - Fine-grained Two-factor Access Control for Web-based Cloud
Computing Services
In this paper, we introduce a new fine-grained two-factor authentication (2FA)
access control system for web-based cloud computing services. Specifically, in our
proposed 2FA access control system, an attribute-based access control mechanism is
implemented with the necessity of both a user secret key and a lightweight security
device. As a user cannot access the system if they do not hold both, the mechanism
can enhance the security of the system, especially in those scenarios where many

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users share the same computer for web-based cloud services. In addition, attribute-
based control in the system also enables the cloud server to restrict the access to those
users with the same set of attributes while preserving user privacy, i.e., the cloud
server only knows that the user fulfills the required predicate, but has no idea on the
exact identity of the user. Finally, we also carry out a simulation to demonstrate the
practicability of our proposed 2FA system.
S3I_DN16_009 - Cloud workflow scheduling with deadlines and time slot
availability
Allocating service capacities in cloud computing is based on the assumption
that they are unlimited and can be used at any time. However, available service
capacities change with workload and cannot satisfy users’ requests at any time from
the cloud provider’s perspective because cloud services can be shared by multiple
tasks. Cloud service providers provide available time slots for new user’s requests
based on available capacities. In this paper, we consider workflow scheduling with
deadline and time slot availability in cloud computing. An iterated heuristic
framework is presented for the problem under study which mainly consists of initial
solution construction, improvement, and perturbation. Three initial solution
construction strategies, two greedy- and fair-based improvement strategies and a
perturbation strategy are proposed. Different strategies in the three phases result in
several heuristics. Experimental results show that different initial solution and
improvement strategies have different effects on solution qualities.
S3I_DN16_010 - Publicly Verifiable Inner Product Evaluation over Outsourced
Data Streams under Multiple Keys
Uploading data streams to a resource-rich cloud server for inner product
evaluation, an essential building block in many popular stream applications (e.g.,
statistical monitoring), is appealing to many companies and individuals. On the other
hand, verifying the result of the remote computation plays a crucial role in addressing
the issue of trust. Since the outsourced data collection likely comes from multiple data
sources, it is desired for the system to be able to pinpoint the originator of errors by
allotting each data source a unique secret key, which requires the inner product
verification to be performed under any two parties’ different keys. However, the
present solutions either depend on a single key assumption or powerful yet
practicallyinefficient fully homomorphic cryptosystems. In this paper, we focus on the
more challenging multi-key scenario where data streams are uploaded by multiple
data sources with distinct keys. We first present a novel homomorphic verifiable tag
technique to publicly verify the outsourced inner product computation on the dynamic
data streams, and then extend it to support the verification of matrix product

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computation. We prove the security of our scheme in the random oracle model.
Moreover, the experimental result also shows the practicability of our design.
S3I_DN16_011 - Inverted Linear Quadtree: Efficient Top K Spatial Keyword
Search
With advances in geo-positioning technologies and geo-location services, there are a
rapidly growing amount of spatiotextual objects collected in many applications such
as location based services and social networks, in which an object is described by its
spatial location and a set of keywords (terms). Consequently, the study of spatial
keyword search which explores both location and textual description of the objects
has attracted great attention from the commercial organizations and research
communities. In the paper, we study two fundamental problems in the spatial keyword
queries: top k spatial keyword search (TOPK-SK), and batch top k spatial keyword
search (BTOPK-SK). Given a set of spatio-textual objects, a query location and a set
of query keywords, the TOPK-SK retrieves the closest k objects each of which
contains all keywords in the query. BTOPK-SK is the batch processing of sets of
TOPK-SK queries. Based on the inverted index and the linear quadtree, we propose a
novel index structure, called inverted linear quadtree (IL-Quadtree), which is carefully
designed to exploit both spatial and keyword based pruning techniques to effectively
reduce the search space. An efficient algorithm is then developed to tackle top k
spatial keyword search. To further enhance the filtering capability of the signature of
linear quadtree, we propose a partition based method. In addition, to deal with
BTOPK-SK, we design a new computing paradigm which partition the queries into
groups based on both spatial proximity and the textual relevance between queries. We
show that the IL-Quadtree technique can also efficiently support BTOPK-SK.
Comprehensive experiments on real and synthetic data clearly demonstrate the
efficiency of our methods.
S3I_DN16_012 - Securing SIFT: Privacy-preserving Outsourcing Computation
of Feature Extractions over Encrypted Image Data
Advances in cloud computing have greatly motivated data owners to outsource their
huge amount of personal multimedia data and/or computationally expensive tasks
onto the cloud by leveraging its abundant resources for cost saving and flexibility.
Despite the tremendous benefits, the outsourced multimedia data and its originated
applications may reveal the data owner’s private information, such as the personal
identity, locations or even financial profiles. This observation has recently aroused
new research interest on privacy-preserving computations over outsourced multimedia
data. In this paper, we propose an effective and practical privacy-preserving
computation outsourcing protocol for the prevailing scale-invariant feature transform
(SIFT) over massive encrypted image data. We first show that previous solutions to

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this problem have either efficiency/security or practicality issues, and none can well
preserve the important characteristics of the original SIFT in terms of distinctiveness
and robustness. We then present a new scheme design that achieves efficiency and
security requirements simultaneously with the preservation of its key characteristics,
by randomly splitting the original image data, designing two novel efficient protocols
for secure multiplication and comparison, and carefully distributing the feature
extraction computations onto two independent cloud servers. We both carefully
analyze and extensively evaluate the security and effectiveness of our design. The
results show that our solution is practically secure, outperforms the state-of-theart, and
performs comparably to the original SIFT in terms of various characteristics,
including rotation invariance, image scale invariance, robust matching across affine
distortion, addition of noise and change in 3D viewpoint and illumination.
S3I_DN16_013 - A Secure and Dynamic Multi-keyword Ranked Search Scheme
over Encrypted Cloud Data
Due to the increasing popularity of cloud computing, more and more data owners are
motivated to outsource their data to cloud servers for great convenience and reduced
cost in data management. However, sensitive data should be encrypted before
outsourcing for privacy requirements, which obsoletes data utilization like keyword-
based document retrieval. In this paper, we present a secure multi-keyword ranked
search scheme over encrypted cloud data, which simultaneously supports dynamic
update operations like deletion and insertion of documents. Specifically, the vector
space model and the widely-used TF IDF model are combined in the index
construction and query generation. We construct a special tree-based index structure
and propose a “Greedy Depth-first Search” algorithm to provide efficient multi-
keyword ranked search. The secure kNN algorithm is utilized to encrypt the index and
query vectors, and meanwhile ensure accurate relevance score calculation between
encrypted index and query vectors. In order to resist statistical attacks, phantom terms
are added to the index vector for blinding search results . Due to the use of our special
tree-based index structure, the proposed scheme can achieve sub-linear search time
and deal with the deletion and insertion of documents flexibly. Extensive experiments
are conducted to demonstrate the efficiency of the proposed scheme.
S3I_DN16_014 - Protecting Your Right: Verifiable Attribute-based Keyword
Search with Fine-grained Owner-enforced Search Authorization in the Cloud
Search over encrypted data is a critically important enabling technique in cloud
computing, where encryption-beforeoutsourcing is a fundamental solution to
protecting user data privacy in the untrusted cloud server environment. Many secure
search schemes have been focusing on the single-contributor scenario, where the
outsourced dataset or the secure searchable index of the dataset are encrypted and

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managed by a single owner, typically based on symmetric cryptography. In this paper,
we focus on a different yet more challenging scenario where the outsourced dataset
can be contributed from multiple owners and are searchable by multiple users, i.e.
multi-user multi-contributor case. Inspired by attribute-based encryption (ABE), we
present the first attribute-based keyword search scheme with efficient user revocation
(ABKS-UR) that enables scalable fine-grained (i.e. file-level) search authorization.
Our scheme allows multiple owners to encrypt and outsource their data to the cloud
server independently. Users can generate their own search capabilities without relying
on an always online trusted authority. Fine-grained search authorization is also
implemented by the owner-enforced access policy on the index of each file. Further,
by incorporating proxy re-encryption and lazy re-encryption techniques, we are able
to delegate heavy system update workload during user revocation to the resourceful
semi-trusted cloud server. We formalize the security definition and prove the
proposed ABKS-UR scheme selectively secure against chosen-keyword attack. To
build confidence of data user in the proposed secure search system, we also design a
search result verification scheme. Finally, performance evaluation shows that the
efficiency of our scheme.
S3I_DN16_015 - Secure Data Analytics for Cloud-Integrated Internet of Things
Applications
Cloud-integrated Internet of Things (IoT) is emerging as the next-generation
service platform that enables smart functionality worldwide. IoT applications such as
smart grid and power systems, e-health, and body monitoring applications along with
large-scale environmental and industrial monitoring are increasingly generating large
amounts of data that can conveniently be analyzed through cloud service provisioning.
However, the nature of these applications mandates the use of secure and privacy-
preserving implementation of services that ensures the integrity of data without any
unwarranted exposure. This article explores the unique challenges and issues within
this context of enabling secure cloud-based data analytics for the IoT. Three main
applications are discussed in detail, with solutions outlined based on the use of fully
homomorphic encryption systems to achieve data security and privacy over cloud-
based analytical phases. The limitations of existing technologies are discussed and
models proposed with regard to achieving high efficiency and accuracy in the
provisioning of analytic services for encrypted data over a cloud platform.
S3I_DN16_016 - A Low-Cost Low-Power Ring Oscillator-based Truly Random
Number Generator for Encryption on Smart Cards
W. Bit rate of the TRNG after post processing is 100 kb/s. The proposed TRNG has
been made into an IP and successfully applied in an SD card for encryption
application. The proposed TRNG has passed the NIST tests and Diehard tests.m

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standard CMOS process, the proposed TRNG has an area as low as 0.005 mm2.
Powered by a single 1.8 V supply voltage, the TRNG has a power consumption of 40
 The design of a low-cost low-power ring oscillator-based truly random number
generator (TRNG) macro-cell, suitable to be integrated in smart cards, is presented.
The oscillator sampling technique is exploited and a tetrahedral oscillator with large
jitter has been employed to realize the TRNG. Techniques to improve the statistical
quality of the ring oscillator-based TRNGs’ bit sequences have been presented and
verified by simulation and measurement. Post digital processor is added to further
enhance the randomness of the output bits. Fabricated in HHNEC 0.13
S3I_DN16_017 - Encrypted Data Management with Deduplication in Cloud
Computing
Cloud computing offers a new way to deliver services by rearranging resources over
the Internet and providing them to users on demand. It plays an important role in
supporting data storage, processing, and management in the Internet of Things (IoT).
Various cloud service providers (CSPs) offer huge volumes of storage to maintain and
manage IoT data, which can include videos, photos, and personal health records.
These CSPs provide desirable service properties, such as scalability, elasticity, fault
tolerance, and pay per use. Thus, cloud computing has become a promising service
paradigm to support IoT applications and IoT system deployment. To ensure data
privacy, existing research proposes to outsource only encrypted data to CSPs.
However, the same or different users could save duplicated data under different
encryption schemes at the cloud. Although cloud storage space is huge, this kind of
duplication wastes networking resources, consumes excess power, and complicates
data management. Thus, saving storage is becoming a crucial task for CSPs.
Deduplication can achieve high space and cost savings, reducing up to 90 to 95
percent of storage needs for backup applications (http://opendedup.org) and up to 68
percent in standard file systems.1 Obviously, the savings, which can be passed back
directly or indirectly to cloud users, are significant to the economics of cloud
business. At the same time, data owners want CSPs to protect their personal data from
unauthorized access. CSPs should therefore perform access control based on the data
owner’s expectations. In addition, data owners want to control not only data access
but also its storage and usage. From a flexibility viewpoint, data deduplication should
cooperate with data access control mechanisms. That is, the same data, although in an
encrypted form, is only saved once at the cloud but can be accessed by different users
based on the data owners’ policies.
S3I_DN16_018 - Dual-Server Public-Key Encryption with Keyword Search for
Secure Cloud Storage

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Searchable encryption is of increasing interest for protecting the data privacy in
secure searchable cloud storage. In this work, we investigate the security of a well-
known cryptographic primitive, namely Public Key Encryption with Keyword Search
(PEKS) which is very useful in many applications of cloud storage. Unfortunately, it
has been shown that the traditional PEKS framework suffers from an inherent
insecurity called inside Keyword Guessing Attack (KGA) launched by the malicious
server. To address this security vulnerability, we propose a new PEKS framework
named Dual-Server Public Key Encryption with Keyword Search (DS-PEKS). As
another main contribution, we define a new variant of the Smooth Projective Hash
Functions (SPHFs) referred to as linear and homomorphic SPHF (LH-SPHF). We
then show a generic construction of secure DS-PEKS from LH-SPHF. To illustrate
the feasibility of our new framework, we provide an efficient instantiation of the
general framework from a DDH-based LH-SPHF and show that it can achieve the
strong security against inside KGA.
S3I_DN16_019 - A recommendation system based on hierarchical clustering of
an article-level citation network
The scholarly literature is expanding at a rate that necessitates intelligent algorithms
for search and navigation.For the most part, the problem of delivering scholarly
articles has been solved. If one knows the title of an article, locating it requires little
effort and, paywalls permitting, acquiring a digital copy has become trivial.However,
the navigational aspect of scientific search – finding relevant, influential articles that
one does not know exist – is in its early development. In this paper, we introduce
Eigenfactor Recommends – a citation-based method for improving scholarly
navigation. The algorithm uses the hierarchical structure of scientific knowledge,
making possible multiple scales of relevance for different users. We implement the
method and generate more than 300 million recommendations from more than 35
million articles from various bibliographic databases including the AMiner dataset.
We find little overlap with co-citation, another well-known citation recommender,
which indicates potential complementarity. In an online A-B comparison using SSRN,
we find that our approach performs as well as co-citation, but this new approach
offers much larger recommendation coverage. We make the code and
recommendations freely available at babel.eigenfactor.org and provide an API for
others to use for implementing and comparing the recommendations on their own
platforms.
S3I_DN16_020 - Efficient Group Key Transfer Protocol for WSNs
Special designs are needed for cryptographic schemes in wireless sensor
networks (WSNs). This is because sensor nodes are limited in memory storage and
computational power. The existing group key transfer protocols for WSNs using

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classical secret sharing require that a t-degree interpolating polynomial be computed
in order to encrypt and decrypt the secret group key. This approach is too
computationally intensive. In this paper, we propose a new group key transfer
protocol using a linear secret sharing scheme (LSSS) and factoring assumption. The
proposed protocol can resist potential attacks and also significantly reduce the
computation complexity of the system while maintaining low communication cost.
Such a scheme is desirable for secure group communications in wireless sensor
networks (WSNs), where portable devices or sensors need to reduce their computation
as much as possible due to battery power limitations.
S3I_SE16_001 - A Tool-Supported Methodology for Validation and Refinement
of Early-Stage Domain Models
Model-driven engineering (MDE) promotes automated model transformations
along the entire development process. Guaranteeing the quality of early models is
essential for a successful application of MDE techniques and related tool-supported
model refinements. Do these models properly reflect the requirements elicited from
the owners of the problem domain? Ultimately, this question needs to be asked to the
domain experts. The problem is that a gap exists between the respective backgrounds
of modeling experts and domain experts. MDE developers cannot show a model to the
domain experts and simply ask them whether it is correct with respect to the
requirements they had in mind. To facilitate their interaction and make such validation
more systematic, we propose a methodology and a tool that derive a set of
customizable questionnaires expressed in natural language from each model to be
validated. Unexpected answers by domain experts help to identify those portions of
the models requiring deeper attention. We illustrate the methodology and the current
status of the developed tool MOTHIA, which can handle UML Use Case, Class, and
Activity diagrams. We assess MOTHIA effectiveness in reducing the gap between
domain and modeling experts, and in detecting modeling faults on the European
Project CHOReOS.
S3I_SE16_002 - Trust Agent-Based Behavior Induction in Social Networks

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The essence of social networks is that they can influence people's public
opinions and group behaviors form quickly. Negative group behavior influences
societal stability significantly, but existing behavior-induction approaches are too
simple and inefficient. To automatically and efficiently induct behavior in social
networks, this article introduces trust agents and designs their features according to
group behavior features. In addition, a dynamics control mechanism can be generated
to coordinate participant behaviors in social networks to avoid a specific restricted
negative group behavior.
S3I_DE16_001 - Incremental and Decremental Max-flow for Online Semi-
supervised Learning
In classification, if a small number of instances is added or removed,
incremental and decremental techniques can be applied to quickly update the model.
However, the design of incremental and decremental algorithms involves many
considerations. In this paper, we focus on linear classifiers including logistic
regression and linear SVM because of their simplicity over kernel or other methods.
By applying a warm start strategy, we investigate issues such as using primal or dual
formulation, choosing optimization methods, and creating practical implementations.
Through theoretical analysis and practical experiments, we conclude that a warm start
setting on a high-order optimization method for primal formulations is more suitable
than others for incremental and decremental learning of linear classification.
S3I_DE16_002 - Personalized Influential Topic Search via Social Network
Summarization
Social networks are a vital mechanism to disseminate information to friends and
colleagues. In this work, we investigate an important problem - the personalized
influential topic search, or PIT-Search in a social network: Given a keyword query q
issued by a user u in a social network, a PIT-Search is to find the top-k q-related
topics that are most influential for the query user u. The influence of a topic to a query
user depends on the social connection between the query user and the social users
containing the topic in the social network. To measure the topics’ influence at the
similar granularity scale, we need to extract the social summarization of the social
network regarding topics. To make effective topic-aware social summarization, we
propose two random-walk based approaches: random clustering and an L-length
random walk. Based on the proposed approaches, we can find a small set of
representative users with assigned influential scores to simulate the influence of the
large number of topic users in the social network with regards to the topic. The

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selected representative users are denoted as the social summarization of topic-aware
influence spread over the social network. And then, we verify the usefulness of the
social summarization by applying it to the problem of personalized influential topic
search. Finally, we evaluate the performance of our algorithms using real-world
datasets, and show the approach is efficient and effective in practice.
S3I_DE16_003 - Survey on Aspect-Level Sentiment Analysis
The field of sentiment analysis, in which sentiment is gathered, analyzed, and
aggregated from text, has seen a lot of attention in the last few years. The
corresponding growth of the field has resulted in the emergence of various subareas,
each addressing a different level of analysis or research question. This survey focuses
on aspect-level sentiment analysis, where the goal is to find and aggregate sentiment
on entities mentioned within documents or aspects of them. An in-depth overview of
the current state-of-the-art is given, showing the tremendous progress that has already
been made in finding both the target, which can be an entity as such, or some aspect
of it, and the corresponding sentiment. Aspect-level sentiment analysis yields very
fine-grained sentiment information which can be useful for applications in various
domains. Current solutions are categorized based on whether they provide a method
for aspect detection, sentiment analysis, or both. Furthermore, a breakdown based on
the type of algorithm used is provided. For each discussed study, the reported
performance is included. To facilitate the quantitative evaluation of the various
proposed methods, a call is made for the standardization of the evaluation
methodology that includes the use of shared data sets. Semanticallyrich concept-
centric aspect-level sentiment analysis is discussed and identified as one of the most
promising future research direction.
S3I_DE16_004 - Multilabel Classification via Co-evolutionary Multilabel
Hypernetwork
Multilabel classification is prevalent in many real-world applications where
data instances may be associated with multiple labels simultaneously. In multilabel
classification, exploiting label correlations is an essential but nontrivial task. Most of
the existing multilabel learning algorithms are either ineffective or computational
demanding and less scalable in exploiting label correlations. In this paper, we propose
a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label
correlations in an effective and efficient way. To this end, we firstly convert the
traditional hypernetwork into a multilabel hypernetwork (MLHN) where label
correlations are explicitly represented. We then propose a co-evolutionary learning
algorithm to learn an integrated classification model for all labels. The proposed Co-

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MLHN exploits arbitrary order label correlations and has linear computational
complexity with respect to the number of labels. Empirical studies on a broad range of
multilabel data sets demonstrate that Co-MLHN achieves competitive results against
state-of-the-art multilabel learning algorithms, in terms of both classification
performance and scalability with respect to the number of labels.
S3I_DE16_005 - Answering Pattern Queries Using Views
Answering queries using views has proven effective for querying relational and
semistructured data. This paper investigates this issue for graph pattern queries based
on graph simulation. We propose a notion of pattern containment to characterize
graph pattern matching using graph pattern views. We show that a pattern query can
be answered using a set of views if and only if it is contained in the views. Based on
this characterization, we develop efficient algorithms to answer graph pattern queries.
We also study problems for determining (minimal, minimum) containment of pattern
queries. We establish their complexity (from cubic-time to NPcomplete) and provide
efficient checking algorithms (approximation when the problem is intractable). In
addition, when a pattern query is not contained in the views, we study maximally
contained rewriting to find approximate answers; we show that it is in cubic-time to
compute such rewriting, and present a rewriting algorithm. We experimentally verify
that these methods are able to efficiently answer pattern queries on large real-world
graphs.
S3I_DE16_006 - Similarity Measure Selection for Clustering Time Series
Databases
In the past few years, clustering has become a popular task associated with time
series. The choice of a suitable distance measure is crucial to the clustering process
and, given the vast number of distance measures for time series available in the
literature and their diverse characteristics, this selection is not straightforward. With
the objective of simplifying this task, we propose a multi-label classification
framework that provides the means to automatically select the most suitable distance
measures for clustering a time series database. This classifier is based on a novel
collection of characteristics that describe the main features of the time series databases
and provide the predictive information necessary to discriminate between a set of
distance measures. In order to test the validity of this classifier, we conduct a
complete set of experiments using both synthetic and real time series databases and a
set of five common distance measures. The positive results obtained by the designed
classification framework for various performance measures indicate that the proposed
methodology is useful to simplify the process of distance selection in time series
clustering tasks.

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S3I_DE16_007 - Incremental Consolidation of Data-Intensive Multi-Flows
Business intelligence (BI) systems depend on efficient integration of disparate
and often heterogeneous data. The integration of data is governed by data-intensive
flows and is driven by a set of information requirements. Designing such flows is in
general a complex process, which due to the complexity of business environments is
hard to be done manually. In this paper, we deal with the challenge of efficient design
and maintenance of data-intensive flows and propose an incremental approach,
namely CoAl , for semi-automatically consolidating data-intensive flows satisfying a
given set of information requirements. CoAl works at the logical level and
consolidates data flows from either high-level information requirements or platform-
specific programs. As CoAl integrates a new data flow, it opts for maximal reuse of
existing flows and applies a customizable cost model tuned for minimizing the overall
cost of a unified solution. We demonstrate the efficiency and effectiveness of our
approach through an experimental evaluation using our implemented prototype.
S3I_WSN16_001 - Analysis of PKF: A Communication Cost Reduction Scheme
for Wireless Sensor Networks
Energy efficiency is a primary concern for wireless sensor networks (WSNs).
One of its most energy-intensive processes is the radio communication. This work
uses a predictor combined with a Kalman filter (KF) to reduce the communication
energy cost for cluster-based WSNs. The technique, called PKF, is suitable for typical
WSN applications with adjustable data quality and tens of picojoule computation cost.
However, it is challenging to precisely quantify its underlying process from a

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mathematical point of view. Through an in-depth mathematical analysis, we formulate
the tradeoff between energy efficiency and reconstruction quality of PKF. One of our
prominent results for that is the explicit expression for the covariance of the doubly
truncated multivariate normal distribution; it improves the previous methods and has
generality. The validity and accuracy of the analysis are verified with both artificial
and real signals. The simulation results, using real temperature values, demonstrate
the efficiency of PKF: without additional data degradation, it reduces the
communication cost by more than 88%. Compared to previous works based on KF,
PKF requires less computational effort while improving the reconstruction quality;
compared with the techniques without KF, the advantages of PKF are even more
significant. It reduces the transmission rate of them by at least 29%. Besides, it can be
integrated into network level techniques to further extend the whole network lifetime.
S3I_WSN16_002 - Online Packet Dispatching for Delay Optimal Concurrent
Transmissions in Heterogeneous Multi-RAT Networks
In this paper, we consider the problem of concurrent transmissions in a wireless
network consisting of multiple radio access technologies (multi-RATs). That is, a
single flow of packets is dispatched over multiple RATs so that the complementary
advantages of different RATs can be exploited. One of the challenging issues arising
in concurrent transmissions is the packet outof- order problem due to diverse wireless
channel states and scheduling policies of different RATs, leading to substantial
performance degradation to delay sensitive applications. To address this problem, we
firstly propose a state-independent packet dispatching (SIPD) policy, which attempts
to find the traffic dispatching ratios over multiple RATs to minimize the maximum
average delay across different RATs in the long run. We further propose a state-
dependent packet dispatching (SDPD) policy, which achieves fine-grained packet
dispatching in the short-term. We use the value function as a measure of the
admittance cost for packet dispatching given the current queueing states, and
formulate the SDPD problem as a convex programming problem. We derive the
close-form solutions for both problems for the special case of two RATs, and adopt
the dual decomposition technique as the solution for the general cases. Simulation
results are presented to compare the performance of the proposed schemes with
existing solutions.
S3I_WSN16_003 - Toward Optimal Adaptive Wireless Communications in
Unknown Environments
Designing efficient channel access schemes for wireless communications
without any prior knowledge about the nature of environments has been a very
challenging issue, in which the channel state distribution of all spectrum resources

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could be entirely or partially stochastic or adversarial at different times and locations.
In this paper, we propose an online learning algorithm for adaptive channel access of
wireless communications in unknown environments based on the theory of
multiarmed bandits (MAB) problems. By automatically tuning two control
parameters, i.e., learning rate and exploration probability, our algorithms could find
the optimal channel access strategies and achieve the almost optimal learning
performance over time in different scenarios. The quantitative performance studies
indicate the superior throughput gain when compared with previous solutions and the
flexibility of our algorithm in practice, which is resilient to both oblivious and
adaptive jamming attacks with different intelligence and attacking strength that ranges
from no-attack to the full-attack of all spectrum resources. We conduct extensive
simulations to validate our theoretical analysis.
S3I_WSN16_004 - Adaptive Pilot Clustering in Heterogeneous Massive MIMO
Networks
We consider the uplink of a cellular massive MIMO network. Acquiring
channel state information at the base stations (BSs) requires uplink pilot signaling.
Since the number of orthogonal pilot sequences is limited by the channel coherence,
pilot reuse across cells is necessary to achieve high spectral efficiency. However,
finding efficient pilot reuse patterns is nontrivial especially in practical asymmetric
BS deployments. We approach this problem using coalitional game theory. Each BS
has a few unique pilots and can form coalitions with other BSs to gain access to more
pilots. The BSs in a coalition thus benefit from serving more users in their cells, at the
expense of higher pilot contamination and interference. Given that a cell’s average
spectral efficiency depends on the overall pilot reuse pattern, the suitable coalitional
game model is in partition form. We develop a low-complexity distributed coalition
formation based on individual stability. By incorporating a base station
intercommunication budget constraint, we are able to control the overhead in message
exchange between the base stations and ensure the algorithm’s convergence to a
solution of the game called individually stable coalition structure. Simulation results
reveal fast algorithmic convergence and substantial performance gains over the
baseline schemes with no pilot reuse, full pilot reuse, or random pilot reuse pattern.
S3I_WSN16_005 - Data Aggregation and Principal Component Analysis in
WSNs
Data aggregation plays an important role inWireless Sensor Networks (WSNs)
as far as it reduces power consumption and boosts the scalability of the network,
specially in topologies that are prone to bottlenecks (e.g. cluster-trees). Existing works
in the literature use clustering approaches, Principal Component Analysis (PCA)
and/or Compressed Sensing (CS) strategies. Our contribution is aligned with PCA and

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explores whether a projection basis that is not the eigenvectors basis may be valid to
sustain a Normalized Mean Squared Error (NMSE) threshold in signal reconstruction
and reduce the energy consumption. We derivate first the NSME achieved with the
new basis and elaborate then on the Jacobi eigenvalue decomposition ideas to propose
a new subspace-based data aggregation method. The proposed solution reduces
transmissions among the sink and one or more Data Aggregation Nodes (DANs) in
the network. In our simulations we consider without loss of generality a single cluster
network and results show that the new technique succeeds in satisfying the NMSE
requirement and gets close in terms of energy consumption to the best possible
solution employing subspace representations. Additionally the proposed method
alleviates the computational load with respect to an eigenvector-based strategy (by a
factor of six in our simulations).
S3I_WSN16_006 - A New cost-effective approach for Battlefield Surveillance in
Wireless Sensor Networks
Assuring security (in the form of attacking mode as well as in safeguard mode)
and at the same time keeping strong eye on the opposition's status (position, quantity,
availability) is the key responsibility of a commander in the battlefield. Battlefield
surveillance is one of the strong applications of Wireless Sensor Networks (WSNs). A
commander is not only liable to his above responsibilities, but also to manage his
duties in an efficient way. For this reason, ensuring maximum destruction with
minimum resources is a major concern of a commander in the battlefield. This paper
focuses on the maximum destruction problem in military affairs. In the work of
Jaigirdar and Islam (2012), the authors proposed two novel algorithms (Maximum
degree analysis and Maximum clique analysis) that ensure the efficiency and cost-
effectiveness of the above problem. A comparative study explaining the number of
resources required for commencing required level of destruction made to the
opponents has been provided in the paper. In this paper the authors have come
forward with another algorithm for the same problem. With the simulation studies and
comparative analysis of the same example set the authors in this paper demonstrate
the effectiveness (in both the quality and quantity) of the new method to be best
among the three.
S3I_NS16_001 - Contributory Broadcast Encryption with Efficient Encryption
and Short Ciphertexts
Broadcast encryption (BE) schemes allow a sender to securely broadcast to any
subset of members but require a trusted party to distribute decryption keys. Group key
agreement (GKA) protocols enable a group of members to negotiate a common
encryption key via open networks so that only the group members can decrypt the
ciphertexts encrypted under the shared encryption key, but a sender cannot exclude

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any particular member from decrypting the ciphertexts. In this paper, we bridge these
two notions with a hybrid primitive referred to as contributory broadcast encryption
(ConBE). In this new primitive, a group of members negotiate a common public
encryption key while each member holds a decryption key. A sender seeing the public
group encryption key can limit the decryption to a subset of members of his choice.
Following this model, we propose a ConBE scheme with short ciphertexts. The
scheme is proven to be fully collusion-resistant under the decision n-Bilinear Diffie-
Hellman Exponentiation (BDHE) assumption in the standard model. Of independent
interest, we present a new BE scheme that is aggregatable. The aggregatability
property is shown to be useful to construct advanced protocols.
S3I_NS16_002 - Building an intrusion detection system using a filter-based
feature selection algorithm
Redundant and irrelevant features in data have caused a long-term problem in
network traffic classification. These features not only slow down the process of
classification but also prevent a classifier from making accurate decisions, especially
when coping with big data. In this paper, we propose a mutual information based
algorithm that analytically selects the optimal feature for classification. This mutual
information based feature selection algorithm can handle linearly and nonlinearly
dependent data features. Its effectiveness is evaluated in the cases of network
intrusion detection. An Intrusion Detection System (IDS), named Least Square
Support Vector Machine based IDS (LSSVM-IDS), is built using the features selected
by our proposed feature selection algorithm. The performance of LSSVM-IDS is
evaluated using three intrusion detection evaluation datasets, namely KDD Cup 99,
NSL-KDD and Kyoto 2006+ dataset. The evaluation results show that our feature
selection algorithm contributes more critical features for LSSVM-IDS to achieve
better accuracy and lower computational cost compared with the state-of-the-art
methods.
S3I_SEC_001: Cloud Workflow Scheduling with Deadlines and Time Slot
Availability
Allocating service capacities in cloud computing is based on the assumption that
they are unlimited and can be used at any time. However, available service capacities
change with workload and cannot satisfy users’ requests at any time from the cloud
provider’s perspective because cloud services can be shared by multiple tasks. Cloud
service providers provide available time slots for new user’s requests based on
available capacities. In this paper, we consider workflow scheduling with deadline
and time slot availability in cloud computing. An iterated heuristic framework is

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presented for the problem under study which mainly consists of initial solution
construction, improvement, and perturbation. Three initial solution construction
strategies, two greedy- and fair-based improvement strategies and a perturbation
strategy are proposed. Different strategies in the three phases result in several
heuristics. Experimental results show that different initial solution and improvement
strategies have different effects on solution qualities.
S3I_SEC_002: Publicly Verifiable Inner Product Evaluation over Outsourced
Data Streams under Multiple Keys
Uploading data streams to a resource-rich cloud server for inner product
evaluation, an essential building block in many popular stream applications (e.g.,
statistical monitoring), is appealing to many companies and individuals. On the other
hand, verifying the result of the remote computation plays a crucial role in addressing
the issue of trust. Since the outsourced data collection likely comes from multiple data
sources, it is desired for the system to be able to pinpoint the originator of errors by
allotting each data source a unique secret key, which requires the inner product
verification to be performed under any two parties’ different keys. However, the
present solutions either depend on a single key assumption or powerful yet practically
inefficient fully homomorphic cryptosystems. In this paper, we focus on the more
challenging multi-key scenario where data streams are uploaded by multiple data
sources with distinct keys. We first present a novel homomorphic verifiable tag
technique to publicly verify the outsourced inner product computation on the dynamic
data streams, and then extend it to support the verification of matrix product
computation. We prove the security of our scheme in the random oracle model.
Moreover, the experimental result also shows the practicability of our design.
S3I_DPS16_001 - Cost Minimization for Rule Caching in Software Defined
Networking
Software-defined networking (SDN) is an emerging network paradigm that
simplifies network management by decoupling the control plane and data plane, such
that switches become simple data forwarding devices and network management is
controlled by logically centralized servers. In SDN-enabled networks, network flow is
managed by a set of associated rules that are maintained by switches in their local
Ternary Content Addressable Memories (TCAMs) which support high-speed parallel
lookup on wildcard patterns. Since TCAM is an expensive hardware and extremely
power-hungry, each switch has only limited TCAM space and it is inefficient and
even infeasible to maintain all rules at local switches. On the other hand, if we
eliminate TCAM occupation by forwarding all packets to the centralized controller for

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processing, it results in a long delay and heavy processing burden on the controller. In
this paper, we strive for the fine balance between rule caching and remote packet
processing by formulating a minimum weighted flow provisioning ( MWFP) problem
with an objective of minimizing the total cost of TCAM occupation and remote packet
processing. We propose an efficient offline algorithm if the network traffic is given,
otherwise, we propose two online algorithms with guaranteed competitive ratios.
Finally, we conduct extensive experiments by simulations using real network traffic
traces. The simulation results demonstrate that our proposed algorithms can
significantly reduce the total cost of remote controller processing and TCAM
occupation, and the solutions obtained are nearly optimal.
S3I_DPS16_002 - On Binary Decomposition based Privacy-preserving
Aggregation Schemes in Real-time Monitoring Systems
In real-time monitoring systems, fine-grained measurements would pose great
privacy threats to the participants as real-time measurements could disclose accurate
people-centric activities. Differential privacy has been proposed to formalize and
guide the design of privacy-preserving schemes. Nonetheless, due to the correlations
and high fluctuations in time-series data, it is hard to achieve an effective privacy and
utility tradeoff by differential privacy mechanisms. To address this issue, in this
paper, we first proposed novel multi-dimensional decomposition based schemes to
compress the noise and enhance the utility in differential privacy. The key idea is to
decompose the measurements into multi-dimensional records and to achieve
differential privacy in bounded dimensions so that the error caused by unbounded
measurements can be significantly reduced. We then extended our developed scheme
and developed a binary decomposition scheme for privacy-preserving time-series
aggregation in real-time monitoring systems. Through a combination of extensive
theoretical analysis and experiments, our data shows that our proposed schemes can
effectively improve usability while achieving the same level of differential privacy
than existing schemes.
S3I_DPS16_003 - An OpenMP Extension that Supports Thread-Level
Speculation

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OpenMP directives are the de-facto standard for shared-memory parallel
programming. However, OpenMP does not guarantee the correctness of the parallel
execution of a given loop if runtime data dependences arise. Consequently, many
highly-parallel regions cannot be safely parallelized with OpenMP due to the
possibility of a dependence violation. In this paper, we propose to augment OpenMP
capabilities, by adding thread-level speculation (TLS) support. Our contribution is
threefold. First, we have defined a new speculative clause for variables inside parallel
loops. This clause ensures that all accesses to these variables will be carried out
according to sequential semantics. Second, we have created a new, software-based
TLS runtime library to ensure correctness in the parallel execution of OpenMP loops
that include speculative variables. Third, we have developed a new GCC plugin,
which seamlessly translates our OpenMP speculative clause into calls to our TLS
runtime engine. The result is the ATLaS C Compiler framework, which takes
advantage of TLS techniques to expand OpenMP functionalities, and guarantees the
sequential semantics of any parallelized loop.
S3I_DPS16_004 - Real-Time Semantic Search Using Approximate Methodology
for Large-Scale Storage Systems
The challenges of handling the explosive growth in data volume and
complexity cause the increasing needs for semantic queries. The semantic queries can
be interpreted as the correlation-aware retrieval, while containing approximate results.
Existing cloud storage systems mainly fail to offer an adequate capability for the
semantic queries. Since the true value or worth of data heavily depends on how
efficiently semantic search can be carried out on the data in (near-) real-time, large
fractions of data end up with their values being lost or significantly reduced due to the
data staleness. To address this problem, we propose a near-real-time and cost-
effective semantic queries based methodology, called FAST. The idea behind FAST is
to explore and exploit the semantic correlation within and among datasets via
correlation-aware hashing and manageable flat-structured addressing to significantly
reduce the processing latency, while incurring acceptably small loss of data-search
accuracy. The near-real-time property of FAST enables rapid identification of
correlated files and the significant narrowing of the scope of data to be processed.
FAST supports several types of data analytics, which can be implemented in existing
searchable storage systems. We conduct a real-world use case in which children
reported missing in an extremely crowded environment (e.g., a highly popular scenic
spot on a peak tourist day) are identified in a timely fashion by analyzing 60 million
images using FAST. FAST is further improved by using semantic-aware namespace
to provide dynamic and adaptive namespace management for ultra-large storage
systems. Extensive experimental results demonstrate the efficiency and efficacy of
FAST in the performance improvements.

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S3I_MC16_001: Service Usage Classification with Encrypted Internet Traffic in
Mobile Messaging Apps
The rapid adoption of mobile messaging Apps has enabled us to collect
massive amount of encrypted Internet traffic of mobile messaging. The classification
of this traffic into different types of in-App service usages can help for intelligent
network management, such as managing network bandwidth budget and providing
quality of services. Traditional approaches for classification of Internet traffic rely on
packet inspection, such as parsing HTTP headers. However, messaging Apps are
increasingly using secure protocols, such as HTTPS and SSL, to transmit data. This
imposes significant challenges on the performances of service usage classification by
packet inspection. To this end, in this paper, we investigate how to exploit encrypted
Internet traffic for classifying in-App usages. Specifically, we develop a system,
named CUMMA, for classifying service usages of mobile messaging Apps by jointly
modeling user behavioral patterns, network traffic characteristics and temporal
dependencies. Along this line, we first segment Internet traffic from traffic-flows into
sessions with a number of dialogs in a hierarchical way. Also, we extract the
discriminative features of traffic data from two perspectives: (i) packet length and (ii)
time delay. Next, we learn a service usage predictor to classify these segmented
dialogs into single-type usages or outliers. In addition, we design a clustering Hidden
Markov Model (HMM) based method to detect mixed dialogs from outliers and
decompose mixed dialogs into sub-dialogs of single-type usage. Indeed, CUMMA
enables mobile analysts to identify service usages and analyze end-user in-App
behaviors even for encrypted Internet traffic. Finally, the extensive experiments on
real-world messaging data demonstrate the effectiveness and efficiency of the
proposed method for service usage classification.

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S3I_CC16_001: Dynamic and Public Auditing with Fair Arbitration for Cloud Data
Cloud users no longer physically possess their data, so how to ensure the integrity of their
outsourced data becomes a challenging task. Recently proposed schemes such as “provable data
possession” and “proofs of retrievability” are designed to address this problem, but they are
designed to audit static archive data and therefore lack of data dynamics support. Moreover,
threat models in these schemes usually assume an honest data owner and focus on detecting a
dishonest cloud service provider despite the fact that clients may also misbehave. This paper
proposes a public auditing scheme with data dynamics support and fairness arbitration of
potential disputes. In particular, we design an index switcher to eliminate the limitation of index
usage in tag computation in current schemes and achieve efficient handling of data dynamics. To
address the fairness problem so that no party can misbehave without being detected, we further
extend existing threat models and adopt signature exchange idea to design fair arbitration
protocols, so that any possible dispute can be fairly settled. The security analysis shows our
scheme is provably secure, and the performance evaluation demonstrates the overhead of data
dynamics and dispute arbitration are reasonable.
S3I_CC16_002: Enabling Cloud Storage Auditing with Verifiable Outsourcing of Key
Updates
Key-exposure resistance has always been an important issue for in-depth cyber defence in
many security applications. Recently, how to deal with the key exposure problem in the settings
of cloud storage auditing has been proposed and studied. To address the challenge, existing
solutions all require the client to update his secret keys in every time period, which may
inevitably bring in new local burdens to the client, especially those with limited computation
resources, such as mobile phones. In this paper, we focus on how to make the key updates as
transparent as possible for the client and propose a new paradigm called cloud storage auditing
with verifiable outsourcing of key updates. In this paradigm, key updates can be safely
outsourced to some authorized party, and thus the key-update burden on the client will be kept
minimal. In particular, we leverage the third party auditor (TPA) in many existing public
auditing designs, let it play the role of authorized party in our case, and make it in charge of both
the storage auditing and the secure key updates for key-exposure resistance. In our design, TPA
only needs to hold an encrypted version of the client's secret key while doing all these
burdensome tasks on behalf of the client. The client only needs to download the encrypted secret
key from the TPA when uploading new files to cloud. Besides, our design also equips the client
with capability to further verify the validity of the encrypted secret keys provided by the TPA.
All these salient features are carefully designed to make the whole auditing procedure with key
exposure resistance as transparent as possible for the client. We formalize the definition and the
security model of this paradigm. The security proof and the performance simulation show that
our detailed design instantiations are secure and efficient.
S3I_CC16_003: Providing User Security Guarantees in Public Infrastructure Clouds
The infrastructure cloud (IaaS) service model offers improved resource flexibility and
availability, where tenants – insulated from the minutiae of hardware maintenance – rent
computing resources to deploy and operate complex systems. Large-scale services running on
IaaS platforms demonstrate the viability of this model; nevertheless, many organizations
operating on sensitive data avoid migrating operations to IaaS platforms due to security

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concerns. In this paper, we describe a framework for data and operation security in IaaS,
consisting of protocols for a trusted launch of virtual machines and domain-based storage
protection. We continue with an extensive theoretical analysis with proofs about protocol
resistance against attacks in the defined threat model. The protocols allow trust to be established
by remotely attesting host platform configuration prior to launching guest virtual machines and
ensure confidentiality of data in remote storage, with encryption keys maintained outside of the
IaaS domain. Presented experimental results demonstrate the validity and efficiency of the
proposed protocols. The framework prototype was implemented on a test bed operating a public
electronic health record system, showing that the proposed protocols can be integrated into
existing cloud environments.
S3I_CC16_004: Attribute-Based Data Sharing Scheme Revisited in Cloud Computing
Cipher-text-policy attribute-based encryption (CP-ABE) is a very promising encryption
technique for secure data sharing in the context of cloud computing. Data owner is allowed to
fully control the access policy associated with his data which to be shared. However, CP-ABE is
limited to a potential security risk that is known as key escrow problem, whereby the secret keys
of users have to be issued by a trusted key authority. Besides, most of the existing CP-ABE
schemes cannot support attribute with arbitrary state. In this paper, we revisit attribute-based data
sharing scheme in order to solve the key escrow issue but also improve the expressiveness of
attribute, so that the resulting scheme is more friendly to cloud computing applications. We
propose an improved two-party key issuing protocol that can guarantee that neither key authority
nor cloud service provider can compromise the whole secret key of a user individually.
Moreover, we introduce the concept of attribute with weight, being provided to enhance the
expression of attribute, which can not only extend the expression from binary to arbitrary state,
but also lighten the complexity of access policy. Therefore, both storage cost and encryption
complexity for a ciphertext are relieved. The performance analysis and the security proof show
that the proposed scheme is able to achieve efficient and secure data sharing in cloud computing.
S3I_CC16_005: An Efficient File Hierarchy Attribute-Based Encryption Scheme in Cloud
Computing
Ciphertext-policy attribute-based encryption (CP-ABE) has been a preferred encryption
technology to solve the challenging problem of secure data sharing in cloud computing. The
shared data files generally have the characteristic of multilevel hierarchy, particularly in the area
of healthcare and the military. However, the hierarchy structure of shared files has not been
explored in CP-ABE. In this paper, an efficient file hierarchy attribute-based encryption scheme
is proposed in cloud computing. The layered access structures are integrated into a single access
structure, and then, the hierarchical files are encrypted with the integrated access structure. The
ciphertext components related to attributes could be shared by the files. Therefore, both
ciphertext storage and time cost of encryption are saved. Moreover, the proposed scheme is
proved to be secure under the standard assumption. Experimental simulation shows that the
proposed scheme is highly efficient in terms of encryption and decryption. With the number of
the files increasing, the advantages of our scheme become more and more conspicuous.
S3I_CC16_006: Identity-Based Proxy-Oriented Data Uploading and Remote Data Integrity
Checking in Public Cloud

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More and more clients would like to store their data to public cloud servers
(PCSs) along with the rapid development of cloud computing. New security problems
have to be solved in order to help more clients process their data in public cloud.
When the client is restricted to access PCS, he will delegate its proxy to process his
data and upload them. On the other hand, remote data integrity checking is also an
important security problem in public cloud storage. It makes the clients check whether
their outsourced data are kept intact without downloading the whole data. From the
security problems, we propose a novel proxy-oriented data uploading and remote data
integrity checking model in identity-based public key cryptography: identity-based
proxy-oriented data uploading and remote data integrity checking in public cloud (ID-
PUIC). We give the formal definition, system model, and security model. Then, a
concrete ID-PUIC protocol is designed using the bilinear pairings. The proposed ID-
PUIC protocol is provably secure based on the hardness of computational Diffie-
Hellman problem. Our ID-PUIC protocol is also efficient and flexible. Based on the
original client's authorization, the proposed ID-PUIC protocol can realize private
remote data integrity checking, delegated remote data integrity checking, and public
remote data integrity checking.
S3I_CC16_007: Enabling Cloud Storage Auditing With Verifiable Outsourcing
of Key Updates
Key-exposure resistance has always been an important issue for in-depth cyber
defence in many security applications. Recently, how to deal with the key exposure
problem in the settings of cloud storage auditing has been proposed and studied. To
address the challenge, existing solutions all require the client to update his secret keys
in every time period, which may inevitably bring in new local burdens to the client,
especially those with limited computation resources, such as mobile phones. In this
paper, we focus on how to make the key updates as transparent as possible for the
client and propose a new paradigm called cloud storage auditing with verifiable
outsourcing of key updates. In this paradigm, key updates can be safely outsourced to
some authorized party, and thus the key-update burden on the client will be kept
minimal. In particular, we leverage the third party auditor (TPA) in many existing
public auditing designs, let it play the role of authorized party in our case, and make it
in charge of both the storage auditing and the secure key updates for key-exposure
resistance. In our design, TPA only needs to hold an encrypted version of the client's
secret key while doing all these burdensome tasks on behalf of the client. The client
only needs to download the encrypted secret key from the TPA when uploading new
files to cloud. Besides, our design also equips the client with capability to further
verify the validity of the encrypted secret keys provided by the TPA. All these salient
features are carefully designed to make the whole auditing procedure with key
exposure resistance as transparent as possible for the client. We formalize the

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definition and the security model of this paradigm. The security proof and the
performance simulation show that our detailed design instantiations are secure and
efficient.
S3I_CC16_008: Outsourcing Eigen-Decomposition and Singular Value
Decomposition of Large Matrix to a Public Cloud
Cloud computing enables customers with limited computational resources to
outsource their huge computation workloads to the cloud with massive computational
power. However, in order to utilize this computing paradigm, it presents various
challenges that need to be addressed, especially security. As eigen-decomposition
(ED) and singular value decomposition (SVD) of a matrix are widely applied in
engineering tasks, we are motivated to design secure, correct, and efficient protocols
for outsourcing the ED and SVD of a matrix to a malicious cloud in this paper. In
order to achieve security, we employ efficient privacy-preserving transformations to
protect both the input and output privacy. In order to check the correctness of the
result returned from the cloud, an efficient verification algorithm is employed. A
computational complexity analysis shows that our protocols are highly efficient. We
also introduce an outsourcing principle component analysis as an application of our
two proposed protocols.
S3I_CC16_009: Performance limitations of a text search application running in
cloud instances
This article analyzes the performance of MySQL in clouds based on commodity
hardware in order to identify the bottlenecks in the execution of series of scripts
developed on the SQL standard. The developed scripts were designed in order to
perform text search in a considerable amount of records. Two types of platforms were
employed: a physical machine that serves as host and an instance within a cloud
infrastructure. The results show that the intensive use of a relational database presents
a greater loss of performance in a cloud instance due limitations in the primary storage
system that was employed in the cloud infrastructure.

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S3I_CC16_0010: A dynamic load balancing method of cloud-center based on
SDN
In order to achieve dynamic load balancing based on data flow level, in this
paper, we apply SDN technology to the cloud data center, and propose a dynamic load
balancing method of cloud center based on SDN. The approach of using the SDN
technology in the current task scheduling flexibility, accomplish real-time monitoring
of the service node flow and load condition by the OpenFlow protocol. When the load
of system is imbalanced, the controller can allocate globally network resources.
What's more, by using dynamic correction, the load of the system is not obvious tilt in
the long run. The results of simulation show that this approach can realize and ensure
that the load will not tilt over a long period of time, and improve the system
throughput.
S3I_CC16_0011: Encryption-Based Solution for Data Sovereignty in Federated
Clouds
The rapidly growing demand for cloud services in the current business practice
has favored the success of the hybrid clouds and the advent of cloud federation. The
available literature of this topic has focused on middleware abstraction to interoperate
heterogeneous cloud platforms and orchestrate different management and business
models. However, cloud federation implies serious security and privacy issues with
respect to data sovereignty when data is outsourced across different judicial and legal
systems. This column describes a solution that applies encryption to protect data
sovereignty in federated clouds rather than restricting the elasticity and migration of
data across federated clouds.
S3I_CC16_0012: Attribute-based access control for multi-authority systems with
constant size ciphertext in cloud computing
In most existing CP-ABE schemes, there is only one authority in the system and
all the public keys and private keys are issued by this authority, which incurs
ciphertext size and computation costs in the encryption and decryption operations that
depend at least linearly on the number of attributes involved in the access policy. We
propose an efficient multi-authority CP-ABE scheme in which the authorities need not
interact to generate public information during the system initialization phase. Our
scheme has constant ciphertext length and a constant number of pairing computations.
Our scheme can be proven CPA-secure in random oracle model under the decision q-
BDHE assumption. When user's attributes revocation occurs, the scheme transfers
most re-encryption work to the cloud service provider, reducing the data owner's
computational cost on the premise of security. Finally the analysis and simulation
result show that the schemes proposed in this thesis ensure the privacy and secure
access of sensitive data stored in the cloud server, and be able to cope with the

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dynamic changes of users' access privileges in large-scale systems. Besides, the multi-
authority ABE eliminates the key escrow problem, achieves the length of ciphertext
optimization and enhances the efficiency of the encryption and decryption operations.
S3I_CC16_0013: AMTS: Adaptive multi-objective task scheduling strategy in
cloud computing
Task scheduling in cloud computing environments is a multi-objective
optimization problem, which is NP hard. It is also a challenging problem to find an
appropriate trade-off among resource utilization, energy consumption and Quality of
Service (QoS) requirements under the changing environment and diverse tasks.
Considering both processing time and transmission time, a PSO-based Adaptive
Multi-objective Task Scheduling (AMTS) Strategy is proposed in this paper. First, the
task scheduling problem is formulated. Then, a task scheduling policy is advanced to
get the optimal resource utilization, task completion time, average cost and average
energy consumption. In order to maintain the particle diversity, the adaptive
acceleration coefficient is adopted. Experimental results show that the improved PSO
algorithm can obtain quasi-optimal solutions for the cloud task scheduling problem.
S3I_CC16_0014: Efficient R-Tree Based Indexing Scheme for Server-Centric
Cloud Storage System
Cloud storage system poses new challenges to the community to support
efficient concurrent querying tasks for various data-intensive applications, where
indices always hold important positions. In this paper, we explore a practical method
to construct a two-layer indexing scheme for multi-dimensional data in diverse server-
centric cloud storage system. We first propose RT-HCN, an indexing scheme
integrating R-tree based indexing structure and HCN-based routing protocol. RT-
HCN organizes storage and compute nodes into an HCN overlay, one of the newly
proposed sever-centric data center topologies. Based on the properties of HCN, we
design a specific index mapping technique to maintain layered global indices and
corresponding query processing algorithms to support efficient query tasks. Then, we
expand the idea of RT-HCN onto another server-centric data center topology DCell,
discovering a potential generalized and feasible way of deploying two-layer indexing
schemes on other server-centric networks. Furthermore, we prove theoretically that
RT-HCN is both space-efficient and query-efficient, by which each node actually
maintains a tolerable number of global indices while high concurrent queries can be
processed within accepted overhead. We finally conduct targeted experiments on
Amazon's EC2 platforms, comparing our design with RT-CAN, a similar indexing
scheme for traditional P2P network. The results validate the query efficiency,
especially the speedup of point query of RT-HCN, depicting its potential applicability
in future data centers.

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S3I_CC16_0015: Dynamic Certification of Cloud Services: Trust, but Verify!
Although intended to ensure cloud service providers' security, reliability, and
legal compliance, current cloud service certifications are quickly outdated. Dynamic
certification, on the other hand, provides automated monitoring and auditing to verify
cloud service providers' ongoing adherence to certification requirements.
S3I_CC16_0016: Privacy preserving and delegated access control for cloud
applications
In cloud computing applications, users' data and applications are hosted by
cloud providers. This paper proposed an access control scheme that uses a
combination of discretionary access control and cryptographic techniques to secure
users' data and applications hosted by cloud providers. Many cloud applications
require users to share their data and applications hosted by cloud providers. To
facilitate resource sharing, the proposed scheme allows cloud users to delegate their
access permissions to other users easily. Using the access control policies that guard
the access to resources and the credentials submitted by users, a third party can infer
information about the cloud users. The proposed scheme uses cryptographic
techniques to obscure the access control policies and users' credentials to ensure the
privacy of the cloud users. Data encryption is used to guarantee the confidentiality of
data. Compared with existing schemes, the proposed scheme is more flexible and easy
to use. Experiments showed that the proposed scheme is also efficient.
S3I_CC16_0017: Auditing a Cloud Provider’s Compliance With Data Backup
Requirements: A Game Theoretical Analysis
The new developments in cloud computing have introduced significant security
challenges to guarantee the confidentiality, integrity, and availability of outsourced
data. A service level agreement (SLA) is usually signed between the cloud provider
(CP) and the customer. For redundancy purposes, it is important to verify the CP’s
compliance with data backup requirements in the SLA. There exist a number of
security mechanisms to check the integrity and availability of outsourced data. This
task can be performed by the customer or be delegated to an independent entity that
we will refer to as the verifier. However, checking the availability of data introduces
extra costs, which can discourage the customer of performing data verification too
often. The interaction between the verifier and the CP can be captured using game
theory in order to find an optimal data verification strategy. In this paper, we
formulate this problem as a two player non-cooperative game. We consider the case in
which each type of data is replicated a number of times, which can depend on a set of
parameters including, among others, its size and sensitivity. We analyze the strategies
of the CP and the verifier at the Nash equilibrium and derive the expected behavior of

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both the players. Finally, we validate our model numerically on a case study and
explain how we evaluate the parameters in the model.
S3I_CC16_0018: An Efficient Privacy-Preserving Ranked Keyword Search
Method
Cloud data owners prefer to outsource documents in an encrypted form for the
purpose of privacy preserving. Therefore it is essential to develop efficient and
reliable ciphertext search techniques. One challenge is that the relationship between
documents will be normally concealed in the process of encryption, which will lead to
significant search accuracy performance degradation. Also the volume of data in data
centers has experienced a dramatic growth. This will make it even more challenging
to design ciphertext search schemes that can provide efficient and reliable online
information retrieval on large volume of encrypted data. In this paper, a hierarchical
clustering method is proposed to support more search semantics and also to meet the
demand for fast ciphertext search within a big data environment. The proposed
hierarchical approach clusters the documents based on the minimum relevance
threshold, and then partitions the resulting clusters into sub-clusters until the
constraint on the maximum size of cluster is reached. In the search phase, this
approach can reach a linear computational complexity against an exponential size
increase of document collection. In order to verify the authenticity of search results, a
structure called minimum hash sub-tree is designed in this paper. Experiments have
been conducted using the collection set built from the IEEE Xplore. The results show
that with a sharp increase of documents in the dataset the search time of the proposed
method increases linearly whereas the search time of the traditional method increases
exponentially. Furthermore, the proposed method has an advantage over the
traditional method in the rank privacy and relevance of retrieved documents
S3I_CC16_0019: Towards Building Forensics Enabled Cloud Through Secure
Logging-as-a-Service
Collection and analysis of various logs (e.g., process logs, network logs) are
fundamental activities in computer forensics. Ensuring the security of the activity logs
is therefore crucial to ensure reliable forensics investigations. However, because of
the black-box nature of clouds and the volatility and co-mingling of cloud data,
providing the cloud logs to investigators while preserving users' privacy and the
integrity of logs is challenging. The current secure logging schemes, which consider
the logger as trusted cannot be applied in clouds since there is a chance that cloud
providers (logger) collude with malicious users or investigators to alter the logs. In
this paper, we analyze the threats on cloud users' activity logs considering the
collusion between cloud users, providers, and investigators. Based on the threat
model, we propose Secure-Logging-as-a-Service ( SecLaaS), which preserves various

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logs generated for the activity of virtual machines running in clouds and ensures the
confidentiality and integrity of such logs. Investigators or the court authority can only
access these logs by the RESTful APIs provided by SecLaaS, which ensures
confidentiality of logs. The integrity of the logs is ensured by hash-chain scheme and
proofs of past logs published periodically by the cloud providers. In prior research, we
used two accumulator schemes Bloom filter and RSA accumulator to build the proofs
of past logs. In this paper, we propose a new accumulator scheme - Bloom-Tree,
which performs better than the other two accumulators in terms of time and space
requirement.
S3I_CC16_0020: A Genetic Algorithm for Virtual Machine Migration in
Heterogeneous Mobile Cloud Computing
Mobile Cloud Computing (MCC) improves the performance of a mobile
application by executing it at a resourceful cloud server that can minimize execution
time compared to a resource-constrained mobile device. Virtual Machine (VM)
migration in MCC brings cloud resources closer to a user so as to further minimize the
response time of an offloaded application. Such resource migration is very effective
for interactive and real-time applications. However, the key challenge is to find an
optimal cloud server for migration that offers the maximum reduction in computation
time. In this paper, we propose a Genetic Algorithm (GA) based VM migration
model, namely GAVMM, for heterogeneous MCC system. In GAVMM, we take user
mobility and load of the cloud servers into consideration to optimize the effectiveness
of VM migration. The goal of GAVMM is to select the optimal cloud server for a
mobile VM and to minimize the total number of VM migrations, resulting in a
reduced task execution time. Additionally, we present a thorough numerical
evaluation to investigate the effectiveness of our proposed model compared to the
state-of-the-art VM migration policies.
S3I_CC16_0021: Online Resource Scheduling Under Concave Pricing for Cloud
Computing
With the booming growth of cloud computing industry, computational
resources are readily and elastically available to the customers. In order to attract
customers with various demands, most Infrastructure-as-a-service (IaaS) cloud service
providers offer several pricing strategies such as pay as you go, pay less per unit when
you use more (so called volume discount), and pay even less when you reserve. The
diverse pricing schemes among different IaaS service providers or even in the same
provider form a complex economic landscape that nurtures the market of cloud
brokers. By strategically scheduling multiple customers' resource requests, a cloud
broker can fully take advantage of the discounts offered by cloud service providers. In
this paper, we focus on how a broker may help a group of customers to fully utilize

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the volume discount pricing strategy offered by cloud service providers through cost-
efficient online resource scheduling. We present a randomized online stack-centric
scheduling algorithm (ROSA) and theoretically prove the lower bound of its
competitive ratio. Our simulation shows that ROSA achieves a competitive ratio close
to the theoretical lower bound under a special case cost function. Trace driven
simulation using Google cluster data demonstrates that ROSA is superior to the
conventional online scheduling algorithms in terms of cost saving.
S3I_CC16_0022: Dynamic Bin Packing for On-Demand Cloud Resource
Allocation
Dynamic Bin Packing (DBP) is a variant of classical bin packing, which
assumes that items may arrive and depart at arbitrary times. Existing works on DBP
generally aim to minimize the maximum number of bins ever used in the packing. In
this paper, we consider a new version of the DBP problem, namely, the MinTotal
DBP problem which targets at minimizing the total cost of the bins used overtime. It
is motivated by the request dispatching problem arising from cloud gaming systems.
We analyze the competitive ratios of the modified versions of the commonly used
First Fit, Best Fit, and Any Fit packing (the family of packing algorithms that open a
new bin only when no currently open bin can accommodate the item to be packed)
algorithms for the MinTotal DBP problem. We show that the competitive ratio of Any
Fit packing cannot be better than μ + 1, where μ is the ratio of the maximum item
duration to the minimum item duration. The competitive ratio of Best Fit packing is
not bounded for any given μ. For First Fit packing, if all the item sizes are smaller
than 1/β of the bin capacity (β> 1 is a constant), the competitive ratio has an upper
bound of β/β-1·μ+3β/β-1 + 1. For the general case, the competitive ratio of First Fit
packing has an upper bound of 2μ + 7. We also propose a Hybrid First Fit packing
algorithm that can achieve a competitive ratio no larger than 5/4 μ + 19/4 when μ is
not known and can achieve a competitive ratio no larger than μ + 5 when μ is known.
S3I_CC16_0023: A Scalable Data Chunk Similarity based Compression
Approach for Efficient Big Sensing Data Processing on Cloud

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Big sensing data is prevalent in both industry and scientific research
applications where the data is generated with high volume and velocity. Cloud
computing provides a promising platform for big sensing data processing and storage
as it provides a flexible stack of massive computing, storage, and software services in
a scalable manner. Current big sensing data processing on Cloud have adopted some
data compression techniques. However, due to the high volume and velocity of big
sensing data, traditional data compression techniques lack sufficient efficiency and
scalability for data processing. Based on specific on-Cloud data compression
requirements, we propose a novel scalable data compression approach based on
calculating similarity among the partitioned data chunks. Instead of compressing basic
data units, the compression will be conducted over partitioned data chunks. To restore
original data sets, some restoration functions and predictions will be designed.
MapReduce is used for algorithm implementation to achieve extra scalability on
Cloud. With real world meteorological big sensing data experiments on U-Cloud
platform, we demonstrate that the proposed scalable compression approach based on
data chunk similarity can significantly improve data compression efficiency with
affordable data accuracy loss.
S3I_J16_001 - SecRBAC: Secure data in the Clouds
Most current security solutions are based on perimeter security. However,
Cloud computing breaks the organization perimeters. When data resides in the Cloud,
they reside outside the organizational bounds. This leads users to a loos of control
over their data and raises reasonable security concerns that slow down the adoption of
Cloud computing. Is the Cloud service provider accessing the data? Is it legitimately
applying the access control policy defined by the user? This paper presents a data-
centric access control solution with enriched role-based expressiveness in which
security is focused on protecting user data regardless the Cloud service provider that
holds it. Novel identity-based and proxy re-encryption techniques are used to protect
the authorization model. Data is encrypted and authorization rules are
cryptographically protected to preserve user data against the service provider access
or misbehavior. The authorization model provides high expressiveness with role
hierarchy and resource hierarchy support. The solution takes advantage of the logic
formalism provided by Semantic Web technologies, which enables advanced rule
management like semantic conflict detection. A proof of concept implementation has
been developed and a working prototypical deployment of the proposal has been
integrated within Google services.
S3I_J16_002 - Trust Agent-Based Behavior Induction in Social Networks

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The essence of social networks is that they can influence people's public
opinions and group behaviors form quickly. Negative group behavior influences
societal stability significantly, but existing behavior-induction approaches are too
simple and inefficient. To automatically and efficiently induct behavior in social
networks, this article introduces trust agents and designs their features according to
group behavior features. In addition, a dynamics control mechanism can be generated
to coordinate participant behaviors in social networks to avoid a specific restricted
negative group behavior.
S3I_J16_003 - A Shoulder Surfing Resistant Graphical Authentication System
Authentication based on passwords is used largely in applications for computer
security and privacy. However, human actions such as choosing bad passwords and
inputting passwords in an insecure way are regarded as ”the weakest link” in the
authentication chain. Rather than arbitrary alphanumeric strings, users tend to choose
passwords either short or meaningful for easy memorization. With web applications
and mobile apps piling up, people can access these applications anytime and
anywhere with various devices. This evolution brings great convenience but also
increases the probability of exposing passwords to shoulder surfing attacks. Attackers
can observe directly or use external recording devices to collect users’ credentials. To
overcome this problem, we proposed a novel authentication system PassMatrix, based
on graphical passwords to resist shoulder surfing attacks. With a one-time valid login
indicator and circulative horizontal and vertical bars covering the entire scope of pass-
images, PassMatrix offers no hint for attackers to figure out or narrow down the
password even they conduct multiple camera-based attacks. We also implemented a
PassMatrix prototype on Android and carried out real user experiments to evaluate its
memorability and usability. From the experimental result, the proposed system
achieves better resistance to shoulder surfing attacks while maintaining usability.
S3I_J16_004 - A Locality Sensitive Low-Rank Model for Image Tag Completion
Many visual applications have benefited from the outburst of web images, yet
the imprecise and incomplete tags arbitrarily provided by users, as the thorn of the
rose, may hamper the performance of retrieval or indexing systems relying on such
data. In this paper, we propose a novel locality sensitive low-rank model for image tag
completion, which approximates the global nonlinear model with a collection of local
linear models. To effectively infuse the idea of locality sensitivity, a simple and
effective pre-processing module is designed to learn suitable representation for data
partition, and a global consensus regularizer is introduced to mitigate the risk of
overfitting. Meanwhile, low-rank matrix factorization is employed as local models,
where the local geometry structures are preserved for the low-dimensional
representation of both tags and samples. Extensive empirical evaluations conducted

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on three datasets demonstrate the effectiveness and efficiency of the proposed method,
where our method outperforms pervious ones by a large margin.
S3I_J16_005 - Quality-Aware Subgraph Matching Over Inconsistent
Probabilistic Graph Databases
Resource Description Framework (RDF) has been widely used in the Semantic
Web to describe resources and their relationships. The RDF graph is one of the most
commonly used representations for RDF data. However, in many real applications
such as the data extraction/integration, RDF graphs integrated from different data
sources may often contain uncertain and inconsistent information (e.g., uncertain
labels or that violate facts/rules), due to the unreliability of data sources. In this paper,
we formalize the RDF data by inconsistent probabilistic RDF graphs, which contain
both inconsistencies and uncertainty. With such a probabilistic graph model, we focus
on an important problem, quality-aware subgraph matching over inconsistent
probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent
probabilistic RDF graphs that are isomorphic to a given query graph and with high
quality scores (considering both consistency and uncertainty). In order to efficiently
answer QA-gMatch queries, we provide two effective pruning methods, namely
adaptive label pruning and quality score pruning, which can greatly filter out false
alarms of subgraphs. We also design an effective index to facilitate our proposed
pruning methods, and propose an efficient approach for processing QA-gMatch
queries. Finally, we demonstrate the efficiency and effectiveness of our proposed
approaches through extensive experiments.
S3I_J16_006 - Inverted Linear Quadtree: Efficient Top K Spatial Keyword
Search
With advances in geo-positioning technologies and geo-location services, there
are a rapidly growing amount of spatio-textual objects collected in many applications
such as location based services and social networks, in which an object is described
by its spatial location and a set of keywords (terms). Consequently, the study of
spatial keyword search which explores both location and textual description of the
objects has attracted great attention from the commercial organizations and research
communities. In the paper, we study two fundamental problems in the spatial keyword
queries: top $k$ spatial keyword search (TOPK-SK), and batch top $k$ spatial
keyword search (BTOPK-SK). Given a set of spatio-textual objects, a query location
and a set of query keywords, the TOPK-SK retrieves the closest $k$ objects each of
which contains all keywords in the query. BTOPK-SK is the batch processing of sets
of TOPK-SK queries. Based on the inverted index and the linear quadtree, we propose
a novel index structure, called inverted linear quadtree (IL-Quadtree), which is
carefully designed to exploit both spatial and keyword based pruning techniques to

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effectively reduce the search space. An efficient algorithm is then developed to tackle
top $k$ spatial keyword sea- ch. To further enhance the filtering capability of the
signature of linear quadtree, we propose a partition based method. In addition, to deal
with BTOPK-SK, we design a new computing paradigm which partition the queries
into groups based on both spatial proximity and the textual relevance between queries.
We show that the IL-Quadtree technique can also efficiently support BTOPK-SK.
Comprehensive experiments on real and synthetic data clearly demonstrate the
efficiency of our methods.
S3I_J16_007 - Practical Approximate k Nearest Neighbor Queries with Location
and Query Privacy
In mobile communication, spatial queries pose a serious threat to user location
privacy because the location of a query may reveal sensitive information about the
mobile user. In this paper, we study approximate k nearest neighbor (kNN) queries
where the mobile user queries the location-based service (LBS) provider about
approximate k nearest points of interest (POIs) on the basis of his current location. We
propose a basic solution and a generic solution for the mobile user to preserve his
location and query privacy in approximate kNN queries. The proposed solutions are
mainly built on the Paillier public-key cryptosystem and can provide both location and
query privacy. To preserve query privacy, our basic solution allows the mobile user to
retrieve one type of POIs, for example, approximate k nearest car parks, without
revealing to the LBS provider what type of points is retrieved. Our generic solution
can be applied to multiple discrete type attributes of private location-based queries.
Compared with existing solutions for kNN queries with location privacy, our solution
is more efficient. Experiments have shown that our solution is practical for kNN
queries.
S3I_J16_008 - Privacy Protection for Wireless Medical Sensor Data
In recent years, wireless sensor networks have been widely used in healthcare
applications, such as hospital and home patient monitoring. Wireless medical sensor
networks are more vulnerable to eavesdropping, modification, impersonation and
replaying attacks than the wired networks. A lot of work has been done to secure
wireless medical sensor networks. The existing solutions can protect the patient data
during transmission, but cannot stop the inside attack where the administrator of the
patient database reveals the sensitive patient data. In this paper, we propose a practical
approach to prevent the inside attack by using multiple data servers to store patient
data. The main contribution of this paper is securely distributing the patient data in
multiple data servers and employing the Paillier and ElGamal cryptosystems to
perform statistic analysis on the patient data without compromising the patients'
privacy.

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S3I_J16_009 - Enabling Fine-Grained Multi-Keyword Search Supporting
Classified Sub-Dictionaries over Encrypted Cloud Data
Using cloud computing, individuals can store their data on remote servers and
allow data access to public users through the cloud servers. As the outsourced data are
likely to contain sensitive privacy information, they are typically encrypted before
uploaded to the cloud. This, however, significantly limits the usability of outsourced
data due to the difficulty of searching over the encrypted data. In this paper, we
address this issue by developing the fine-grained multi-keyword search schemes over
encrypted cloud data. Our original contributions are three-fold. First, we introduce the
relevance scores and preference factors upon keywords which enable the precise
keyword search and personalized user experience. Second, we develop a practical and
very efficient multi-keyword search scheme. The proposed scheme can support
complicated logic search the mixed “AND”, “OR” and “NO” operations of keywords.
Third, we further employ the classified sub-dictionaries technique to achieve better
efficiency on index building, trapdoor generating and query. Lastly, we analyze the
security of the proposed schemes in terms of confidentiality of documents, privacy
protection of index and trapdoor, and unlinkability of trapdoor. Through extensive
experiments using the real-world dataset, we validate the performance of the proposed
schemes. Both the security analysis and experimental results demonstrate that the
proposed schemes can achieve the same security level comparing to the existing ones
and better performance in terms of functionality, query complexity and efficiency.
S3I_J16_010 - Leveraging Data Deduplication to Improve the Performance of
Primary Storage Systems in the Cloud
With the explosive growth in data volume, the I/O bottleneck has become an
increasingly daunting challenge for big data analytics in the Cloud. Recent studies
have shown that moderate to high data redundancy clearly exists in primary storage
systems in the Cloud. Our experimental studies reveal that data redundancy exhibits a
much higher level of intensity on the I/O path than that on disks due to relatively high
temporal access locality associated with small I/O requests to redundant data.
Moreover, directly applying data deduplication to primary storage systems in the
Cloud will likely cause space contention in memory and data fragmentation on disks.
Based on these observations, we propose a performance-oriented I/O deduplication,
called POD, rather than a capacity-oriented I/O deduplication, exemplified by iDedup,
to improve the I/O performance of primary storage systems in the Cloud without
sacrificing capacity savings of the latter. POD takes a two-pronged approach to
improving the performance of primary storage systems and minimizing performance
overhead of deduplication, namely, a request-based selective deduplication technique,
called Select-Dedupe, to alleviate the data fragmentation and an adaptive memory

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management scheme, called iCache, to ease the memory contention between the
bursty read traffic and the bursty write traffic. We have implemented a prototype of
POD as a module in the Linux operating system. The experiments conducted on our
lightweight prototype implementation of POD show that POD significantly
outperforms iDedup in the I/O performance measure by up to 87.9 percent with an
average of 58.8 percent. Moreover, our evaluation results also show that POD
achieves comparable or better capacity savings than iDedup.
S3I_J16_011 - Two-Factor Data Security Protection Mechanism for Cloud
Storage System
In this paper, we propose a two-factor data security protection mechanism with
factor revocability for cloud storage system. Our system allows a sender to send an
encrypted message to a receiver through a cloud storage server. The sender only needs
to know the identity of the receiver but no other information (such as its public key or
its certificate). The receiver needs to possess two things in order to decrypt the
ciphertext. The first thing is his/her secret key stored in the computer. The second
thing is a unique personal security device which connects to the computer. It is
impossible to decrypt the ciphertext without either piece. More importantly, once the
security device is stolen or lost, this device is revoked. It cannot be used to decrypt
any ciphertext. This can be done by the cloud server which will immediately execute
some algorithms to change the existing ciphertext to be un-decryptable by this device.
This process is completely transparent to the sender. Furthermore, the cloud server
cannot decrypt any ciphertext at any time. The security and efficiency analysis show
that our system is not only secure but also practical.
S3I_J16_012 - Providing Privacy-Aware Incentives in Mobile Sensing Systems
Mobile sensing relies on data contributed by users through their mobile device
(e.g., smart phone) to obtain useful information about people and their surroundings.
However, users may not want to contribute due to lack of incentives and concerns on
possible privacy leakage. To effectively promote user participation, both incentive and
privacy issues should be addressed. Although incentive and privacy have been
addressed separately in mobile sensing, it is still an open problem to address them
simultaneously. In this paper, we propose two credit-based privacy-aware incentive
schemes for mobile sensing systems, where the focus is on privacy protection instead
of on the design of incentive mechanisms. Our schemes enable mobile users to earn
credits by contributing data without leaking which data they have contributed, and
ensure that malicious users cannot abuse the system to earn unlimited credits.
Specifically, the first scheme considers scenarios where an online trusted third party
(TTP) is available, and relies on the TTP to protect user privacy and prevent abuse
attacks. The second scheme considers scenarios where no online TTP is available. It

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applies blind signature, partially blind signature, and a novel extended Merkle tree
technique to protect user privacy and prevent abuse attacks. Security analysis and cost
evaluations show that our schemes are secure and efficient.
S3I_J16_013 - A Simple Message-Optimal Algorithm for Random Sampling
from a Distributed Stream
We present a simple, message-optimal algorithm for maintaining a random
sample from a large data stream whose input elements are distributed across multiple
sites that communicate via a central coordinator. At any point in time, the set of
elements held by the coordinator represent a uniform random sample from the set of
all the elements observed so far. When compared with prior work, our algorithms
asymptotically improve the total number of messages sent in the system. We present a
matching lower bound, showing that our protocol sends the optimal number of
messages up to a constant factor with large probability. We also consider the
important case when the distribution of elements across different sites is non-uniform,
and show that for such inputs, our algorithm significantly outperforms prior solutions.
S3I_J16_014 - Multi-Grained Block Management to Enhance the Space
Utilization of File Systems on PCM Storages
Phase-change memory (PCM) is a promising candidate as a storage medium to
resolve the performance gap between main memory and storage in battery-powered
mobile computing systems. However, it is more expensive than flash memory, and
thus introduces a more serious storage capacity issue for low-cost solutions. This issue
is further exacerbated by the fact that existing file systems are usually designed to
trade space utilization for performance over block-oriented storage devices. In this
work, we propose a multi-grained block management strategy to improve the space
utilization of file systems over PCM-based storage systems. By utilizing the byte-
addressability and fast read/write feature of PCM, a methodology is proposed to
dynamically allocate multiple sizes of blocks to fit the size of each file, so as to
resolve the space fragmentation issue with minimized space and management
overheads. The space utilization of file systems is analyzed with consideration of
block sizes. A series of experiments was conducted to evaluate the efficacy of the
proposed strategy, and the results show that the proposed strategy can significantly
improve the space utilization of file systems.
S3I_J16_015 - Resource-Saving File Management Scheme for Online Video
Provisioning on Content Delivery Networks
Content delivery networks (CDNs) have been widely implemented to provide
scalable cloud services. Such networks support resource pooling by allowing virtual
machines or physical servers to be dynamically activated and deactivated according to

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current user demand. This paper examines online video replication and placement
problems in CDNs. An effective video provisioning scheme must simultaneously (i)
utilize system resources to reduce total energy consumption and (ii) limit replication
overhead. We propose a scheme called adaptive data placement (ADP) that can
dynamically place and reorganize video replicas among cache servers on subscribers’
arrival and departure. Both the analyses and simulation results show that ADP can
reduce the number of activated cache servers with limited replication overhead. In
addition, ADP's performance is approximate to the optimal solution.
S3I_J16_016 - Inference Attack on Browsing History of Twitter Users Using
Public Click Analytics and Twitter Metadata
Twitter is a popular online social network service for sharing short messages
(tweets) among friends. Its users frequently use URL shortening services that provide
(i) a short alias of a long URL for sharing it via tweets and (ii) public click analytics
of shortened URLs. The public click analytics is provided in an aggregated form to
preserve the privacy of individual users. In this paper, we propose practical attack
techniques inferring who clicks which shortened URLs on Twitter using the
combination of public information: Twitter metadata and public click analytics.
Unlike the conventional browser history stealing attacks, our attacks only demand
publicly available information provided by Twitter and URL shortening services.
Evaluation results show that our attack can compromise Twitter users' privacy with
high accuracy.
S3I_J16_017 - Clustering Data Streams Based on Shared Density between Micro-
Clusters
As more and more applications produce streaming data, clustering data streams
has become an important technique for data and knowledge engineering. A typical
approach is to summarize the data stream in real-time with an online process into a
large number of so called micro-clusters. Micro-clusters represent local density
estimates by aggregating the information of many data points in a defined area. On
demand, a (modified) conventional clustering algorithm is used in a second offline
step to re-cluster the micro-clusters into larger final clusters. For re-clustering, the
centers of the micro-clusters are used as pseudo points with the density estimates used
as their weights. However, information about density in the area between micro-
clusters is not preserved in the online process and re-clustering is based on possibly
inaccurate assumptions about the distribution of data within and between micro-
clusters (e.g., uniform or Gaussian). This paper describes DB_STREAM, the first
micro-cluster-based online clustering component that explicitly captures the density
between micro-clusters via a shared density graph. The density information in this
graph is then exploited for re-clustering based on actual density between adjacent

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micro-clusters. We discuss the space and time complexity of maintaining the shared
density graph. Experiments on a wide range of synthetic and real data sets highlight
that using shared density improves clustering quality over other popular data stream
clustering methods which require the creation of a larger number of smaller micro-
clusters to achieve comparable results.
IEEE-2016 NS2 Project Titles
S3I_NS2_16_001 : Network Topology Tomography Under Multipath Routing
Network topology tomography can infer a tree topology for single-source networks
using end-to-end measurements. However, multipath routing, which introduces
multiple paths between end-hosts, violates the tree topology model. In this letter, we
demonstrate that such nontree topologies are also identifiable. We employ graph cuts
and show the nontree topology can be decomposed into two identifiable
subtopologies. And by reconnecting the cut paths, the nontree topology can be
recovered after obtaining these subtopologies. To detect the paths that share cuts, we
propose a scheme based on measurements of the end-to-end packet arrival order.
Simulation results show that our scheme achieves the desirable detection accuracy.
S3I_NS2_16_002 : HDEER: A Distributed Routing Scheme for Energy-Efficient
Networking
The proliferation of new online Internet services has substantially increased the
energy consumption in wired networks, which has become a critical issue for Internet
service providers. In this paper, we target the network-wide energy-saving problem by
leveraging speed scaling as the energy-saving strategy. We propose a distributed
routing scheme–HDEER–to improve network energy efficiency in a distributed
manner without significantly compromising traffic delay. HDEER is a two-stage
routing scheme where a simple distributed multipath finding algorithm is firstly
performed to guarantee loop-free routing, and then a distributed routing algorithm is
executed for energy-efficient routing in each node among the multiple loop-free paths.
We conduct extensive experiments on the NS3 simulator and simulations with real
network topologies in different scales under different traffic scenarios. Experiment
results show that HDEER can reduce network energy consumption with a fair tradeoff
between network energy consumption and traffic delay.
S3I_NS2_16_003 : Mobile Coordinated Wireless Sensor Network: An Energy
Efficient Scheme for Real-Time Transmissions

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This paper introduces the mobile access coordinated wireless sensor network (MC-
WSN)—a novel energy efficient scheme for time-sensitive applications. In
conventional sensor networks with mobile access points (SENMA), the mobile access
points (MAs) traverse the network to collect information directly from individual
sensors. While simplifying the routing process, a major limitation with SENMA is
that data transmission is limited by the physical speed of the MAs and their trajectory
length, resulting in low throughput and large delay. In an effort to resolve this
problem, we introduce the MC-WSN architecture, for which a major feature is that:
through active network deployment and topology design, the number of hops from
any sensor to the MA can be limited to a pre-specified number. In this paper, we
investigate the optimal topology design that minimizes the average number of hops
from sensor to MA, and provide the throughput analysis under both single-path and
multipath routing cases. Moreover, putting MC-WSN in the bigger picture of network
design and development, we provide a unified framework for wireless network
modeling and characterization. Under this general framework, it can be seen that MC-
WSN reflects the integration of structure-ensured reliability/efficiency and ad-hoc
enabled flexibility.
S3I_NS2_16_004 : A secure-efficient data collection algorithm based on self-
adaptive sensing model in mobile Internet of vehicles
Existing research on data collection using wireless mobile vehicle network
emphasizes the reliable delivery of information. However, other performance
requirements such as life cycle of nodes, stability and security are not set as primary
design objectives. This makes data collection ability of vehicular nodes in real
application environment inferior. By considering the features of nodes in wireless
IoV, such as large scales of deployment, volatility and low time delay, an efficient
data collection algorithm is proposed for mobile vehicle network environment. An
adaptive sensing model is designed to establish vehicular data collection protocol. The
protocol adopts group management in model communication. The vehicular sensing
node in group can adjust network sensing chain according to sensing distance
threshold with surrounding nodes. It will dynamically choose a combination of
network sensing chains on basis of remaining energy and location characteristics of
surrounding nodes. In addition, secure data collection between sensing nodes is
undertaken as well. The simulation and experiments show that the vehicular node can
realize secure and real-time data collection. Moreover, the proposed algorithm is
superior in vehicular network life cycle, power consumption and reliability of data
collection by comparing to other algorithms.
S3I_NS2_16_005 : Resisting blackhole attacks on MANETs

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MANET routing protocols are designed based on the assumption that all nodes
cooperate without maliciously disrupting the operation of the routing protocol. AODV
is a reactive MANET routing protocol that is vulnerable to a dramatic collapse of
network performance in the presence of blackhole attack. The paper introduces a new
concept of Self-Protocol Trustiness (SPT) in which detecting a malicious intruder is
accomplished by complying with the normal protocol behavior and lures the
malicious node to give an implicit avowal of its malicious behavior. We present a
Blackhole Resisting Mechanism (BRM) to resist such attacks that can be incorporated
into any reactive routing protocol. It does not require expensive cryptography or
authentication mechanisms, but relies on locally applied timers and thresholds to
classify nodes as malicious. No modifications to the packet formats are needed, so the
overhead is a small amount of calculation at nodes, and no extra communication.
Using NS2 simulation, we compare the performance of networks using AODV under
blackhole attacks with and without our mechanism to SAODV, showing that it
significantly reduces the effect of a blackhole attack.
S3I_NS2_16_006 : Secret Common Randomness From Routing Metadata in Ad
Hoc Networks
Establishing secret common randomness between two or multiple devices in a
network resides at the root of communication security. In its most frequent form of
key establishment, the problem is traditionally decomposed into a randomness
generation stage (randomness purity is subject to employing often costly true random
number generators) and an information-exchange agreement stage, which relies either
on public-key infrastructure or on symmetric encryption (key wrapping). In this paper,
we propose a secret-common-randomness establishment algorithm for ad hoc
networks, which works by harvesting randomness directly from the network routing
metadata, thus achieving both pure randomness generation and (implicitly) secret-key
agreement. Our algorithm relies on the route discovery phase of an ad hoc network
employing the dynamic source routing protocol, is lightweight, and requires relatively
little communication overhead. The algorithm is evaluated for various network
parameters in an OPNET ad hoc network simulator. Our results show that, in just 10
min, thousands of secret random bits can be generated network-wide, between
different pairs in a network of 50 users.
S3I_NS2_16_007 : Queue Stability Analysis in Network Coded Wireless
Multicast Network
This letter considers a single hop wireless multicast network. We first introduce a
new two-level queuing system consisting of a main queue and a virtual queue, where
each packet in the virtual queue is associated with a user index set. Then, we propose
a network coding based packet scheduling method to maximize the system input rate

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under the queue stability constraint. Our analytical and simulation results demonstrate
the effectiveness of the proposed solution.
S3I_NS2_16_008 : Delay-Energy Tradeoff in Multicast Scheduling for Green
Cellular Systems
Multicast transmission based on real-time network state information is a resource-
friendly technique to improve the energy efficiency and reduce the traffic burden for
cellular systems. This paper evaluates the effectiveness of this technique for downlink
transmissions. In particular, a scenario is considered in which multiple mobile users
(MUs) asynchronously request to download one common message locally cached at a
base station (BS). Due to the randomness of both the channel conditions and the
request arrivals from the MUs, the BS may choose to intelligently hold the arrived
requests, especially when the channel conditions are bad or the number of requests is
small, and then serve them in one shot later via multicasting. Clearly it is of great
interest to balance the delay (incurred by holding the requests) and the energy
efficiency (EE, defined as the energy cost per request), and this motivates us to
quantify the fundamental tradeoff for the proposed “hold-then-serve” scheme. For the
scenario with single channel and unit message sizes, it is shown that for a fixed
channel bandwidth, the delay-EE tradeoff reduces to judiciously choosing the optimal
stopping rule for when to serve all the arrived requests, where the effect of the
bandwidth on the achievable delay-EE region is discussed further. By using optimal
stopping theory, it is shown that the optimal stopping rule exists for general Markov
channel models and request arrival processes. Particularly, for the hard deadline and
proportional delay penalty cases, it is shown that the optimal stopping rule exhibits a
threshold structure, and the corresponding threshold in the former case is time varying
while in the latter case it is a constant. Finally, for the more general scenario with
multiple channels and arbitrary message sizes, the optimal scheduling is formulated as
a Markov decision process problem, where some efficient suboptimal scheduling
algorithms are proposed.
S3I_NS2_16_009 : Delay Analysis of Social Group Multicast-Aided Content
Dissemination in Cellular System
Based on the common interest of mobile users (MUs) in a social group, the
dissemination of content across the social group is studied as a powerful supplement
to conventional cellular communication with the goal of improving the delay
performance of the content dissemination process. The content popularity is modeled
by a Zipf distribution to characterize the MUs' different interests in different contents.
The factor of altruism (FA) terminology is introduced for quantifying the willingness
of content owners to share their content. We model the dissemination process of a
specific packet by a pure-birth-based Markov chain and evaluate the statistical

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properties of both the network's dissemination delay as well as of the individual user-
delay. Compared to the conventional base station (BS)-aided multicast, our scheme is
capable of reducing the average dissemination delay by about 56.5%. Moreover, in
contrast to the BS-aided multicast, increasing the number of MUs in the target social
group is capable of reducing the average individual user-delay by 44.1% relying on
our scheme. Furthermore, our scheme is more suitable for disseminating a popular
piece of content.
S3I_NS2_16_010 : End-to-End coding for TCP
Although widely used, TCP has many limitations in meeting the throughput and
latency requirements of applications in wireless networks, high-speed data center
networks, and heterogeneous multi-path networks. Instead of relying purely on
retransmission upon packet loss, coding has potential to improve the performance of
TCP by ensuring better transmission reliability. Coding has been verified to work well
at the link layer but has not been fully studied at the transport layer. There are many
advantages but also challenges in exploiting coding at the transport layer. In this
article, we focus on how to leverage end-to-end coding in TCP. We reveal the
problems TCP faces and the opportunities coding can bring to improve TCP
performance. We further analyze the challenges faced when applying the coding
techniques to TCP and present the current applications of coding in TCP.
S3I_NS2_16_011 : Link Allocation for Multiuser Systems With Hybrid RF/FSO
Backhaul: Delay-Limited and Delay-Tolerant Designs
In this paper, we consider a cascaded radio frequency (RF) and hybrid RF/free space
optical (FSO) system where several mobile users transmit their data over an RF link to
a decode-and-forward relay node (e.g., a small cell base station) and the relay
forwards the information to a destination (e.g., a macro-cell base station) over a
hybrid RF/FSO backhaul link. The relay and the destination employ multiple antennas
for transmission and reception over the RF links while each mobile user has a single
antenna. The RF links are orthogonal to the FSO link but half-duplex with respect to
each other, i.e., either the user-relay RF link or the relay-destination RF link is active.
For this communication setup, we derive the optimal fixed and adaptive link
allocation policies for sharing the transmission time between the RF links based on
the statistical and instantaneous channel state information (CSI) of the RF and FSO
links, respectively. Thereby, we consider the following two scenarios depending on
the delay requirements: 1) delay-limited transmission where the relay has to
immediately forward the packets received from the users to the destination, and 2)
delay-tolerant transmission where the relay is allowed to store the packets received
from the users in its buffer and forward them to the destination when the quality of the
relay-destination RF link is favorable. Our numerical results illustrate the

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effectiveness of the proposed communication architecture and link allocation policies,
and their superiority compared to existing schemes, which employ only one type of
backhaul link.
S3I_NS2_16_012 : Fair Routing for Overlapped Cooperative Heterogeneous
In recent years, as wireless sensor networks (WSNs) are widely diffused, multiple
overlapping WSNs constructed on the same area become more common. In such a
situation, their lifetime is expected to be extended by cooperative packet forwarding.
Although some researchers have studied about cooperation in multiple WSNs, most of
them do not consider the heterogeneity in the characteristics of each WSN such as
battery capacity, operation start time, the number of nodes, nodes locations, energy
consumption, packet size and/or data transmission timing, and so on. In a
heterogeneous environment, naive lifetime improvement with cooperation may not be
fair. In this paper, we propose a fair cooperative routing method for heterogeneous
overlapped WSNs. It introduces an energy pool to maintain the total amount of energy
consumption by cooperative forwarding. The energy pool plays a role of broker for
fair cooperation. Finally, simulation results show the excellent performance of the
proposed method.
S3I_NS2_16_013 : Adaptive and Channel-Aware Detection of Selective
Forwarding Attacks in Wireless Sensor Networks
Wireless sensor networks (WSNs) are vulnerable to selective forwarding attacks that
can maliciously drop a subset of forwarding packets to degrade network performance
and jeopardize the information integrity. Meanwhile, due to the unstable wireless
channel in WSNs, the packet loss rate during the communication of sensor nodes may
be high and vary from time to time. It poses a great challenge to distinguish the
malicious drop and normal packet loss. In this paper, we propose a channel-aware
reputation system with adaptive detection threshold (CRS-A) to detect selective
forwarding attacks in WSNs. The CRS-A evaluates the data forwarding behaviors of
sensor nodes, according to the deviation of the monitored packet loss and the
estimated normal loss. To optimize the detection accuracy of CRS-A, we theoretically
derive the optimal threshold for forwarding evaluation, which is adaptive to the time-
varied channel condition and the estimated attack probabilities of compromised nodes.
Furthermore, an attack-tolerant data forwarding scheme is developed to collaborate
with CRS-A for stimulating the forwarding cooperation of compromised nodes and
improving the data delivery ratio of the network. Extensive simulation results
demonstrate that CRS-A can accurately detect selective forwarding attacks and
identify the compromised sensor nodes, while the attack-tolerant data forwarding
scheme can significantly improve the data delivery ratio of the network.

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S3I_NS2_16_014 : Reverse Update: A Consistent Policy Update Scheme for
Software-Defined Networking
Policy and path updates are common causes of network instability, leading to service
disruptions or vulnerable intermediate states. In this letter, we propose the reverse
update, an update scheme for software-defined networking that guarantees to preserve
properties of flows during the transition time. We prove through a formal model that
the proposal achieves consistent policy updates, in which in-transit packets are always
handled in the next forwarding hops by the same or a more recent policy. The main
contributions are: 1) a relaxation of the concept of per-packet-consistency in the data
plane of software-defined networking; and 2) a policy update scheme, proved to be
consistent and efficient. A software-defined networking simulator was developed and
validated. The results of our simulations show that the proposed reverse update
scheme is faster and has lower overhead than the current two-phase update proposed
in the literature.
S3I_NS2_16_015 : On the Throughput-Delay Tradeoff in Georouting Networks
We study the scaling properties of a georouting scheme in a wireless multi-hop
network of n mobile nodes. Our aim is to increase the network capacity quasi-linearly
with n , while keeping the average delay bounded. In our model, we consider mobile
nodes moving according to an independent identically distributed random walk with
velocity v and transmitting packets to randomly chosen fixed and known destinations.
The average packet delivery delay of our scheme is of order 1/v , and it achieves
network capacity of order ({n}/{log nlog log n}) . This shows a practical
throughput-delay tradeoff, in particular when compared with the seminal result of
Gupta and Kumar, which shows network capacity of order {(n/log n)}^{1/2} and
negligible delay and the groundbreaking result of Grossglauser and Tse, which
achieves network capacity of order n but with an average delay of order sqrt {n}/v .
The foundation of our improved capacity and delay tradeoff relies on the fact that we
use a mobility model that contains straight-line segments, a model that we consider
more realistic than classic Brownian motions. We confirm the generality of our
analytical results using simulations under various interference models.
S3I_NS2_16_016 : An Eﬃcient Tree-based Self-Organizing Protocol for Internet
of Things
Tree networks are widely applied in Sensor Networks of Internet of Things (IoTs).
This paper proposes an Efficient Tree-based Self-organizing Protocol (ETSP) for
sensor networks of IoTs. In ETSP, all nodes are divided into two kinds: network
nodes and non-network nodes. Network nodes can broadcast packets to their
neighboring nodes. Non-network nodes collect the broadcasted packets and determine

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whether to join the network. During the self-organizing process, we use different
metrics such as number of child nodes, hop, communication distance and residual
energy to reach available sink nodes’ weight, the node with max weight will be
selected as sink node. Non-network nodes can be turned into network nodes when
they join the network successfully. Then a tree-based network can be obtained one
layer by one layer. The topology is adjusted dynamically to balance energy
consumption and prolong network lifetime. We conduct experiments with NS2 to
evaluate ETSP. Simulation results show that our proposed protocol can construct a
reliable tree-based network quickly. With the network scale increasing, the self-
organization time, average hop and packet loss ratio won’t increase more.
Furthermore, the success rate of packet in ETSP is much higher compared with
AODV and DSDV.
S3I_NS2_16_017 : Modified AODV Routing Protocol to Improve Security and
Performance against Black Hole Attack
A Mobile Ad hoc NETwork (MANET) is a collection of autonomous nodes that have
the ability to communicate with each other without having fixed infrastructure or
centralized access point such as a base station. This kind of networks is very
susceptible to adversary's malicious attacks, due to the dynamic changes of the
network topology, trusting the nodes to each other, lack of fixed substructure for the
analysis of nodes behaviors and constrained resources. One of these attacks is black
hole attack. In this attack, malicious nodes inject fault routing information to the
network and lead all data packets toward themselves, then destroy them all. In this
paper, we propose a solution, which enhances the security of the Ad-hoc On-demand
Distance Vector (AODV) routing protocol to encounter the black hole attacks. Our
solution avoids the black hole and the multiple black hole attacks. The simulation
results using the Network Simulator NS2 shows that our protocol provides better
security and better performance in terms of the packet delivery ratio than the AODV
routing protocol in the presence of one or multiple black hole attacks with marginal
rise in average end-to-end delay and normalized routing overhead.
S3I_NS2_16_018 : Resisting Blackhole Attacks on MANETs
MANET routing protocols are designed based on the assumption that all nodes
cooperate without maliciously disrupting the operation of the routing protocol. AODV
is a reactive MANET routing protocol that is vulnerable to a dramatic collapse of
network performance in the presence of blackhole attack. The paper introduces a new
concept of Self-Protocol Trustiness (SPT) in which detecting a malicious intruder is
accomplished by complying with the normal protocol behavior and lures the
malicious node to give an implicit avowal of its malicious behavior. We present a
Blackhole Resisting Mechanism (BRM) to resist such attacks that can be incorporated

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into any reactive routing protocol. It does not require expensive cryptography or
authentication mechanisms, but relies on locally applied timers and thresholds to
classify nodes as malicious. No modifications to the packet formats are needed, so the
overhead is a small amount of calculation at nodes, and no extra communication.
Using NS2 simulation, we compare the performance of networks using AODV under
blackhole attacks with and without our mechanism to SAODV, showing that it
significantly reduces the effect of a blackhole attack.
S3I_NS2_16_019 : Constructing A Shortest Path Overhearing Tree With
Maximum Lifetime In WSNs
Secure data collection is an important problem in wireless sensor networks. Different
approaches have been proposed. One of them is overhearing. We investigate the
problem of constructing a shortest path overhearing tree with the maximum lifetime.
We propose three approaches. The first one is a polynomial-time heuristic. The
second one uses ILP (Integer Linear Programming) to iteratively find a monitoring
node and a parent for each sensor node. The last one optimally solves the problem by
using MINLP (Mixed-Integer Non-Linear Programming). We have implemented the
three approaches using MIDACO solver and MATLAB Intlinprog, and performed
extensive simulations using NS2.35. The simulation results show that the average
lifetime of all the network instances achieved by the heuristic approach is 85.69% of
that achieved by the ILP-based approach and 81.05% of that obtained by the MINLP-
based approach, and the performance of the ILP-based approach is almost equivalent
to that of the MINLP-based approach.
S3I_NS2_16_020 : Energy-Efﬁcient Adaptive Forwarding Scheme for MANETs
Flooding is the simplest way of broadcasting, in which each node in the network
retransmits an incoming message once. Simple flooding technique in wireless Ad-hoc
networks causes the broadcast storm problem. However, this technique is inefficient
in terms of resource consumption such as bandwidth and energy. This paper presents a
new hybrid scheme that combines different techniques that collaborate to reduce
overhead and conserve energy. We propose an Energy-Efficient Adaptive Forwarding
Scheme, that utilizes the information of the 1-hop neighbouring radios. In this scheme
nodes do not need a positioning system or distance calculation to determine their
location. In addition to the previous works, the proposed protocol divides the network
into different groups based on their transmission-power levels. Therefore, the node
which receives HELLO message from different groups is considered a Gateway node.
This node efficiently participates in forwarding RREQ packets and the unnecessary
redundant retransmission is avoided. The performance evaluation of the proposed
protocol shows a reduction in the routing overhead and in energy consumption, when
compared with the Pure-Flooding AODV and Dynamic-Power AODV using NS2.

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S3I_NS2_16_021 : Trusted Secure Adhoc On-Demand Multipath Distance
Vector Routing in MANET
A mobile ad hoc network (MANET) is a collectionof wireless nodes, which works
well only if those mobile nodes aregood and behave cooperatively. The lack of
infrastructuresupport and resource constraint is the key issue that causesdishonest and
non-co-operative nodes. Therefore, MANET isvulnerable to serious attacks. To
reduce the hazards from suchnodes and enhance the security of the network, this
paperextends an Ad hoc On-Demand Multipath Distance Vector(AOMDV) Routing
protocol, named as Trust-based Secured Adhoc On-demand Multipath Distance
Vector (TS-AOMDV), whichis based on the nodes' routing behavior. The proposed
TSAOMDVaims at identifying and isolating the attacks such asflooding, black hole,
and gray hole attacks in MANET. With thehelp of Intrusion Detection System (IDS)
and trust-based routing, attack identification and isolation are carried out in two
phases ofrouting such as route discovery and data forwarding phase. IDSfacilitates
complete routing security by observing both controlpackets and data packets that are
involved in the routeidentification and the data forwarding phases. To improve
therouting performance, the IDS integrates the measured statisticsinto the AOMDV
routing protocol for the detection of attackers. This facilitates the TS-AOMDV to
provide better routingperformance and security in MANET. Finally, the Trust
basedSecured AOMDV, TS-AOMDV is compared with the existingAOMDV through
the NS2 based simulation model. Theperformance evaluation reveals that the
proposed TS-AOMDVimproves the performance in terms of throughput by
57.1%more than that of an AOMDV under adversary scenario. Thesimulated results
show that the TS-AOMDV outperforms theAOMDV routing protocol.
S3I_NS2_16_022 : QoS and Security in VOIP Networks through Admission
Control Mechanism
With the developing understanding of Information Security and digital assets, IT
technology has put on tremendous importance of network admission control (NAC).
In NAC architecture, admission decisions and resource reservations are taken at edge
devices, rather than resources or individual routers within the network. The NAC
architecture enables resilient resource reservation, maintaining reservations even after
failures and intra-domain rerouting. Admission Control Networks destiny is based on
IP networks through its Security and Quality of Service (QoS) demands for real time
multimedia application via advance resource reservation techniques. To achieve
Security & QoS demands, in real time performance networks, admission control
algorithm decides whether the new traffic flow can be admitted to the network or not.
Secure allocation of Peer for multimedia traffic flows with required performance is a
great challenge in resource reservation schemes. In this paper, we have proposed our

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model for VoIP networks in order to achieve security services along with QoS, where
admission control decisions are taken place at edge routers. We have analyzed and
argued that the measurement based admission control should be done at edge routers
which employs on-demand probing parallel from both edge routers to secure the
source and destination nodes respectively. In order to achieve Security and QoS for a
new call, we choose various probe packet sizes for voice and video calls respectively.
Similarly a technique is adopted to attain a security allocation approach for selecting
an admission control threshold by proposing our admission control algorithm. All
results are tested on NS2 based simulation to evalualate the network performance of
edge router based upon network admission control in VoIP traffic.
S3I_NS2_16_023 : An Energy Consumption Evaluation of Reactive and
Proactive Routing Protocols in Mobile Ad-hoc Network
In Mobile Ad-hoc NETwork (MANET) each node has the possibility to move freely
in the space and communicate with each other over wireless link without any
centralized controller or base station. These characteristics makes MANET useful and
practical in several fields like military scenarios, sensor networks, Rescue operations,
students on campus, etc. but this kind of network still suffers from a number of
problems, power consumption is one of the most crucial design concerns in Mobile
Ad-hoc networks as the nodes in MANET have battery limited. In this paper, we will
discuss about the aspect of energy consumption in MANET's routing protocols. A
performance comparison of four routing protocols Dynamic Source Routing (DSR),
Ad hoc On-Demand Distance Vector (AODV), Destination-Sequenced Distance
Vector (DSDV) and Optimized Link State Routing (OLSR) with respect to average
energy consumption are explained thoroughly. Then, an evaluation of how the varying
parameters of network in diverse scenarios affect the power consumption in these four
protocols is discussed. A detailed simulation model using Network Simulator 2 (NS2)
with different mobility and traffic models is used to study their energy consumption.
S3I_NS2_16_024 : Energy Detection Analytical Model for Handoff Process to
Support Mobile Cloud Computing Environment
Mobile devices play an integral role in our day lives and have brought the
revolutionary change in business, education, and entertainment. Moreover, the
emergence of cloud computing technology greatly extended the significance of smart
device. On the other hand, the smart devices experience the problem when obtaining
the multiple cloud services during the handoff process. In this paper, we propose an
energy detection (ED) analytical model for handoff process that calculates the energy
consumption for each handoff process in the cloud computing environment. Our ED
analytic model is developed to examine the consumed energy for different handoff
processes in cloud computing. The model helps the mobile users to get prior

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information for the status of the mobile when executing the handoff process. To
reconfirm the validity of ED analytical model, we have test programmed in NS2. The
results demonstrate that the ED analytical model efficiently detects the energy
consumption of mobile devices during the handoff process in cloud computing
environment.
S3I_NS2_16_025 : Nonsmooth Nonconvex Optimization for Low-Frequency
Geosounding Inversion
A study of the application of nonconvex regularization operators to the
electromagnetic sounding inverse problem is presented. A comparison is presented
among three nonconvex regularization algorithms: one smooth usually considered,
two nonsmooth, and a convex one, the total variation (TV) operator. One of the
nonsmooth nonconvex regularization methods is a novel implementation based on the
Legendre–Fenchel transform and the Bregman iterative algorithm. The nonconvex
regularization operator is approximated by the convex dual, and the minimization is
then implemented considering the equivalence between the Bregman iteration and the
augmented Lagrangian methods. The algorithm is simple and provides for better
models when applied to synthetic data, than those obtained with TV, and other
nonconvex smooth regularizers. Results of the application to field data are also
presented, observing that NS2 recovers a model in better agreement with the truth,
compared to those obtained with additional magnetometric resistivity data by other
researchers.
S3I_NS2_16_026 : Hierarchical Location-Based Services for Wireless Sensor
Networks
Nowadays Wireless Sensor Networks have attracted worldwide research and
industrial interest, because they can be applied in various areas. Geographic routing
protocols are very suitable to wireless sensor networks because they use location
information when they need to route packets. Obviously, location information is
maintained by Location-Based Services provided by network nodes in a distributed
way. The location based services can be classified into two classes: Flooding-Based
and Rendezvous-based location services.In this paper we choose to compare two
hierarchical rendezvous location based-services, GLS (Grid Location Service) and
HLS (Hierarchical Location Service) coupled to the GPSR routing protocol (Greedy
Perimeter Stateless Routing).The simulations were performed using NS2 simulator for
wireless sensor networks to evaluate the performance and power of the two services in
term of location overhead, the request travel time (RTT) and the query Success ratio
(QSR).This work presents also the scalability performance study of both GLS and
HLS, specifically, what happens if the number of nodes N increases. The study will

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focus on three qualitative metrics: The location maintenance cost,the location query
cost and the storage cost.
S3I_NS2_16_027 : Modified AODV Routing Protocol to Improve Security and
Performance against Black Hole Attack
A Mobile Ad hoc NETwork (MANET) is a collection of autonomous nodes that have
the ability to communicate with each other without having fixed infrastructure or
centralized access point such as a base station. This kind of networks is very
susceptible to adversary's malicious attacks, due to the dynamic changes of the
network topology, trusting the nodes to each other, lack of fixed substructure for the
analysis of nodes behaviors and constrained resources. One of these attacks is black
hole attack. In this attack, malicious nodes inject fault routing information to the
network and lead all data packets toward themselves, then destroy them all. In this
paper, we propose a solution, which enhances the security of the Ad-hoc On-demand
Distance Vector (AODV) routing protocol to encounter the black hole attacks. Our
solution avoids the black hole and the multiple black hole attacks. The simulation
results using the Network Simulator NS2 shows that our protocol provides better
security and better performance in terms of the packet delivery ratio than the AODV
routing protocol in the presence of one or multiple black hole attacks with marginal
rise in average end-to-end delay and normalized routing overhead.
S3I_NS2_16_028 : Attacks against AODV Routing Protocol in Mobile Ad-
HocNetworks
A Mobile Ad hoc NETwork (MANET) is much more vulnerable to attack than a
wired network due to the dynamic changes of the network topology, high mobility,
limited physical security and lack of centralized administration. Unfortunately, the
routing protocols are designed based on the assumption that all nodes trust each other
and cooperate without maliciously disrupting the operation of routing. This paper
analyzes the impact of security attacks on the performance of the AODV routing
protocol. Simulations are setup in the NS-2 network simulator and the performance of
the AODV routing protocol is discussed under black hole, flooding and rushing
attacks. This analysis is provided in terms of performance metrics, such as a packet
delivery ratio, the average end-to-end delay and normalized routing load.
S3I_NS2_16_029 : Novel Scheme to Heal MANET in Smart City Network
Today's generation has perceived wireless networking prospective applications in
tremendously erratic and vibrant surroundings. Businesses as well as individuals pick
wireless medium as their choice as it facilitates flexibility of location. It's obvious due
to its convenience in terms of mobility, portability or even ease of installation at any
preferred location. Mobile network has an intrinsic scalability restraint in terms of

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attainable network capability. The potential challenge of wireless communication in
Smart cities network is, the environs that these communications travel through is
changeable. So, the wireless networks which have ability to resolve their own
fragmented communication links will definitely enhance their pervasive recognition.
Due to the expansion of network capability changes are made to the network design
and infrastructures giving way to new techniques for system development for this type
of medium. Since it's the beginning there are initial hiccups, despite that, the modern
advances in self-healing wireless networks are good enough in resolving the problem.
The downlinks are repaired by using power conscious steady nodes. Authors have
proposed, a self-healing structure and mobile Power aware stable nodes for smart city
networks. Proposed design has been checked using NS2 simulator with existing
schemes and show good results in most of the cases.
S3I_NS2_16_030 : Flow Allocation for Maximum Throughput and Bounded
Delay on Multiple Disjoint Paths for Random Access Wireless Multihop
Networks
In this paper, we consider random access, wireless, multi-hop networks, with multi-
packet reception capabilities, where multiple flows are forwarded to the gateways
through node disjoint paths. We explore the issue of allocating flow on multiple paths,
exhibiting both intra- and inter-path interference, in order to maximize average
aggregate flow throughput (AAT) and also provide bounded packet delay. A
distributed flow allocation scheme is proposed where allocation of flow on paths is
formulated as an optimization problem. Through an illustrative topology it is shown
that the corresponding problem is non-convex. Furthermore, a simple, but accurate
model is employed for the average aggregate throughput achieved by all flows, that
captures both intra- and inter-path interference through the SINR model. The
proposed scheme is evaluated through Ns2 simulations of several random wireless
scenarios. Simulation results reveal that, the model employed, accurately captures the
AAT observed in the simulated scenarios, even when the assumption of saturated
queues is removed. Simulation results also show that the proposed scheme achieves
significantly higher AAT, for the vast majority of the wireless scenarios explored,
than the following flow allocation schemes: one that assigns flows on paths on a
round-robin fashion, one that optimally utilizes the best path only, and another one
that assigns the maximum possible flow on each path. Finally, a variant of the
proposed scheme is explored, where interference for each link is approximated by
considering its dominant interfering nodes only.
S3I_NS2_16_031 : Analysis and Comparison of EEEMR Protocol with the Flat
Routing Protocols of Wireless Sensor Networks

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Wireless Sensor Communication Networks have more concern on its routing
techniques. Since the WSCNs nodes are battery powered, routing algorithms should
assure the concept of energy saving without affecting the other performance metric
like Throughput, Delay and Packet delivery ratio etc. The modified AOMDV called as
Enhanced Energy Efficient Multipath Routing Protocol (EEEMRP) is a proposed
algorithm with the concept of crossbreed the AOMDV routing with the Cuckoo
Search algorithm. In this paper, we compare the QoS parameters such as Throughput,
Average Delay and Packet Delivery ratio with Traditional Flat Routing Protocols such
as DSR, DSDV and with AODV. Simulation is performed using NS2 and the results
show that the proposed EEEMRP routing protocol is better than DSR, DSDV and
AODV.
S3I_NS2_16_032 : P-LEACH: Energy Efficient Routing Protocol for Wireless
Sensor Networks
Wireless Sensor Network (WSN) are of paramount significance since they are
responsible for maintaining the routes in the network, data forwarding, and ensuring
reliable multi-hop communication. The main requirement of a wireless sensor
network is to prolong network energy efficiency and lifetime. Researchers have
developed protocols Low Energy Adaptive Clustering Hierarchy (LEACH) and
Power-Efficient Gathering in Sensor Information Systems (PEGASIS) for reducing
energy consumption in the network. However, the existing routing protocols
experience many shortcomings with respect to energy and power consumption.
LEACH features the dynamicity but has limitations due to its cluster-based
architecture, while PEGASIS overcomes the limitations of LEACH but lacks
dynamicity. In this paper, we introduce PEGASIS-LEACH (P-LEACH), a near
optimal cluster-based chain protocol that is an improvement over PEGASIS and
LEACH both. This protocol uses an energy-efficient routing algorithm to transfer the
data in WSN. To validate the energy effectiveness of P-LEACH, we simulate the
performance using Network Simulator (NS2) and MATLAB.
S3I_NS2_16_033 : A Novel Framework to Enhance the Performance of
Contention Based Synchronous MAC Protocol
In this paper, We propose a novel framework to improve the end-to-end transmission
delay (E2ETD) and packet delivery ratio (PDR) of existing contention based
synchronous MAC protocols designed for wireless sensor networks, without
increasing the duty cycle (DC). This is achieved by partitioning the n deployed sensor
nodes into k disjoint sets (DSs) which are of almost equal size. It then suitably
modifies the cycle structure followed by the existing contention based synchronous
MAC protocols by mapping the data transmission process of k existing cycles into
one restructured cycle. To evaluate the performance of this approach, we implement

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RMAC, PRMAC, and CLMAC protocols in the proposed framework using ns2.35
simulator. Results indicate that our scalable framework reduces the E2ETD and
increases the PDR significantly at the cost of a very small increase in average energy
consumption.
S3I_NS2_16_034 : Efﬁcient Wireless Multimedia Multicast in Multi-rate Multi-
channel Mesh Networks
Devices in wireless mesh networks can operate on multiple channels and
automatically adjust their transmission rates for the occupied channels. This paper
shows how to improve performance-guaranteed multicasting transmission coverage
for wireless multi-hop mesh networks by exploring the transmission opportunity
offered by multiple rates (MR) and multiple channels (MC). Based on the
characteristics of transmissions with different rates, we propose and analyze parallel
low-rate transmissions (PLT) and alternative rate transmissions (ART) to explore the
advantages of MRMC in improving the performance and coverage tradeoff under the
constraint of limited channel resources. We then apply these new transmission
schemes to improving the WMN multicast experience. Combined with the strategy of
reliable interference-controlled connections, a novel MRMC multicast algorithm (LC-
MRMC) is designed to make efficient use of channel and rate resources to greatly
extend wireless multicast coverage with high throughput and short delay performance.
Our NS2 simulation results prove that ART and LC-MRMC achieve improved
wireless transmission quality across much larger areas as compared to other related
studies.
S3I_NS2_16_035 - Optimization of Key Predistribution Protocol Based on
Supernetworks Theory in Heterogeneous WSN
This work develops an equilibrium model for finding the optimal distribution
strategy to maximize performance of key predistribution protocols in terms of cost,
resilience, connectivity, and lifetime. As an essential attribute of wireless sensor
networks, heterogeneity and its impacts on random key predistribution protocols are
first discussed. Using supernetworks theory, the optimal node deployment model is
proposed and illustrated. In order to find the equilibrium performance of our model,
all optimal performance functions are changed into variational inequalities so that this
optimization problem can be solved. A small-scale example is presented to illustrate
the applicability of our model.

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S3I_NS2_16_036 - Low-Cost Localization for Multihop Heterogeneous Wireless
Sensor Networks
In this paper, we propose a novel low-cost localization algorithm tailored for
multihop heterogeneous wireless sensor networks (HWSNs) where nodes'
transmission capabilities are different. This characteristic, if not taken into account
when designing the localization algorithm, may severely hinder its accuracy.
Assuming different nodes' transmission capabilities, we develop two different
approaches to derive the expected hop progress (EHP). Exploiting the latter, we
propose a localization algorithm that is able to accurately locate the sensor nodes
owing to a new low-cost implementation. Furthermore, we develop a correction
mechanism, which complies with the heterogeneous nature of wireless sensor
networks (WSNs) to further improve localization accuracy without incurring any
additional costs. Simulations results show that the proposed algorithm, whether
applied with or without correction, outperforms in accuracy the most representative
WSN localization algorithms.
S3I_NS2_16_037 - Auction-Based Data Gathering Scheme for Wireless Sensor
Networks
This letter proposes a novel data gathering scheme for wireless sensor networks
(WSNs) that limits the energy expenditure, and hence, prolongs network lifetime.
Data gathering is modeled as an auction where a node broadcasts its own result only if
it is higher than the maximum already-broadcasted result by other nodes. For a WSN
of 100 nodes, the mathematical and simulation results show that the proposed scheme
can save up to 70% of the energy consumption with <;1% performance loss,
compared with the conventional scheme.
S3I_NS2_16_038 - Cost-Aware Activity Scheduling for Compressive Sleeping
In this paper, we consider a compressive sleeping wireless sensor network
(WSN) for monitoring parameters in the sensor field, where only a fraction of sensor
nodes (SNs) are activated to perform the sensing task and their data are gathered at a
fusion center (FC) to estimate all the other SNs' data using the compressive sensing
(CS) principle. Typically, research published concerning CS implicitly assume the
sampling costs for all samples are equal and suggest random sampling as an
appropriate approach to achieve good reconstruction accuracy. However, this
assumption does not hold for compressive sleeping WSNs, which have significant
variability in sampling cost owing to the different physical conditions at particular

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SNs. To exploit this sampling cost nonuniformity, we propose a cost-aware activity
scheduling approach that minimizes the sampling cost with constraints on the
regularized mutual coherence of the equivalent sensing matrix. In addition, for the
case with prior information about the signal support, we extend the proposed approach
to incorporate the prior information by considering an additional constraint on the
mean square error (MSE) of the oracle estimator for sparse recovery. Our numerical
experiments demonstrate that, in comparison with other designs in the literature, the
proposed activity scheduling approaches lead to improved tradeoffs between
reconstruction accuracy and sampling cost for compressive sleeping WSNs.
S3I_NS2_16_039 - Wireless Sensor Network Simulation Frameworks: A Tutorial
Review: MATLAB/Simulink bests the rest
A Wireless Sensor Network (WSN) is a distributed set of sensors deployed to
work together for collective sensing and possible data processing. A WSN can be used
to monitor environmental behavior and structural integrity in a variety of application
fields, thus becoming an integral part of the consumer electronics of smart buildings
in smart cities. Due to ever-increasing population growth, along with limited natural
resources, smart cities are expected to be the wave of the future. For instance, WSNs
are widely used in industrial settings with machine monitoring and play an important
role in monitoring the structural integrity of large buildings and bridges. This article
focuses on existing WSN simulation frameworks that could be integrated with
realtime hardware prototypes. We analyze and compare various such simulation
frameworks, and we determine a suitable simulation environment that supports
specific software packages.
S3I_NS2_16_040 - The Impact of Incomplete Secure Connectivity on the
Lifetime of Wireless Sensor Networks
Key predistribution schemes accommodate secure connectivity by establishing
pairwise keys between nodes. However, ensuring security for all communication links
of a wireless sensor network (WSN) is nontrivial due to the memory limitations of the
nodes. If some of the links are not available due to the lack of a primary security
association between the transmitter and the receiver, nodes can still send their data to
the base station but probably not via the best route that maximizes the network
lifetime. In this study, we propose a linear programming framework to explore the
incomplete secure connectivity problem with respect to its impact on network
lifetime, path length, queue size, and energy dissipation. The numerical results show

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that if any two nodes share a key with a probability of at least 0.3, then we should
expect only a marginal drop (i.e., less than 3.0%) in lifetime as compared to a fully
connected network.
S3I_NS2_16_041 - Energy-Efficient Cooperative Relaying for Unmanned Aerial
Vehicles
Airborne relaying can extend wireless sensor networks (WSNs) to remote
human-unfriendly terrains. However, lossy airborne channels and limited battery of
unmanned aerial vehicles (UAVs) are critical issues, adversely affecting success rate
and network lifetime, especially in real-time applications. We propose an energy-
efficient cooperative relaying scheme which extends network lifetime while
guaranteeing the success rate. The optimal transmission schedule of the UAVs is
formulated to minimize the maximum (min-max) energy consumption under
guaranteed bit error rates, and can be judiciously reformulated and solved using
standard optimisation techniques. We also propose a computationally efficient
suboptimal algorithm to reduce the scheduling complexity, where energy balancing
and rate adaptation are decoupled and carried out in a recursive alternating manner.
Simulation results confirm that the suboptimal algorithm cuts off the complexity by
orders of magnitude with marginal loss of the optimal network yield (throughput) and
lifetime. The proposed suboptimal algorithm can also save energy by 50 percent,
increase network yield by 15 percent, and extend network lifetime by 33 percent,
compared to the prior art.
S3I_NS2_16_042 - A Fuzzy Logic-Based Clustering Algorithm for WSN to
Extend the Network Lifetime
Wireless sensor network (WSN) brings a new paradigm of real-time embedded
systems with limited computation, communication, memory, and energy resources
that are being used for huge range of applications where the traditional infrastructure-
based network is mostly infeasible. The sensor nodes are densely deployed in a hostile
environment to monitor, detect, and analyze the physical phenomenon and consume
considerable amount of energy while transmitting the information. It is impractical
and sometimes impossible to replace the battery and to maintain longer network life
time. So, there is a limitation on the lifetime of the battery power and energy
conservation is a challenging issue. Appropriate cluster head (CH) election is one
such issue, which can reduce the energy consumption dramatically. Low energy
adaptive clustering hierarchy (LEACH) is the most famous hierarchical routing
protocol, where the CH is elected in rotation basis based on a probabilistic threshold
value and only CHs are allowed to send the information to the base station (BS). But

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in this approach, a super-CH (SCH) is elected among the CHs who can only send the
information to the mobile BS by choosing suitable fuzzy descriptors, such as
remaining battery power, mobility of BS, and centrality of the clusters. Fuzzy
inference engine (Mamdani's rule) is used to elect the chance to be the SCH. The
results have been derived from NS-2 simulator and show that the proposed protocol
performs better than the LEACH protocol in terms of the first node dies, half node
alive, better stability, and better lifetime.
S3I_NS2_16_043 - Energy profiling in practical sensor networks: Identifying
hidden consumers
Reducing energy consumption of wireless sensor nodes extends battery life and
/ or enables the use of energy harvesting and thus makes feasible many applications
that might otherwise be impossible, too costly or require constant maintenance.
However, theoretical approaches proposed to date that minimise WSN energy needs
generally lead to less than expected savings in practice. We examine experiences of
tuning the energy profile for two near-production wireless sensor systems and
demonstrate the need for (a) microbenchmark-based energy consumption profiling,
(b) examining start-up costs, and (c) monitoring the nodes during long-term
deployments. The tuning exercise resulted in reductions in energy consumption of a)
93% for a multihop Telos-based system (average power 0.029 mW) b) 94.7% for a
single hop Ti- 8051-based system during startup, and c) 39% for a Ti- 8051 system
post start-up. The work reported shows that reducing the energy consumption of a
node requires a whole system view, not just measurement of a “typical” sensing cycle.
We give both generic lessons and specific application examples that provide guidance
for practical WSN design and deployment.
S3I_NS2_16_044 - Intercept Behavior Analysis of Industrial Wireless Sensor
Networks in the Presence of Eavesdropping Attack
This paper studies the intercept behavior of an industrial wireless sensor
network (WSN) consisting of a sink node and multiple sensors in the presence of an
eavesdropping attacker, where the sensors transmit their sensed information to the
sink node through wireless links. Due to the broadcast nature of radio wave
propagation, the wireless transmission from the sensors to the sink can be readily
overheard by the eavesdropper for interception purposes. In an information-theoretic
sense, the secrecy capacity of the wireless transmission is the difference between the
channel capacity of the main link (from sensor to sink) and that of the wiretap link

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(from sensor to eavesdropper). If the secrecy capacity becomes nonpositive due to the
wireless fading effect, the sensor's data transmission could be successfully intercepted
by the eavesdropper and an intercept event occurs in this case. However, in industrial
environments, the presence of machinery obstacles, metallic frictions, and engine
vibrations makes the wireless fading fluctuate drastically, resulting in the degradation
of the secrecy capacity. As a consequence, an optimal sensor scheduling scheme is
proposed in this paper to protect the legitimate wireless transmission against the
eavesdropping attack, where a sensor with the highest secrecy capacity is scheduled to
transmit its sensed information to the sink. Closed-form expressions of the probability
of occurrence of an intercept event (called intercept probability) are derived for the
conventional round-robin scheduling and the proposed optimal scheduling schemes.
Also, an asymptotic intercept probability analysis is conducted to provide an insight
into the impact of the sensor scheduling on the wireless security. Numerical results
demonstrate that the proposed sensor scheduling scheme outperforms the
conventional round-robin scheduling in terms of the intercept probability.
S3I_NS2_16_045 - Distributed k-Means Algorithm and Fuzzy c-Means
Algorithm for Sensor Networks Based on Multiagent
This paper is concerned with developing a distributed k-means algorithm and a
distributed fuzzy c-means algorithm for wireless sensor networks (WSNs) where each
node is equipped with sensors. The underlying topology of the WSN is supposed to be
strongly connected. The consensus algorithm in multiagent consensus theory is
utilized to exchange the measurement information of the sensors in WSN. To obtain a
faster convergence speed as well as a higher possibility of having the global optimum,
a distributed k-means++ algorithm is first proposed to find the initial centroids before
executing the distributed k-means algorithm and the distributed fuzzy c-means
algorithm. The proposed distributed k-means algorithm is capable of partitioning the
data observed by the nodes into measure-dependent groups which have small in-group
and large out-group distances, while the proposed distributed fuzzy c-means algorithm
is capable of partitioning the data observed by the nodes into different measure-
dependent groups with degrees of membership values ranging from 0 to 1. Simulation
results show that the proposed distributed algorithms can achieve almost the same
results as that given by the centralized clustering algorithms.
S3I_NS2_16_046 - Game-Theoretic Multi-Channel Multi-Access in Energy
Harvesting Wireless Sensor Networks
Energy harvesting (EH) has been proposed as a promising technology to extend
the lifetime of wireless sensor networks (WSNs) by continuously harvesting

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green/renewable energy. However, the intermittent and random EH process as well as
the complexity in achieving global network information call for efficient energy
management and distributed resource optimization. Considering the complex
interactions among individual sensors, we use the game theory to perform distributed
optimization for the general multi-channel multi-access problem in an EH-WSN,
where strict delay constraints are imposed for the data transmission. Sensors'
competition for channel access is formulated as a non-cooperative game, which is
proved to be an ordinal potential game that has at least one Nash equilibrium (NE).
Furthermore, all the NE of the game is proved to be Pareto optimal, and Jain's fairness
index bound of the NE is theoretically derived. Finally, we design a fully distributed,
online learning algorithm for the multi-channel multi-access in the EH-WSN, which is
proved to converge to the NE of the formulated game. Simulation results validate the
effectiveness of the proposed algorithm.
S3I_NS2_16_047 - Non-Parametric and Semi-Parametric RSSI/Distance
Modeling for Target Tracking in Wireless Sensor Networks
This paper introduces two main contributions to the wireless sensor network
(WSN) society. The first one consists of modeling the relationship between the
distances separating sensors and the received signal strength indicators (RSSIs)
exchanged by these sensors in an indoor WSN. In this context, two models are
determined using a radio-fingerprints database and kernel-based learning methods.
The first one is a non-parametric regression model, while the second one is a semi-
parametric regression model that combines the well-known log-distance theoretical
propagation model with a non-linear fluctuation term. As for the second contribution,
it consists of tracking a moving target in the network using the estimated
RSSI/distance models. The target's position is estimated by combining acceleration
information and the estimated distances separating the target from sensors having
known positions, using either the Kalman filter or the particle filter. A fully
comprehensive study of the choice of parameters of the proposed distance models and
their performances is provided, as well as a study of the performance of the two
proposed tracking methods. Comparisons with recently proposed methods are also
provided.
S3I_NS2_16_048 - SRA: A Sensing Radius Adaptation Mechanism for
Maximizing Network Lifetime in WSNs
Coverage is an important issue that has been widely discussed in wireless
sensor networks (WSNs). However, it is still a big challenge to achieve both purposes
of full coverage and energy balance. This paper considers the area coverage problem

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for a WSN where each sensor has variable sensing radius. To prolong the network
lifetime, a weighted Voronoi diagram (WVD) is proposed as a tool for determining
the responsible sensing region of each sensor according to the remaining energy in a
distributed manner. The proposed mechanism, called SRA, mainly consists of three
phases. In the first phase, each sensor and its neighboring nodes cooperatively
construct the WVD for identifying the responsible monitoring area. In the second
phase, each sensor adjusts its sensing radius to reduce the overlapping sensing region
such that the purpose of energy conservation can be achieved. In the last phase, the
sensor with the least remaining energy further adjusts its sensing radius with its
neighbor for maximizing the network lifetime. Performance evaluation and analysis
reveal that the proposed SRA mechanism outperforms the existing studies in terms of
the network lifetime and the degree of energy balance.
S3I_NS2_16_049 - Neighbor-Aided Spatial-Temporal Compressive Data
Gathering in Wireless Sensor Networks
The integration between data collection methods in wireless sensor networks
(WSNs) and compressive sensing (CS) provides energy efficient paradigms. Single-
dimensional CS approaches are inapplicable in spatial and temporal correlated WSNs
while the Kronecker compressive sensing (KCS) model suffers performance
degradation along with the increasing data dimensions. In this letter, a neighbor-aided
compressive sensing (NACS) scheme is proposed for efficient data gathering in
spatial and temporal correlated WSNs. During every sensing period, the sensor node
just sends the raw readings within the sensing period to a randomly and uniquely
selected neighbor. Then, the CS measurements created by the neighbor are sent to the
sink node directly. The equivalent sensing matrix is proved to satisfy both structured
random matrix (SRM) and generalized KCS models. And, by introducing the idea of
SRM to KCS, the recovery performance of KCS is significantly improved. Simulation
results demonstrate that compared with the conventional KCS models, the proposed
NACS model can achieve vastly superior recovery performance and receptions with
much fewer transmissions.
S3I_NS2_16_050 - Charge Redistribution-Aware Power Management for
Supercapacitor-Operated Wireless Sensor Networks
Supercapacitors (SCs) have been used in energy harvesting wireless sensor
networks (WSNs) to relieve the life cycle limitations that most traditional
rechargeable storage devices suffer from. SC-operated WSNs present new challenges
for power management due to its high self-discharge and charge redistribution. Power
management algorithms have been developed to reduce self-discharge loss, but few

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studies have focused on charge redistribution loss in SC-operated WSNs. In this
paper, we investigate how SC charge redistribution affects power management in
long-term WSN applications, and develop a practical power manager to reduce
redistribution loss by scheduling the workload in a way that maintains a relatively
balanced voltage between the main branch and the delayed branch of an SC. The
manager has low computational complexity and yields considerably smaller charge
redistribution loss than other power managers.
S3I_NS2_16_051 - Distributed Sequential Location Estimation of a Gas Source
via Convex Combination in WSNs
Localization of the hazardous gas source plays an important role in the
protection of public security, since it can save a lot of time for subsequent rescue
works. For gas source localization (GSL), a large number of gas sensor nodes can be
rapidly deployed to construct a wireless sensor network (WSN) and cover the whole
concerned area. Although least-squares (LS) methods can solve the problem of GSL
in WSNs regardless of the distribution of measurement noises, centralized LS
methods are not power efficient and robust since they require the gathering and
processing of large-scale measurements on a central node. In this paper, we propose a
novel distributed method for GSL in WSNs, which is performed on a sequence of
sensor nodes successively. Each sensor node in the sequence conducts an individual
estimation and a convex combination. The individual estimation is inspired by the LS
formulation of the problem of GSL in WSNs. The proposed method is fully
distributed and computationally efficient, and it does not rely on the absolute location
of the sensor nodes. Extensive simulation results and a set of experimental results
demonstrate that the success rate and localization accuracy of the proposed method
are generally higher than those of the trust-region-reflective method.
S3I_NS2_16_052 - Self-Sustainable Communications With RF Energy
Harvesting: Ginibre Point Process Modeling and Analysis
RF-enabled wireless power transfer and energy harvesting has recently
emerged as a promising technique to provision perpetual energy replenishment for
low-power wireless networks. The network devices are replenished by the RF energy
harvested from the transmission of ambient RF transmitters, which offers a practical
and promising solution to enable self-sustainable communications. This paper adopts
a stochastic geometry framework based on the Ginibre model to analyze the
performance of self-sustainable communications over cellular networks with general
fading channels. Specifically, we consider the point-to-point downlink transmission
between an access point and a battery-free device in the cellular networks, where the

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ambient RF transmitters are randomly distributed following a repulsive point process,
called Ginibre α-determinantal point process (DPP). Two practical RF energy
harvesting receiver architectures, namely time-switching and power-splitting, are
investigated. We perform an analytical study on the RF-powered device and derive
the expectation of the RF energy harvesting rate, the energy outage probability and the
transmission outage probability over Nakagami-m fading channels. These are
expressed in terms of so-called Fredholm determinants, which we compute efficiently
with modern techniques from numerical analysis. Our analytical results are
corroborated by the numerical simulations, and the efficiency of our approximations is
demonstrated. In practice, the accurate simulation of any of the Fredholm determinant
appearing in the manuscript is a matter of seconds. An interesting finding is that a
smaller value of α (corresponding to larger repulsion) yields a better transmission
outage performance when the density of the ambient RF transmitters is small.
However, it yields a lower transmission outage probability when the density of the
ambient RF transmitters is large. We also show analytically that the power-splitting
architecture outperfor- s the time-switching architecture in terms of transmission
outage performances. Lastly, our analysis provides guidelines for setting the time-
switching and power-splitting coefficients at their optimal values.
S3I_NS2_16_053 - Allocation of Heterogeneous Resources of an IoT Device to
Flexible Services
Internet of Things (IoT) devices can be equipped with multiple heterogeneous
network interfaces. An overwhelmingly large amount of services may demand some
or all of these interfaces’ available resources. Herein, we present a precise
mathematical formulation of assigning services to interfaces with heterogeneous
resources in one or more rounds. For reasonable instance sizes, the presented
formulation produces optimal solutions for this computationally hard problem. We
prove the NP-Completeness of the problem and develop two algorithms to
approximate the optimal solution for big instance sizes. The first algorithm allocates
the most demanding service requirements first, considering the average cost of
interfaces resources. The second one calculates the demanding resource shares and
allocates the most demanding of them first by choosing randomly among equally
demanding shares. Finally, we provide simulation results giving insight into services
splitting over different interfaces for both cases.
S3I_NS2_16_054 - Mobile Demand Profiling for Cellular Cognitive Networking
In the next few years, mobile networks will undergo significant evolutions in
order to accommodate the ever-growing load generated by increasingly pervasive

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smartphones and connected objects. Among those evolutions, cognitive networking
upholds a more dynamic management of network resources that adapts to the
significant spatiotemporal fluctuations of the mobile demand. Cognitive networking
techniques root in the capability of mining large amounts of mobile traffic data
collected in the network, so as to understand the current resource utilization in an
automated manner. In this paper, we take a first step towards cellular cognitive
networks by proposing a framework that analyzes mobile operator data, builds
profiles of the typical demand, and identifies unusual situations in network-wide
usages. We evaluate our framework on two real-world mobile traffic datasets, and
show how it extracts from these a limited number of meaningful mobile demand
profiles. In addition, the proposed framework singles out a large number of outlying
behaviors in both case studies, which are mapped to social events or technical issues
in the network.
S3I_NS2_16_055 - Enhanced Indoor Location Tracking Through Body
Shadowing Compensation
This paper presents a radio frequency (RF)-based location tracking system that
improves its performance by eliminating the shadowing caused by the human body of
the user being tracked. The presence of such a user will influence the RF signal paths
between a body-worn node and the receiving nodes. This influence will vary with the
user's location and orientation and, as a result, will deteriorate the performance
regarding location tracking. By using multiple mobile nodes, placed on different parts
of a human body, we exploit the fact that the combination of multiple measured signal
strengths will show less variation caused by the user's body. Another method is to
compensate explicitly for the influence of the body by using the user's orientation
toward the fixed infrastructure nodes. Both approaches can be independently
combined and reduce the influence caused by body shadowing, hereby improving the
tracking accuracy. The overall system performance is extensively verified on a
building-wide testbed for sensor experiments. The results show a significant
improvement in tracking accuracy. The total improvement in mean accuracy is 38.1%
when using three mobile nodes instead of one and simultaneously compensating for
the user's orientation.
S3I_NS2_16_056 - Efficient and Privacy-preserving Polygons Spatial Query
Framework for Location-based Services
With the pervasiveness of mobile devices and the development of wireless
communication technique, location-based services (LBS) have made our life more
convenient, and the polygons spatial query, which can provide more flexible LBS, has

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attracted considerable interest recently. However, the flourish of polygons spatial
query still faces many challenges including the query information privacy. In this
paper, we present an efficient and privacy-preserving polygons spatial query
framework for location-based services, called Polaris. With Polaris, the LBS provider
outsources the encrypted LBS data to cloud server, and the registered user can query
any polygon range to get accurate LBS results without divulging his/her query
information to the LBS provider and cloud server. Specifically, an efficient special
polygons spatial query algorithm (SPSQ) over ciphertext is constructed, based on an
improved homomorphic encryption technology over composite order group. With
SPSQ, Polaris can search outsourced encrypted LBS data in cloud server by the
encrypted request, and respond the encrypted polygons spatial query results
accurately. Detailed security analysis shows that the proposed Polaris can resist
various known security threats. In addition, performance evaluations via
implementing Polaris on smartphone and workstation with real LBS dataset
demonstrate Polaris’ effectiveness in term of real environment.
S3I_NS2_16_057 - A General Privacy-Preserving Auction Mechanism for
Secondary Spectrum Markets
Auctions are among the best-known market-based tools to solve the problem of
dynamic spectrum redistribution. In recent years, a good number of strategy-proof
auction mechanisms have been proposed to improve spectrum utilization and to
prevent market manipulation. However, the issue of privacy preservation in spectrum
auctions remains open. On the one hand, truthful bidding reveals bidders' private
valuations of the spectrum. On the other hand, coverage/interference areas of the
bidders may be revealed to determine conflicts. In this paper, we present PISA, which
is a PrIvacy preserving and Strategy-proof Auction mechanism for spectrum
allocation. PISA provides protection for both bid privacy and coverage/interference
area privacy leveraging a privacy-preserving integer comparison protocol, which is
well applicable in other contexts. We not only theoretically prove the privacy-
preserving properties of PISA, but also extensively evaluate its performance.
Evaluation results show that PISA achieves good spectrum allocation efficiency with
light computation and communication overheads.
S3I_NS2_16_058 - Optimized In-Band Full-Duplex MIMO Relay Under Single-
Stream Transmission
This paper presents a coherent scheme to optimize an in-band full-duplex
multiple-input-multiple-output (MIMO) relay via beamforming and transmit power
allocation in a two-hop single-input-single-output (SISO) link under full channel

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knowledge and perfect hardware assumptions. First, we derive in closed form the
optimal pair of transmit power and receive filter for a fixed transmit filter by unifying
the minimum-mean-square-error (MMSE) filtering with the SISO-equivalent power
allocation, as an iterative approach is not guaranteed to converge to global optimum.
Second, we propose a heuristic algorithm to approximate the optimal transmit filter
for a fixed receive filter. Furthermore, we study the well-known null-space projection
constraint and derive a singular value decomposition (SVD)-based solution for the
arbitrary-rank self-interference channel by generalizing the optimal solution under the
assumption of rank-1 self-interference channel. Finally, we combine these solutions
into a partially iterative algorithm in order to address the global optimization as our
observations justify that some of the aforementioned schemes converge to the optimal
solution under certain criteria. The numerical analysis of the proposed iterative
algorithm demonstrates close-to-optimal performance relative to the theoretical upper
bound of the end-to-end link in terms of maximum achievable throughput.
S3I_NS2_16_059 - The Extra Connectivity, Extra Conditional Diagnosability,
and t/m -Diagnosability of Arrangement Graphs
Extra connectivity is an important indicator of the robustness of a
multiprocessor system in presence of failing processors. The g -extra conditional
diagnosability and the t/m -diagnosability are two important diagnostic strategies at
system-level that can significantly enhance the system’s self-diagnosing capability.
The g -extra conditional diagnosability is defined under the assumption that every
component of the system removing a set of faulty vertices has more than g vertices.
The t/m -diagnosis strategy can detect up to t faulty processors which might include
at most m misdiagnosed processors, where m is typically a small integer number. In
this paper, we analyze the combinatorial properties and fault tolerant ability for an
(n,k) -arrangement graph, denoted by A_{n,k} , a well-known interconnection
network proposed for multiprocessor systems. We first establish that the A_{n,k} ’s
one-extra connectivity is (2k-1)break (n-k)-1 ( kge 3 , nge k+2 ), two-extra
connectivity is (3k-2)(n-k)-3 ( kge 4 , nge k+2 ), and three-extra connectivity is
(4k-4)(n-k)-4 ( kge 4 , nge k+2 or kge 3 , nge k+3 ), respectively. And then, we
address the g -extra conditional diagnosability of A_{n,k} under the PMC model for
- formula> 1!le! g le 3 . Finally, we determine that the (n,k) -arrangement graph
A_{n,k} is [(2k-1)(n-k)-1]/1 -diagnosable ( kge 4 , nge k+2 ), [(3k-2)(n-k)-3]/2 -
diagnosable ( kge 4 , nge k+2 ), and [(4k-4)(n-k)-4]/3 -diagnosable ( kge 4 , nge
k+3 ) under the PMC model, respectively.
S3I_NS2_16_060 - A Cloud-Based Architecture for the Internet of Spectrum
Devices Over Future Wireless Networks

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The dramatic increase in data rates in wireless networks has caused radio
spectrum usage to be an essential and critical issue. Spectrum sharing is widely
recognized as an affordable, near-term method to address this issue. This paper first
characterizes the new features of spectrum sharing in future wireless networks,
including heterogeneity in sharing bands, diversity in sharing patterns, crowd
intelligence in sharing devices, and hyperdensification in sharing networks. Then, to
harness the benefits of these unique features and promote a vision of spectrum without
bounds and networks without borders, this paper introduces a new concept of the
Internet of spectrum devices (IoSDs) and develops a cloud-based architecture for
IoSD over future wireless networks, with the prime aim of building a bridging
network among various spectrum monitoring devices and massive spectrum
utilization devices, and enabling a highly efficient spectrum sharing and management
paradigm for future wireless networks. Furthermore, this paper presents a systematic
tutorial on the key enabling techniques of the IoSD, including big spectrum data
analytics, hierarchal spectrum resource optimization, and quality of experience-
oriented spectrum service evaluation. In addition, the unresolved research issues are
also presented.
S3I_NS2_16_061 - Optimality of Fast Matching Algorithms for Random
Networks with Applications to Structural Controllability
Network control refers to a very large and diverse set of problems including
controllability of linear time-invariant dynamical systems, where the objective is to
select an appropriate input to steer the network to a desired state. There are many
notions of controllability, one of them being structural controllability, which is
intimately connected to finding maximum matchings on the underlying network
topology. In this work, we study fast, scalable algorithms for finding maximum
matchings for a large class of random networks. First, we illustrate that degree
distribution random networks are realistic models for real networks in terms of
structural controllability. Subsequently, we analyze a popular, fast and practical
heuristic due to Karp and Sipser as well as a simplification of it. For both heuristics,
we establish asymptotic optimality and provide results concerning the asymptotic size
of maximum matchings for an extensive class of random networks.
S3I_NS2_16_062 - Cooperative Data Scheduling in Hybrid Vehicular Ad Hoc
Networks: VANET as a Software Defined Network
This paper presents the first study on scheduling for cooperative data
dissemination in a hybrid infrastructure-to-vehicle (I2V) and vehicle-to-vehicle (V2V)
communication environment. We formulate the novel problem of cooperative data

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scheduling (CDS). Each vehicle informs the road-side unit (RSU) the list of its current
neighboring vehicles and the identifiers of the retrieved and newly requested data. The
RSU then selects sender and receiver vehicles and corresponding data for V2V
communication, while it simultaneously broadcasts a data item to vehicles that are
instructed to tune into the I2V channel. The goal is to maximize the number of
vehicles that retrieve their requested data. We prove that CDS is NP-hard by
constructing a polynomial-time reduction from the Maximum Weighted Independent
Set (MWIS) problem. Scheduling decisions are made by transforming CDS to MWIS
and using a greedy method to approximately solve MWIS. We build a simulation
model based on realistic traffic and communication characteristics and demonstrate
the superiority and scalability of the proposed solution. The proposed model and
solution, which are based on the centralized scheduler at the RSU, represent the first
known vehicular ad hoc network (VANET) implementation of software defined
network (SDN) concept.
S3I_NS2_16_063 - Doherty Power Amplifier With Extended Bandwidth and
Improved Linearizability Under Carrier-Aggregated Signal Stimuli
This letter proposes a novel output combining network devised to extend a
Doherty power amplifier's (DPA's) radio frequency bandwidth (RFBW), while
maintaining the proper load modulation, and to ensure its linearizability when driven
with intra- and inter-band carrier aggregated (CA) communication signals. Based on
the proposed topology, a wideband 20-W DPA was designed and implemented using
packaged GaN transistors. Drain efficiency in excess of 48% was recorded at 6 dB
back-off over the frequency range of 1.72-2.27 GHz. The DPA prototype was
successfully linearized when driven with wideband (up to 160 MHz instantaneous
bandwidth) and dual-band (up to 300 MHz carrier separation) modulated stimuli and
achieved an average drain efficiency in excess of 45%.
S3I_NS2_16_064 - Beamforming OFDM Performance Under Joint Phase Noise
and I/Q Imbalance
Phase noise (PHN) and in-phase/quadrature (I/Q) imbalance are two major
radio-frequency (RF) impairments in direct-conversion wireless transceivers.
Beamforming and orthogonal frequency-division multiplexing (OFDM) schemes have
been adopted in broadband wireless standards due to their significant performance
gains. In this paper, we analyze the impact of joint I/Q imbalance and PHN on
beamforming OFDM direct-conversion transceivers. We derive an exact normalized-
mean-square-error expression (NMSE) and examine several special and asymptotic
cases to gain insights. One such insight is that, asymptotically, for both large signal-

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to-noise ratio (SNR) and the beamforming array size, the PHN and I/Q imbalance
effects become decoupled.
S3I_NS2_16_065 - Review of Active and Reactive Power Sharing Strategies in
Hierarchical Controlled Microgrids
Microgrids consist of multiple parallel-connected distributed generation (DG)
units with coordinated control strategies, which are able to operate in both grid-
connected and islanded mode. Microgrids are attracting more and more attention since
they can alleviate the stress of main transmission systems, reduce feeder losses, and
improve system power quality. When the islanded microgrids are concerned, it is
important to maintain system stability and achieve load power sharing among the
multiple parallel-connected DG units. However, the poor active and reactive power
sharing problems due to the influence of impedance mismatch of the DG feeders and
the different ratings of the DG units are inevitable when the conventional droop
control scheme is adopted. Therefore, the adaptive/improved droop control, network-
based control methods and cost-based droop schemes are compared and summarized
in this paper for active power sharing. Moreover, nonlinear and unbalanced loads
could further affect the reactive power sharing when regulating the active power, and
it is difficult to share the reactive power accurately only by using the enhanced virtual
impedance method. Therefore, the hierarchical control strategies are utilized as
supplements of the conventional droop controls and virtual impedance methods. The
improved hierarchical control approaches such as the algorithms based on graph
theory, multi-agent system, the gain scheduling method and predictive control have
been proposed to achieve proper reactive power sharing for islanded microgrids and
eliminate the effect of the communication delays on hierarchical control. Finally, the
future research trends on islanded microgrids are also discussed in this paper.
S3I_NS2_16_066 - Network Selection and Channel Allocation for Spectrum
Sharing in 5G Heterogeneous Networks
The demand for spectrum resources has increased dramatically with the advent
of modern wireless applications. Spectrum sharing, considered as a critical
mechanism for 5G networks, is envisioned to address spectrum scarcity issue and
achieve high data rate access, and guaranteed the quality of service (QoS). From the
licensed network's perspective, the interference caused by all secondary users (SUs)
should be minimized. From secondary networks point of view, there is a need to
assign networks to SUs in such a way that overall interference is reduced, enabling the
accommodation of a growing number of SUs. This paper presents a network selection
and channel allocation mechanism in order to increase revenue by accommodating

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more SUs and catering to their preferences, while at the same time, respecting the
primary network operator's policies. An optimization problem is formulated in order
to minimize accumulated interference incurred to licensed users and the amount that
SUs have to pay for using the primary network. The aim is to provide SUs with a
specific QoS at a lower price, subject to the interference constraints of each available
network with idle channels. Particle swarm optimization and a modified version of the
genetic algorithm are used to solve the optimization problem. Finally, this paper is
supported by extensive simulation results that illustrate the effectiveness of the
proposed methods in finding a near-optimal solution.
S3I_NS2_16_067 - Stability Challenges and Enhancements for Vehicular
Channel Congestion Control Approaches
Channel congestion is one of the major challenges for IEEE 802.11p-based
vehicular networks. Unless controlled, congestion increases with vehicle density,
leading to high packet loss and degraded safety application performance. We study
two classes of congestion control algorithms, i.e., reactive state-based and linear
adaptive. In this paper, the reactive state-based approach is represented by the
decentralized congestion control framework defined in the European
Telecommunications Standards Institute. The linear adaptive approach is represented
by the LInear MEssage Rate Integrated Control (LIMERIC) algorithm. Both
approaches control safety message transmissions as a function of channel load [i.e.,
channel busy percentage (CBP)]. A reactive state-based approach uses CBP directly,
defining an appropriate transmission behavior for each CBP value, e.g., via a table
lookup. By contrast, a linear adaptive approach identifies the transmission behavior
that drives CBP toward a target channel load. Little is known about the relative
performance of these approaches and any existing comparison is limited by
incomplete implementations or stability anomalies. To address this, this paper makes
three main contributions. First, we study and compare the two aforementioned
approaches in terms of channel stability and show that the reactive state-based
approach can be subject to major oscillation. Second, we identify the root causes and
introduce stable reactive algorithms. Finally, we compare the performance of the
stable reactive approach with the linear adaptive approach and the legacy IEEE
802.11p. It is shown that the linear adaptive approach still achieves a higher message
throughput for any given vehicle density for the defined performance metrics.
S3I_NS2_16_068 - Performance Modeling and Analysis of the IEEE 802.11p
EDCA Mechanism for VANET

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This paper studies performance modeling of the IEEE 802.11p enhanced
distributed channel access (EDCA) mechanism and develops performance models to
analyze the access performance of the IEEE 802.11p EDCA mechanism. A 2-D
Markov chain is first constructed to model the backoff procedure of an access
category (AC) queue and establish a relationship between the transmission probability
and collision probability of the AC queue. Then, a 1-D discrete-time Markov chain is
constructed to model the contention period of an AC queue and establish another
relationship between the transmission probability and collision probability of the AC
queue. Unlike most existing work, the 1-D Markov chain is extended to be infinite in
modeling the contention period of an AC queue under both saturated and nonsaturated
conditions. The two Markov models take into account all major factors that could
affect the access performance of the IEEE 802.11p EDCA mechanism, including the
saturation condition, standard parameters, backoff counter freezing, and internal
collision. Based on the two Markov models, performance models are further derived
to describe the relationships between the parameters of an AC queue and the access
performance of the AC queue in terms of the transmission probability, collision
probability, normalized throughput, and average access delay, respectively. The
effectiveness of the performance models are verified through simulation results.
S3I_NS2_16_069 - Non-Intrusive Planning the Roadside Infrastructure for
Vehicular Networks
In this article, we describe a strategy for planning the roadside infrastructure for
vehicular networks based on the global behavior of drivers. Instead of relying on the
trajectories of all vehicles, our proposal relies on the migration ratios of vehicles
between urban regions in order to infer the better locations for deploying the roadside
units. By relying on the global behavior of drivers, our strategy does not incur in
privacy concerns. Given a set of α available roadside units, our goal is to select those
α-better locations for placing the roadside units in order to maximize the number of
distinct vehicles experiencing at least one V2I contact opportunity. Our results
demonstrate that full knowledge of the vehicle trajectories are not mandatory for
achieving a close-to-optimal deployment performance when we intend to maximize
the number of distinct vehicles experiencing (at least one) V2I contact opportunities.
S3I_NS2_16_070 - Enhanced power management scheme for embedded road
side units
In this study, a green vehicular ad-hoc network (VANET) infrastructure is
suggested. The main players in such an infrastructure are the road side units (RSUs)
which are able to harvest the energy needed for their work from the surrounding

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environment, especially the solar energy. Such a suggestion permits to install the
RSUs in any place without considering the power supply availability and hence, an
extensive area is covered by the VANET infrastructure with an improved
performance. To achieve this goal, a new distributed power management scheme
called duty cycle estimation-event driven duty cycling is suggested and installed
locally in the RSUs in order to decrease their power consumption and to extend the
lifetime of their batteries. Embedded UBICOM IP2022 network processer platform is
adopted to implement the proposed RSU and the detailed design steps are described,
while the necessary values of the system components such as the number of solar cell
panels, battery cells capacity and so on, are tuned to suit the design goals. The
suggested method is compared with other duty cycling methods to show its
effectiveness to build a green VANET infrastructure.
S3I_NS2_16_071 - Communication Scheduling and Control of a Platoon of
Vehicles in VANETs
This paper is concerned with the problem of vehicular platoon control in
vehicular ad hoc networks subject to capacity limitation and random packet dropouts.
By introducing binary sequences as the basis of network access scheduling and
modeling random packet dropouts as independent Bernoulli processes, we derive a
closed-form methodology for vehicular platoon control. In particular, an interesting
framework for network access scheduling and platoon control codesign is established
based on a set of priority rules for network access control. The resulting platoon
control and scheduling algorithm can resolve network access conflicts in vehicular ad
hoc networks and guarantee string stability and zero steady-state spacing errors. The
effectiveness of the method is demonstrated by numerical simulations and
experiments with laboratory-scale Arduino cars.
S3I_NS2_16_072 - Secure and Robust Multi-Constrained QoS Aware Routing
Algorithm for VANETs
Secure QoS routing algorithms are a fundamental part of wireless networks that
aim to provide services with QoS and security guarantees. In vehicular ad hoc
networks (VANETs), vehicles perform routing functions, and at the same time act as
end-systems thus routing control messages are transmitted unprotected over wireless
channels. The QoS of the entire network could be degraded by an attack on the
routing process, and manipulation of the routing control messages. In this paper, we
propose a novel secure and reliable multi-constrained QoS aware routing algorithm
for VANETs. We employ the ant colony optimisation (ACO) technique to compute
feasible routes in VANETs subject to multiple QoS constraints determined by the data

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traffic type. Moreover, we extend the VANET-oriented evolving graph (VoEG) model
to perform plausibility checks on the routing control messages exchanged among
vehicles. Simulation results show that the QoS can be guaranteed while applying
security mechanisms to ensure a reliable and robust routing service.
S3I_NS2_16_073 - Data Dissemination With Network Coding in Two-Way
Vehicle-to-Vehicle Networks
Vehicular ad hoc networks (VANETs) can efficiently offer safety-related and
commercial contents for in-vehicle consumption. In this paper, we analyze the
vehicle-to-vehicle (V2V) data dissemination with network coding in two-way road
networks, where the vehicles move in opposite directions. In particular, depending on
whether the broadcasting coverage areas overlap or not, two-way data dissemination
is usually carried out in two phases, namely, the encountering phase and the separated
phase. We first derive the probability mass function (pmf) of the dissemination
completion time for the encountering disseminators during the encountering phase.
The data dissemination velocity in the separated phase is mathematically derived. We
prove that, without the help of the data dissemination in their own direction, the
vehicles cannot recover all original packets from the opposite direction under a scarce
handover condition. Furthermore, the dissemination slope and cache capacity of the
vehicles in the proposed model are also analytically presented. Simulation results are
provided to confirm the accuracy of the developed analytical results.
S3I_NS2_16_074 - Analytical Model and Performance evaluation of Long Term
Evolution for vehicle Safety Services
In traffic jam or dense vehicle environment, vehicular ad-hoc networks
(VANET) can’t meet safety requirement due to serious packet collision. The
traditional cellular network solves packet collision, but suffers from long end-to-end
delay. 3GPP Long Term Evolution (LTE) overcomes both drawbacks, thus it may be
used instead of VANET in some extreme environments. We use Markov models with
the dynamic scheduling and semipersistent scheduling (SPS) to evaluate how many
idle resources of LTE can be provided for safety services and how safety applications
impact on LTE traditional users. Based on the analysis, we propose to reserve the idle
radio resources in LTE for vehicular safety services (LTE-V). Additionally, we
propose the weighted-fair-queueing (WFQ) algorithm to schedule beacons for safety
services using LTE reserved resource. Numerical results verify that the proposed
mechanism can significantly improve the reliability of safety application by
borrowing limited LTE bandwidth. We also build NS3 simulation platform to verify
the effectiveness of the proposed Markov models. Finally, the reliability of

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applications including cooperation collision warning, slow vehicle indication and rear-
end collision warning using DSRC with LTE-V are evaluated. The simulation results
demonstrate that the stringent QoS requirement of the above three applications can be
satisfied even under heavy traffic.
S3I_NS2_16_075 - An Efficient Conditional Privacy-Preserving Authentication
Scheme for Vehicular Sensor Networks Without
Constructing intelligent and efficient transportation systems for modern
metropolitan areas has become a very important quest for nations possessing
metropolitan cities with ever-increasing populations. A new trend is the development
of smart vehicles with multiple sensors able to dynamically form a temporary
vehicular ad hoc network (VANET) or a vehicular sensor network (VSN). Along with
a wireless-enabled roadside unit (RSU) network, drivers in a VSN can efficiently
exchange important or urgent traffic information and make driving decisions
accordingly. In order to support secure communication and driver privacy for vehicles
in a VSN, we develop a new identity-based (ID-based) signature based on the elliptic
curve cryptosystem (ECC) and then adopt it to propose a novel conditional privacy-
preserving authentication scheme based on our invented ID-based signature. This
scheme provides secure authentication process for messages transmitted between
vehicles and RSUs. A batch message verification mechanism is also supported by the
proposed scheme to increase the message processing throughput of RSUs. To further
enhance scheme efficiency, both pairing operation and MapToPoint operation are not
applied in the proposed authentication scheme. In comparison with existing pseudo-
ID-based authentication solutions for VSN, this paper shows that the proposed scheme
has better performance in terms of time consumption.
S3I_NS2_16_076 - SCRP: Stable CDS-Based Routing Protocol for Urban
Vehicular Ad Hoc Networks
This paper addresses the issue of selecting routing paths with minimum end-to-
end delay (E2ED) for nonsafety applications in urban vehicular ad hoc networks
(VANETs). Most existing schemes aim at reducing E2ED via greedy-based
techniques (i.e., shortest path, connectivity, or number of hops), which make them
prone to the local maximum problem and to data congestion, leading to higher E2ED.
As a solution, we propose SCRP, which is a distributed routing protocol that
computes E2ED for the entire routing path before sending data messages. To do so,
SCRP builds stable backbones on road segments and connects them at intersections
via bridge nodes. These nodes assign weights to road segments based on the collected
information of delay and connectivity. Routes with the lowest aggregated weights are

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selected to forward data packets. Simulation results show that SCRP outperforms
some of the well-known protocols in literature.
S3I_NS2_16_077 - DIVERT: A Distributed Vehicular Traffic Re-routing System
for Congestion Avoidance
Centralized solutions for vehicular traffic re-routing to alleviate congestion
suffer from two intrinsic problems: scalability, as the central server has to perform
intensive computation and communication with the vehicles in real-time; and privacy,
as the drivers have to share their location as well as the origins and destinations of
their trips with the server. This article proposes DIVERT, a distributed vehicular re-
routing system for congestion avoidance. DIVERT offloads a large part of the
rerouting computation at the vehicles, and thus, the re-routing process becomes
practical in real-time. To take collaborative rerouting decisions, the vehicles exchange
messages over vehicular ad hoc networks. DIVERT is a hybrid system because it still
uses a server and Internet communication to determine an accurate global view of the
traffic. In addition, DIVERT balances the user privacy with the re-routing
effectiveness. The simulation results demonstrate that, compared with a centralized
system, the proposed hybrid system increases the user privacy by 92% on average. In
terms of average travel time, DIVERT’s performance is slightly less than that of the
centralized system, but it still achieves substantial gains compared to the no re-routing
case. In addition, DIVERT reduces the CPU and network load on the server by
99.99% and 95%, respectively.
S3I_NS2_16_078 - A Threshold Anonymous Authentication Protocol for
VANETs
Vehicular ad hoc networks (VANETs) have recently received significant
attention in improving traffic safety and efficiency. However, communication trust
and user privacy still present practical concerns to the deployment of VANETs, as
many existing authentication protocols for VANETs either suffer from the heavy
workload of downloading the latest revocation list from a remote authority or cannot
allow drivers on the road to decide the trustworthiness of a message when the
authentication on messages is anonymous. In this paper, to cope with these
challenging concerns, we propose a new authentication protocol for VANETs in a
decentralized group model by using a new group signature scheme. With the
assistance of the new group signature scheme, the proposed authentication protocol is
featured with threshold authentication, efficient revocation, unforgeability,
anonymity, and traceability. In addition, the assisting group signature scheme may
also be of independent interest, as it is characterized by efficient traceability and

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message linkability at the same time. Extensive analyses indicate that our proposed
threshold anonymous authentication protocol is secure, and the verification of
messages among vehicles can be accelerated by using batch message processing
techniques.
S3I_NS2_16_079 - A Novel Approach for Improved Vehicular Positioning Using
Cooperative Map Matching and Dynamic Base Station DGPS Concept
In this paper, a novel approach for improving vehicular positioning is
presented. This method is based on the cooperation of the vehicles by communicating
their measured information about their position. This method consists of two steps. In
the first step, we introduce our cooperative map matching method. This map matching
method uses the V2V communication in a vehicular ad hoc network (VANET) to
exchange global positioning system (GPS) information between vehicles. Having a
precise road map, vehicles can apply the road constraints of other vehicles in their
own map matching process and acquire a significant improvement in their positioning.
After that, we have proposed the concept of a dynamic base station DGPS (DDGPS),
which is used by vehicles in the second step to generate and broadcast the GPS
pseudorange corrections that can be used by newly arrived vehicles to improve their
positioning. The DDGPS is a decentralized cooperative method that aims to improve
the GPS positioning by estimating and compensating the common error in GPS
pseudorange measurements. It can be seen as an extension of DGPS where the base
stations are not necessarily static with an exact known position. In the DDGPS
method, the pseudorange corrections are estimated based on the receiver's belief on its
positioning and its uncertainty and then broadcasted to other GPS receivers. The
performance of the proposed algorithm has been verified with simulations in several
realistic scenarios.
S3I_NS2_16_080 - Stochastic Modeling of Single-Hop Cluster Stability in
Vehicular Ad Hoc Networks
Node clustering is a potential approach to improve the scalability of networking
protocols in vehicular ad hoc networks (VANETs). High relative vehicle mobility and
frequent network topology changes inflict new challenges on maintaining stable
clusters. As a result, cluster stability is a crucial measure of the efficiency of
clustering algorithms for VANETs. This paper presents a stochastic analysis of the
vehicle mobility impact on single-hop cluster stability. A stochastic mobility model is
adopted to capture the time variations of intervehicle distances (distance headways).
First, we propose a discrete-time lumped Markov chain to model the time variations
of a system of distance headways. Second, the first passage time analysis is used to

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derive probability distributions of the time periods of the invariant cluster-overlap
state and cluster membership as measures of cluster stability. Third, queueing theory
is utilized to model the limiting behaviors of the numbers of common and unclustered
nodes between neighboring clusters. Numerical results are presented to evaluate the
proposed models, which demonstrate a close agreement between analytical and
simulation results.
MATLAB IMAGE PROCESSING AND
WIRELESS COMMUNICATION - 2016-
2017
S3I_MAT16_001 - Dynamic Facial Expression Recognition with Atlas Construction and
Sparse Representation
In this paper, a new dynamic facial expression recognition method is proposed.
Dynamic facial expression recognition is formulated as a longitudinal groupwise registration
problem. The main contributions of this method lie in the following aspects: 1) subject-specific
facial feature movements of different expressions are described by a diffeomorphic growth
model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse
groupwise image registration method, which can describe the overall facial feature changes
among the whole population and can suppress the bias due to large intersubject facial variations;
and 3) both the image appearance information in spatial domain and topological evolution
information in temporal domain are used to guide recognition by a sparse representation method.
The proposed framework has been extensively evaluated on five databases for different

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applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial
expression recognition, and UNBC-McMaster database for spontaneous pain expression
monitoring. This framework is also compared with several state-of-the-art dynamic facial
expression recognition methods. The experimental results demonstrate that the recognition rates
of the new method are consistently higher than other methods under comparison.
S3I_MAT16_002 - Lossless Compression of JPEG Coded Photo Collections
The explosion of digital photos has posed a significant challenge to photo storage
and transmission for both personal devices and cloud platforms. In this paper, we propose a
novel lossless compression method to further reduce the size of a set of JPEG coded correlated
images without any loss of information. The proposed method jointly removes inter/intra image
redundancy in the feature, spatial, and frequency domains. For each collection, we first organize
the images into a pseudo video by minimizing the global prediction cost in the feature domain.
We then present a hybrid disparity compensation method to better exploit both the global and
local correlations among the images in the spatial domain. Furthermore, the redundancy between
each compensated signal and the corresponding target image is adaptively reduced in the
frequency domain. Experimental results demonstrate the effectiveness of the proposed lossless
compression method. Compared with the JPEG coded image collections, our method achieves
average bit savings of more than 31%.
S3I_MAT16_003 - Pixel modeling using histograms based on fuzzy partitions for dynamic
background subtraction
We propose a novel pixel-modeling approach for background subtraction using
histograms based on strong uniform fuzzy partitions. In the proposed method, the temporal
distribution of pixel values is represented by a histogram based on a triangular partition. The
threshold for background segmentation is set adaptively according to the shape of the histogram.
Histogram accumulation is controlled adaptively by a fuzzy controller under a supervised
learning framework. Benefiting from the adaptive scheme, with no parameter tuning, the
proposed algorithm functions well across a wide spectrum of challenging environments. The
performance of the proposed method is evaluated against more than 20 state-of-the-art methods
in complex outdoor environments, particularly in those consisting of highly dynamic
backgrounds and camouflaged foregrounds. Experimental results confirm that the proposed
method performs effectively in terms of both the true positive rate and the noise suppression
ability. Further, it outperforms other state-of-the-art methods by a significant margin.
S3I_MAT16_004 - Layer-Based Approach for Image Pair Fusion
Recently, image pairs, such as noisy and blurred images or infrared and noisy
images, have been considered as a solution to provide high-quality photographs under low
lighting conditions. In this paper, a new method for decomposing the image pairs into two layers,
i.e., the base layer and the detail layer, is proposed for image pair fusion. In the case of infrared
and noisy images, simple naive fusion leads to unsatisfactory results due to the discrepancies in
brightness and image structures between the image pair. To address this problem, a local
contrast-preserving conversion method is first proposed to create a new base layer of the infrared
image, which can have visual appearance similar to another base layer, such as the denoised
noisy image. Then, a new way of designing three types of detail layers from the given noisy and

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infrared images is presented. To estimate the noise-free and unknown detail layer from the three
designed detail layers, the optimization framework is modeled with residual-based sparsity and
patch redundancy priors. To better suppress the noise, an iterative approach that updates the
detail layer of the noisy image is adopted via a feedback loop. This proposed layer-based method
can also be applied to fuse another noisy and blurred image pair. The experimental results show
that the proposed method is effective for solving the image pair fusion problem.
S3I_MAT16_005 - Adaptive Pairing Reversible Watermarking
This letter revisits the pairwise reversible watermarking scheme of Ou et al., 2013.
An adaptive pixel pairing that considers only pixels with similar prediction errors is introduced.
This adaptive approach provides an increased number of pixel pairs where both pixels are
embedded and decreases the number of shifted pixels. The adaptive pairwise reversible
watermarking outperforms the state-of-the-art low embedding bit-rate schemes proposed so far.
S3I_MAT16_006 - Adaptive Part-Level Model Knowledge Transfer for Gender
Classification
In this letter, we propose an adaptive part-level model knowledge transfer approach
for gender classification of facial images based on Fisher vector (FV). Specifically, we first
decompose the whole face image into several parts and compute the dense FVs on each face part.
An adaptive transfer learning model is then proposed to reduce the discrepancies between the
training data and the testing data for enhancing classification performance. Compared to the
existing gender classification methods, the proposed approach is more adaptive to the testing
data, which is quite beneficial to the performance improvement. Extensive experiments on
several public domain face data sets clearly demonstrate the effectiveness of the proposed
approach.
S3I_MAT16_007 - Patch-Based Video Denoising With Optical Flow Estimation
A novel image sequence denoising algorithm is presented. The proposed approach
takes advantage of the selfsimilarity and redundancy of adjacent frames. The algorithm is
inspired by fusion algorithms, and as the number of frames increases, it tends to a pure temporal
average. The use of motion compensation by regularized optical flow methods permits robust
patch comparison in a spatiotemporal volume. The use of principal component analysis ensures
the correct preservation of fine texture and details. An extensive comparison with the state-of-
the-art methods illustrates the superior performance of the proposed approach, with improved
texture and detail reconstruction.
S3I_MAT16_008 - Fusion of Quantitative Image and Genomic Biomarkers to Improve
Prognosis Assessment of Early Stage Lung Cancer Patients
This study aims to develop a new quantitative image feature analysis scheme and
investigate its role along with 2 genomic biomarkers namely, protein expression of the excision
repair cross-complementing 1 (ERCC1) genes and a regulatory subunit of ribonucleotide
reductase (RRM1), in predicting cancer recurrence risk of Stage I non-small-cell lung cancer
(NSCLC) patients after surgery. Methods: By using chest computed tomography images, we
developed a computer-aided detection scheme to segment lung tumors and computed tumor-
related image features. After feature selection, we trained a Naïve Bayesian network based

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classifier using 8 image features and a Multilayer Perceptron classifier using 2 genomic
biomarkers to predict cancer recurrence risk, respectively. Two classifiers were trained and
tested using a dataset with 79 Stage I NSCLC cases, a synthetic minority oversampling technique
and a leave-one-case-out validation method. A fusion method was also applied to combine
prediction scores of two classifiers. Results: AUC (areas under ROC curves) values are
0.78±0.06 and 0.68±0.07 when using the image feature and genomic biomarker based classifiers,
respectively. AUC value significantly increased to 0.84±0.05 (p<0.05) when fusion of two
classifier-generated prediction scores using an equal weighting factor. Conclusion: A
quantitative image feature based classifier yielded significantly higher discriminatory power than
a genomic biomarker based classifier in predicting cancer recurrence risk. Fusion of prediction
scores generated by the two classifiers further improved prediction performance. Significance:
We demonstrated a new approach that has potential to assist clinicians in more effectively
managing Stage I NSCLC patients to reduce cancer recurrence risk.
S3I_MAT16_009 - Multivideo Object Cosegmentation for Irrelevant Frames Involved
Videos
Even though there have been a large amount of previous work on video segmentation
techniques, it is still a challenging task to extract the video objects accurately without
interactions, especially for those videos which contain irrelevant frames (frames containing no
common targets). In this essay, a novel multivideo object cosegmentation method is raised to
cosegment common or similar objects of relevant frames in different videos, which includes
three steps: 1) object proposal generation and clustering within each video; 2) weighted graph
construction and common objects selection; and 3) irrelevant frames detection and pixel-level
segmentation refinement. We apply our method on challenging datasets and exhaustive
comparison experiments demonstrate the effectiveness of the proposed method.
S3I_MAT16_010 - Multi-Viewpoint Panorama Construction with Wide-Baseline Images
We present a novel image stitching approach, which can produce visually
plausible panoramic images with input taken from different viewpoints. Unlike
previous methods, our approach allows wide baselines between images and non-
planar scene structures. Instead of 3D reconstruction, we design a mesh based
framework to optimize alignment and regularity in 2D. By solving a global objective
function consisting of alignment and a set of prior constraints, we construct panoramic
images, which are locally as perspective as possible and yet nearly orthogonal in the
global view. We improve composition and achieve good performance on misaligned
area. Experimental results on challenging data demonstrate the effectiveness of the
proposed method.
S3I_MAT16_011 - A Security-Enhanced Alignment-Free Fuzzy Vault-Based
Fingerprint Cryptosystem Using Pair-Polar Minutiae Structures
Alignment-free fingerprint cryptosystems perform matching using relative
information between minutiae, e.g., local minutiae structures, is promising, because it
can avoid the recognition errors and information leakage caused by template

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alignment/registration. However, as most local minutiae structures only contain
relative information of a few minutiae in a local region, they are less discriminative
than the global minutiae pattern. Besides, the similarity measures for trivially/coarsely
quantized features in the existing work cannot provide a robust way to deal with
nonlinear distortions, a common form of intra-class variation. As a result, the
recognition accuracy of current alignment-free fingerprint cryptosystems is
unsatisfying. In this paper, we propose an alignment-free fuzzy vault-based fingerprint
cryptosystem using highly discriminative pair-polar (P-P) minutiae structures. The
fine quantization used in our system can largely retain information about a fingerprint
template and enables the direct use of a traditional, well-established minutiae matcher.
In terms of template/key protection, the proposed system fuses cancelable biometrics
and biocryptography. Transforming the P-P minutiae structures before encoding
destroys the correlations between them, and can provide privacy-enhancing features,
such as revocability and protection against cross-matching by setting distinct
transformation seeds for different applications. The comparison with other minutiae-
based fingerprint cryptosystems shows that the proposed system performs favorably
on selected publicly available databases and has strong security.
S3I_MAT16_012 - Microwave Unmixing With Video Segmentation for Inferring
Broadleaf and Needleleaf Brightness Temperatures and Abundances From
Mixed Forest Observations
Passive microwave sensors have better capability of penetrating forest layers to
obtain more information from forest canopy and ground surface. For forest
management, it is useful to study passive microwave signals from forests. Passive
microwave sensors can detect signals from needleleaf, broadleaf, and mixed forests.
The observed brightness temperature of a mixed forest can be approximated by a
linear combination of the needleleaf and broadleaf brightness temperatures weighted
by their respective abundances. For a mixed forest observed by an N-band microwave
radiometer with horizontal and vertical polarizations, there are 2 N observed
brightness temperatures. It is desirable to infer 4 N + 2 unknowns: 2 N broadleaf
brightness temperatures, 2 N needleleaf brightness temperatures, 1 broadleaf
abundance, and 1 needleleaf abundance. This is a challenging underdetermined
problem. In this paper, we devise a novel method that combines microwave unmixing
with video segmentation for inferring broadleaf and needleleaf brightness
temperatures and abundances from mixed forests. We propose an improved Otsu
method for video segmentation to infer broadleaf and needleleaf abundances. The
brightness temperatures of needleleaf and broadleaf trees can then be solved by the
nonnegative least squares solution. For our mixed forest unmixing problem, it turns
out that the ordinary least squares solution yields the desired positive brightness
temperatures. The experimental results demonstrate that the proposed method is able

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to unmix broadleaf and needleleaf brightness temperatures and abundances well. The
absolute differences between the reconstructed and observed brightness temperatures
of the mixed forest are well within 1 K.
S3I_MAT16_013 - 2D Orthogonal Locality Preserving Projection for Image
Denoising
Sparse representations using transform-domain techniques are widely used for better
interpretation of the raw data. Orthogonal locality preserving projection (OLPP) is a
linear technique that tries to preserve local structure of data in the transform domain
as well. Vectorized nature of OLPP requires high-dimensional data to be converted to
vector format, hence may lose spatial neighborhood information of raw data. On the
other hand, processing 2D data directly, not only preserves spatial information, but
also improves the computational efficiency considerably. The 2D OLPP is expected to
learn the transformation from 2D data itself. This paper derives mathematical
foundation for 2D OLPP. The proposed technique is used for image denoising task.
Recent state-of-the-art approaches for image denoising work on two major
hypotheses, i.e., non-local self-similarity and sparse linear approximations of the data.
Locality preserving nature of the proposed approach automatically takes care of self-
similarity present in the image while inferring sparse basis. A global basis is adequate
for the entire image. The proposed approach outperforms several state-of-the-art
image denoising approaches for gray-scale, color, and texture images.
S3I_MAT16_014 - Exploring the Usefulness of Light Field Cameras for
Biometrics : An Empirical Study on Face and Iris Recognition
A light field sensor can provide useful information in terms of multiple depth (or
focus) images, holding additional information that is quite useful for biometric
applications. In this paper, we examine the applicability of a light field camera for
biometric applications by considering two prominently used biometric characteristics:
1) face and 2) iris. To this extent, we employed a Lytro light field camera to construct
two new and relatively large scale databases, for both face and iris biometrics. We
then explore the additional information available from different depth images, which
are rendered by light field camera, in two different manners: 1) by selecting the best
focus image from the set of depth images and 2) combining all the depth images using
super-resolution schemes to exploit the supplementary information available within
the set elements. Extensive evaluations are carried out on our newly constructed
database, demonstrating the significance of using additional information rendered by a
light field camera to improve the overall performance of the biometric system.
S3I_MAT16_015 - Spectral–Spatial Adaptive Sparse Representation for
Hyperspectral Image Denoising

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In this paper, a novel spectral-spatial adaptive sparse representation (SSASR) method
is proposed for hyperspectral image (HSI) denoising. The proposed SSASR method
aims at improving noise-free estimation for noisy HSI by making full use of highly
correlated spectral information and highly similar spatial information via sparse
representation, which consists of the following three steps. First, according to spectral
correlation across bands, the HSI is partitioned into several nonoverlapping band
subsets. Each band subset contains multiple continuous bands with highly similar
spectral characteristics. Then, within each band subset, shape-adaptive local regions
consisting of spatially similar pixels are searched in spatial domain. This way,
spectral-spatial similar pixels can be grouped. Finally, the highly correlated and
similar spectral-spatial information in each group is effectively used via the joint
sparse coding, in order to generate better noise-free estimation. The proposed SSASR
method is evaluated by different objective metrics in both real and simulated
experiments. The numerical and visual comparison results demonstrate the
effectiveness and superiority of the proposed method.
S3I_MAT16_016 - Robust Sclera Recognition System With Novel Sclera
Segmentation and Validation Techniques
Sclera blood veins have been investigated recently as a biometric trait which can be
used in a recognition system. The sclera is the white and opaque outer protective part
of the eye. This part of the eye has visible blood veins which are randomly distributed.
This feature makes these blood veins a promising factor for eye recognition. The
sclera has an advantage in that it can be captured using a visible-wavelength camera.
Therefore, applications which may involve the sclera are wide ranging. The
contribution of this paper is the design of a robust sclera recognition system with high
accuracy. The system comprises of new sclera segmentation and occluded eye
detection methods. We also propose an efficient method for vessel enhancement,
extraction, and binarization. In the feature extraction and matching process stages, we
additionally develop an efficient method, that is, orientation, scale, illumination, and
deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases
show an advantage in terms of segmentation accuracy and computational complexity
compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.
S3I_MAT16_017 - Enhancing Sketch-Based Image Retrieval by Re-Ranking and
Relevance Feedback
A sketch-based image retrieval often needs to optimize the tradeoff between
efficiency and precision. Index structures are typically applied to large-scale databases
to realize efficient retrievals. However, the performance can be affected by
quantization errors. Moreover, the ambiguousness of user-provided examples may
also degrade the performance, when compared with traditional image retrieval

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methods. Sketch-based image retrieval systems that preserve the index structure are
challenging. In this paper, we propose an effective sketch-based image retrieval
approach with re-ranking and relevance feedback schemes. Our approach makes full
use of the semantics in query sketches and the top ranked images of the initial results.
We also apply relevance feedback to find more relevant images for the input query
sketch. The integration of the two schemes results in mutual benefits and improves the
performance of the sketch-based image retrieval.
S3I_MAT16_018 - Detection of Moving Objects Using Fuzzy Color Difference
Histogram Based Background Subtraction
Detection of moving objects in the presence of complex scenes such as dynamic
background (e.g, swaying vegetation, ripples in water, spouting fountain),
illumination variation, and camouflage is a very challenging task. In this context, we
propose a robust background subtraction technique with three contributions. First, we
present the use of color difference histogram (CDH) in the background subtraction
algorithm. This is done by measuring the color difference between a pixel and its
neighbors in a small local neighborhood. The use of CDH reduces the number of false
errors due to the non-stationary background, illumination variation and camouflage.
Secondly, the color difference is fuzzified with a Gaussian membership function.
Finally, a novel fuzzy color difference histogram (FCDH) is proposed by using fuzzy
c-means (FCM) clustering and exploiting the CDH. The use of FCM clustering
algorithm in CDH reduces the large dimensionality of the histogram bins in the
computation and also lessens the effect of intensity variation generated due to the fake
motion or change in illumination of the background. The proposed algorithm is tested
with various complex scenes of some benchmark publicly available video sequences.
It exhibits better performance over the state-of-the-art background subtraction
techniques available in the literature in terms of classification accuracy metrics like
MCC and PCC.
S3I_MAT16_019 - A Decomposition Framework for Image Denoising
Algorithms
In this paper, we consider an image decomposition model that provides a novel
framework for image denoising. The model computes the components of the image to
be processed in a moving frame that encodes its local geometry (directions of
gradients and level lines). Then, the strategy we develop is to denoise the components
of the image in the moving frame in order to preserve its local geometry, which would
have been more affected if processing the image directly. Experiments on a whole
image database tested with several denoising methods show that this framework can
provide better results than denoising the image directly, both in terms of Peak signal-
to-noise ratio and Structural similarity index metrics.

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S3I_MAT16_020 - Distance-Based Encryption: How to Embed Fuzziness in
Biometric-Based Encryption
We introduce a new encryption notion called distance-based encryption (DBE) to
apply biometrics in identity-based encryption. In this notion, a ciphertext encrypted
with a vector and a threshold value can be decrypted with a private key of another
vector, if and only if the distance between these two vectors is less than or equal to the
threshold value. The adopted distance measurement is called Mahalanobis distance,
which is a generalization of Euclidean distance. This novel distance is a useful
recognition approach in the pattern recognition and image processing community. The
primary application of this new encryption notion is to incorporate biometric
identities, such as face, as the public identity in an identity-based encryption. In such
an application, usually the input biometric identity associated with a private key will
not be exactly the same as the input biometric identity in the encryption phase, even
though they are from the same user. The introduced DBE addresses this problem well
as the decryption condition does not require identities to be identical but having small
distance. The closest encryption notion to DBE is the fuzzy identity-based encryption,
but it measures biometric identities using a different distance called an overlap
distance (a variant of Hamming distance) that is not widely accepted by the pattern
recognition community, due to its long binary representations. In this paper, we study
this new encryption notion and its constructions. We show how to generically and
efficiently construct such a DBE from an inner product encryption (IPE) with
reasonable size of private keys and ciphertexts. We also propose a new IPE scheme
with the shortest private key to build DBE, namely, the need for a short private key.
Finally, we study the encryption efficiency of DBE by splitting our IPE encryption
algorithm into offline and online algorithms.
S3I_MAT16_021 - Scalable Feature Matching by Dual Cascaded Scalar
Quantization for Image Retrieval
In this paper, we investigate the problem of scalable visual feature matching in large-
scale image search and propose a novel cascaded scalar quantization scheme in dual
resolution. We formulate the visual feature matching as a range-based neighbor search
problem and approach it by identifying hyper-cubes with a dual-resolution scalar
quantization strategy. Specifically, for each dimension of the PCA-transformed
feature, scalar quantization is performed at both coarse and fine resolutions. The
scalar quantization results at the coarse resolution are cascaded over multiple
dimensions to index an image database. The scalar quantization results over multiple
dimensions at the fine resolution are concatenated into a binary super-vector and
stored into the index list for efficient verification. The proposed cascaded scalar
quantization (CSQ) method is free of the costly visual codebook training and thus is

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independent of any image descriptor training set. The index structure of the CSQ is
flexible enough to accommodate new image features and scalable to index large-scale
image database. We evaluate our approach on the public benchmark datasets for
large-scale image retrieval. Experimental results demonstrate the competitive retrieval
performance of the proposed method compared with several recent retrieval
algorithms on feature quantization.
S3I_MAT16_022 - ACE–An Effective Anti-forensic Contrast Enhancement
Technique
Detecting Contrast Enhancement (CE) in images and anti-forensic approaches against
such detectors have gained much attention in multimedia forensics lately. Several
contrast enhancement detectors analyze the first order statistics such as gray-level
histogram of images to determine whether an image is CE or not. In order to counter
these detectors various anti-forensic techniques have been proposed. This led to a
technique that utilized second order statistics of images for CE detection. In this letter,
we propose an effective anti-forensic approach that performs CE without significant
distortion in both the first and second order statistics of the enhanced image. We
formulate an optimization problem using a variant of the well known Total Variation
(TV) norm image restoration formulation. Experiments show that the algorithm
effectively overcomes the first and second order statistics based detectors without loss
in quality of the enhanced image.
S3I_MAT16_023 - Visualization of Tumor Response to Neoadjuvant Therapy for
Rectal Carcinoma by Nonlinear Optical Imaging
The continuing development of nonlinear optical imaging techniques has opened
many new windows in biological exploration. In this study, a nonlinear optical
microscopy-multiphoton microscopy (MPM) was expanded to detect tumor response
in rectal carcinoma after neoadjuvant therapy; especially normal tissue, pre- and post-
therapeutic cancerous tissues were investigated in order to present more detailed
information and make comparison. It was found that the MPM has ability not only to
directly visualize histopathologic changes in rectal carcinoma, including stromal
fibrosis, colloid response, residual tumors, blood vessel hyperplasia, and
inflammatory reaction, which had been proven to have important influence on
estimation of the prognosis and the effect of neoadjuvant treatment, but also to
provide quantitative optical biomarkers including the intensity ratio of SHG over
TPEF and collagen orientation index. These results show that the MPM will become a
useful tool for clinicians to determine whether neoadjuvant therapy is effective or
treatment strategy is approximate, and this study may provide the groundwork for
further exploration into the application of MPM in a clinical setting.

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S3I_MAT16_024 - Robust Edge-Stop Functions for Edge-Based Active Contour
Models in Medical Image Segmentation
Edge-based active contour models are effective in segmenting images with intensity
inhomogeneity but often fail when applied to images containing poorly defined
boundaries, such as in medical images. Traditional edge-stop functions (ESFs) utilize
only gradient information, which fails to stop contour evolution at such boundaries
because of the small gradient magnitudes. To address this problem, we propose a
framework to construct a group of ESFs for edge-based active contour models to
segment objects with poorly defined boundaries. In our framework, which
incorporates gradient information as well as probability scores from a standard
classifier, the ESF can be constructed from any classification algorithm and applied to
any edge-based model using a level set method. Experiments on medical images using
the distance regularized level set for edge-based active contour models as well as the
k-nearest neighbors and the support vector machine confirm the effectiveness of the
proposed approach.
S3I_MAT16_025 - A Combined KFDA Method and GUI Realized for Face
Recognition
Traditional face recognition methods such as Principal Components Analysis(PCA),
Independent Component Analysis(ICA) and Linear Discriminant Analysis(LDA) are
linear discriminant methods, but in the real situation, a lot of problems can't be linear
discriminated; therefore, researchers proposed face recognition method based on
kernel techniques which can transform the nonlinear problem of inputting space into
the linear problem of high dimensional space. In this paper, we propose a recognition
method based on kernel function which combines kernel Fisher Discriminant
Analysis(KFDA) with kernel Principle Components Analysis(KPCA) and use typical
ORL(Olivetti Research Laboratory) face database as our experimental database. There
are four key steps: constructing feature subspace, image projection, feature extraction
and image recognition. We found that the recognition accuracy has been greatly
improved by using nonlinear identification method and combined feature extraction
methods. We use MATLAB software as the platform, and use the GUI to demonstrate
the process of face recognition in order to achieving human-computer interaction and
making the process and result more intuitive.
S3I_MAT16_026 - A Cost-Effective Minutiae Disk Code
For Fingerprint Recognition And Its
Implementation

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Fingerprint is one of the unique biometric features for the application of identity
security. Minutiae cylinder code (MCC) constructs a cylinder for each minutia to
record the contribution of the neighbor minutiae, which has great performance on
fingerprint recognition. However, the computation time of the MCC is high.
Therefore, we proposed a new disk structure to encode the local structure for each
minutia. The proposed minutiae disk code (MDC) clearly illustrates the distribution of
the neighbor minutiae and encodes the neighbor minutiae more efficiently by having
280.08× speed faster than the MCC encoding part on Matlab platform. The proposed
MDC approach has 96.81% recognition rate on FVC2000 and FVC2002 datasets. The
hardware implementation can achieve the operating frequency at 111MHz, which can
process 1234 fingerprint images per second with the image size of 255 χ 255 and the
maximum of 64 minutiae, under TSMC 90nm CMOS technology. The hardware
implementation has 141.27× speed faster than the MCC method.
S3I_MAT16_027 - A Hands-on Application-Based Tool for STEM Students to
Understand Differentiation
The main goal of this project is to illustrate to college students in science, technology,
engineering, and mathematics (STEM) fields some fundamental concepts in calculus.
A high-level technical computing language - MATLAB, is the core platform used in
the construction of this project. A graphical user interface (GUI) is designed to
interactively explain the intuition behind a key mathematical concept: differentiation.
The GUI demonstrates how a derivative operation (as a form of kernel) can be applied
on one-dimensional (1D) and two-dimensional (2D) images (as a form of vector). The
user can interactively select from a set of predetermined operations and images in
order to show how the selected kernel operates on the corresponding image. Such
interactive tools in calculus courses are of great importance and need, especially for
STEM students who seek an intuitive and visual understanding of mathematical
notions that are often presented to them as abstract concepts. In addition to students,
instructors can greatly benefit from using such tools to elucidate the use of
fundamental concepts in mathematics in a real world context.
S3I_MAT16_028 - Rotation Invariant Texture Description Using Symmetric
Dense Microblock Difference
This letter is devoted to the problem of rotation invariant texture classification. Novel
rotation invariant feature, symmetric dense microblock difference (SDMD), is
proposed which captures the information at different orientations and scales. N -fold
symmetry is introduced in the feature design configuration, while retaining the
random structure that provides discriminative power. The symmetry is utilized to
achieve a rotation invariance. The SDMD is extracted using an image pyramid and
encoded by the Fisher vector approach resulting in a descriptor which captures

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variations at different resolutions without increasing the dimensionality. The proposed
image representation is combined with the linear SVM classifier. Extensive
experiments are conducted on four texture data sets [Brodatz, UMD, UIUC, and
Flickr material data set (FMD)] using standard protocols. The results demonstrate that
our approach outperforms the state of the art in texture classification. The MATLAB
code is made available.11
S3I_MAT16_029 - A Novel Image Quality Assessment With Globally and
Locally Consilient Visual Quality Perception
Computational models for image quality assessment (IQA) have been developed by
exploring effective features that are consistent with the characteristics of a human
visual system (HVS) for visual quality perception. In this paper, we first reveal that
many existing features used in computational IQA methods can hardly characterize
visual quality perception for local image characteristics and various distortion types.
To solve this problem, we propose a new IQA method, called the structural contrast-
quality index (SC-QI), by adopting a structural contrast index (SCI), which can well
characterize local and global visual quality perceptions for various image
characteristics with structural-distortion types. In addition to SCI, we devise some
other perceptually important features for our SC-QI that can effectively reflect the
characteristics of HVS for contrast sensitivity and chrominance component variation.
Furthermore, we develop a modified SC-QI, called structural contrast distortion
metric (SC-DM), which inherits desirable mathematical properties of valid distance
metricability and quasi-convexity. So, it can effectively be used as a distance metric
for image quality optimization problems. Extensive experimental results show that
both SC-QI and SC-DM can very well characterize the HVS's properties of visual
quality perception for local image characteristics and various distortion types, which
is a distinctive merit of our methods compared with other IQA methods. As a result,
both SC-QI and SC-DM have better performances with a strong consilience of global
and local visual quality perception as well as with much lower computation
complexity, compared with the state-of-the-art IQA methods. The MATLAB source
codes of the proposed SC-QI and SC-DM are publicly available online at
https://sites.google.com/site/sunghobaecv/iqa.
S3I_MAT16_030 - A DCT-based Total JND Proﬁle for Spatio-Temporal and
Foveated Masking Effects
In image and video processing fields, DCT-based just noticeable difference (JND)
profiles have effectively been utilized to remove perceptual redundancies in pictures
for compression. In this paper, we solve two problems that are often intrinsic to the
conventional DCT-based JND profiles: (i) no foveated masking (FM) JND model has
been incorporated in modeling the DCT-based JND profiles; and (ii) the conventional

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temporal masking (TM) JND models assume that all moving objects in frames can be
well tracked by the eyes and that they are projected on the fovea regions of the eyes,
which is not a realistic assumption and may result in poor estimation of JND values
for untracked moving objects (or image regions). To solve these two problems, we
first propose a generalized JND model for joint effects between TM and FM effects.
With this model, called the temporal-foveated masking (TFM) JND model, JND
thresholds for any tracked/untracked and moving/still image regions can be
elaborately estimated. Finally, the TFM-JND model is incorporated into a total DCT-
based JND profile with a spatial contrast sensitivity function, luminance masking, and
contrast masking JND models. In addition, we propose a JND adjustment method for
our total JND profile to avoid overestimation of JND values for image blocks of fixed
sizes with various image characteristics. To validate the effectiveness of the total JND
profile, an experiment involving a subjective distortionvisibility assessment has been
conducted. The experiment results show that the proposed total DCT-based JND
profile yields significant performance improvement with much higher capability of
distortion concealment (average 5.6 dB lower PSNR) compared to state-of-the-art
JND profiles. The MATLAB source code of the proposed total DCT-based JND
profile is publicly available online at https://sites.google.com/site/sunghobaecv/jnd
S3I_MAT16_031 - PiCode: a New Picture-Embedding 2D Barcode
Nowadays, 2D barcodes have been widely used as an interface to connect potential
customers and advertisement contents. However, the appearance of a conventional 2D
barcode pattern is often too obtrusive for integrating into an aesthetically designed
advertisement. Besides, no human readable information is provided before the
barcode is successfully decoded. This paper proposes a new picture-embedding 2D
barcode, called PiCode, which mitigates these two limitations by equipping a
scannable 2D barcode with a picturesque appearance. PiCode is designed with careful
considerations on both the perceptual quality of the embedded image and the decoding
robustness of the encoded message. Comparisons with the existing beautified 2D
barcodes show that PiCode achieves one of the best perceptual qualities for the
embedded image, and maintains a better tradeoff between image quality and decoding
robustness in various application conditions. PiCode has been implemented in the
MATLAB on a PC and some key building blocks have also been ported to Android
and iOS platforms. Its practicality for real-world applications has been successfully
demonstrated.
S3I_MAT16_032 - OCR Based Feature Extraction and Template Matching
Algorithms for Qatari Number Plate
There are several algorithms and methods that could be applied to perform the
character recognition stage of an automatic number plate recognition system;

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however, the constraints of having a high recognition rate and real-time processing
should be taken into consideration. In this paper four algorithms applied to Qatari
number plates are presented and compared. The proposed algorithms are based on
feature extraction (vector crossing, zoning, combined zoning and vector crossing) and
template matching techniques. All four proposed algorithms have been implemented
and tested using MATLAB. A total of 2790 Qatari binary character images were used
to test the algorithms. Template matching based algorithm showed the highest
recognition rate of 99.5% with an average time of 1.95 ms per character.
S3I_MAT16_033 - HD Qatari ANPR System
Recently, Automatic Number Plate Recognition (ANPR) systems have become
widely used in safety, security, and commercial aspects. The whole ANPR system is
based on three main stages: Number Plate Localization (NPL), Character
Segmentation (CS), and Optical Character Recognition (OCR). In recent years, to
provide better recognition rate, High Definition (HD) cameras have started to be used.
However, most known techniques for standard definition are not suitable for real-time
HD image processing due to the computationally intensive cost of localizing the
number plate. In this paper, algorithms to implement the three main stages of a high
definition ANPR system for Qatari number plates are presented. The algorithms have
been tested using MATLAB and two databases as a proof of concept. Implementation
results have shown that the system is able to process one HD image in 61 ms, with an
accuracy of 98.0% in NPL, 99.75% per character in CS, and 99.5% in OCR.
S3I_MAT16_034 - Template Matching of Aerial Images using GPU
During the last decade, processor architectures have emerged with hundreds and
thousands of high speed processing cores in a single chip. These cores can work in
parallel to share a work load for faster execution. This paper presents performance
evaluations on such multicore and many-core devices by mapping a computationally
expensive correlation kernel of a template matching process using various
programming models. The work builds a base performance case by a sequential
mapping of the algorithm on an Intel processor. In the second step, the performance of
the algorithm is enhanced by parallel mapping of the kernel on a shared memory
multicore machine using OpenMP programming model. Finally, the Normalized
Cross-Correlation (NCC) kernel is scaled to map on a many-core K20 GPU using
CUDA programming model. In all steps, the correctness of the implementation of
algorithm is taken care by comparing computed data with reference results from a
high level implementation in MATLAB. The performance results are presented with
various optimization techniques for MATLAB, Sequential, OpenMP and CUDA
based implementations. The results show that GPU based implementation achieves
32x and 5x speed-ups respectively to the base case and multicore implementations

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respectively. Moreover, using inter-block sub-sampling on an 8-bit 4000×4000
reference gray-scale image achieves the execution time upto 2.8sec with an error
growth less than 20% for the selected templates of size 96×96.
S3I_MAT16_035 - Analysis of Adaptive Filter and ICA for Noise Cancellation
from a Video Frame
Noise cancellation algorithms have been frequently applied in many fields including
image/video processing. Adaptive noise cancellation algorithms exploit the
correlation property of noise and remove the noise from the input signal more
effectively than non-adaptive algorithms. In this paper different noise cancellation
techniques are applied to de-noise a video frame. Three different variants of gradient
based adaptive filtering algorithms and independent component analysis (ICA)
procedure are implemented and compared on the basis of signal to noise ratio (SNR)
and computational time. The common algorithms used in adaptive filters are least
mean square (LMS), normalized least means square (NLMS), and recursive least
mean square (RLS). The simulation results demonstrates that NLMS algorithm is
computationally efficient but cannot handle impulsive noise whereas LMS and RLS
can perform better for long duration noise signals. The comparative analysis of
adaptive filtering algorithms and ICA shows that ICA can perform better then all three
iterative gradient based algorithms because of its non-iterative nature. For testing and
simulations, three variants of white Gaussian noise (WGN) are used to corrupt the
video frame.
S3I_MAT16_036 - Active Learning Methods for Efﬁcient Hybrid Biophysical
Variable Retrieval
Kernel-based machine learning regression algorithms (MLRAs) are potentially
powerful methods for being implemented into operational biophysical variable
retrieval schemes. However, they face difficulties in coping with large training data
sets. With the increasing amount of optical remote sensing data made available for
analysis and the possibility of using a large amount of simulated data from radiative
transfer models (RTMs) to train kernel MLRAs, efficient data reduction techniques
will need to be implemented. Active learning (AL) methods enable to select the most
informative samples in a data set. This letter introduces six AL methods for achieving
optimized biophysical variable estimation with a manageable training data set, and
their implementation into a Matlab-based MLRA toolbox for semiautomatic use. The
AL methods were analyzed on their efficiency of improving the estimation accuracy
of the leaf area index and chlorophyll content based on PROSAIL simulations. Each
of the implemented methods outperformed random sampling, improving retrieval
accuracy with lower sampling rates. Practically, AL methods open opportunities to

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feed advanced MLRAs with RTM-generated training data for the development of
operational retrieval models.
S3I_MAT16_037 - Development of a Brain-Computer Interface Based on Visual
Stimuli for the Movement of a Robot Joints
This paper presents a brain computer interface (BCI) to control a robotic arm by brain
signals from visual stimuli. The following signal processing steps were established;
acquisition of brain signals by electroencephalography (EEG) electrodes; noise
reduction; extraction of signal characteristics and signal classification. Reliable brain
signals were obtained by the use of the Emotiv EPOC® commercial hardware. The
OpenViBE® commercial software was used to program the signal processing
algorithms. By using Matlab® together with an Arduino® electronic board, two servo
motors were controlled to drive two joints of a 5 degrees-of-freedom robot
commanded by P300-type evoked potential brain signals from visual stimulation
when a subject concentrates on particular images from an image matrix displayed in
the computer screen. The experiments were conducted with and without auditive and
visual noise (artifacts) to find out the noise influence in the signal classification
outcome. The obtained experimental results presented an efficiency in the
identification stage up to 100% with and without hearing noise conditions. However,
under visual noise conditions a maximum efficiency of 50% was reached. The
experiments for the servomotors control were carried out without noise, reaching an
efficiency of 100% in the identification stage.

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ECE - EMBEDDED IEEE
PROJECTS - 2016-2017
S3I_EMB16_001 - Remote monitoring of photovoltaic systems using embedded
system clusters
Remote monitoring of photovoltaic systems is critically important for the users. The
performance of each component existing in these systems should be observable. In
this study, a cheap and easily mountable remote monitoring design for low cost
photovoltaic systems located near urban areas is proposed. With this design, it is
aimed to transmit collected information at the remote solar energy station with MPI
(Message Passing Interface). A design has been done for a remote monitoring of a
1kW photovoltaic system. With this design, panel and battery voltages, temperature
and humidity can be observed remotely. An embedded system cluster consisting of
single-board computers has been used in the design. This cluster is composed of a
center single-board computer and remote node single-board computers as many as the
photovoltaic system count. Collected information is broadcasted over internet using
the single-board computer at the center.
S3I_EMB16_002 - Wearable Noncontact Armband for Mobile ECG Monitoring
System
One of the best ways to obtain health information is from an electrocardiogram
(ECG). Through an ECG, characteristics such as patients’ heartbeats, heart conditions,
and heart disease can be analyzed. Unfortunately, most available healthcare devices
do not provide clinical data such as information regarding patients’ heart activities.
Many researchers have tried to solve this problem by inventing wearable heart
monitoring systems with a chest strap or wristband, but their performances were not
feasible for practical applications. Thus, the aim of this study is to build a new system
to monitor heart activity through ECG signals. The proposed system consists of
capacitive-coupled electrodes embedded in an armband. It is considered to be a
reliable, robust, and low-power-transmission ECG monitoring system. The reliability
of this system was achieved by the careful placement of sensors in the armband.
Bluetooth low energy (BLE) was used as the protocol for data transmission; this
protocol was proposed to develop the low-power-transmission system. For robustness,
the proposed system is equipped with analysis capabilities–e.g., real-time heartbeat
detection and a filter algorithm to ignore distractions from body movements or noise
from the environment.

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S3I_EMB16_003 - UR-SolarCap: An Open Source Intelligent Auto-Wakeup
Solar Energy Harvesting System for Supercapacitor Based Energy Buffering
Energy harvesting systems that couple solar panels with supercapacitor buffers offer
an attractive option for powering computational systems deployed in “field settings,”
where power infrastructure is inaccessible. Supercapacitors offer a particularly
compelling advantage over electrochemical batteries for such settings because of their
ability to survive many more charge-discharge cycles. We share UR-SolarCap – a
versatile open source design for such a harvesting system that targets embedded
system applications requiring power in the 1–10 W range. Our system is designed for
high efficiency and controllability and, importantly, supports auto-wakeup from a
state of complete energy depletion. This paper summarizes our design methodology,
and the rationale behind our design and configuration decisions. Results from the
operation and testing of a system realized with our design demonstrate: (a) an
achievable harvester efficiency of 85%, (b) the ability to maintain sustained operation
over a two week period when the solar panel and buffer are sized appropriately, and
(c) a robust auto-wakeup functionality that resumes system operation upon availability
of harvestable energy after a period in which the system has been forced into a
dormant state because of a lack of usable energy. To facilitate the use of the system by
researchers exploring embedded system applications in environments that lack a
power infrastructure, our designs are available for download as an archive containing
design schematics, PCB files, firmware code, and a component list for assembly of the
system. Additionally, a limited number of pre-assembled kits are available upon
request.
S3I_EMB16_004 - Real-time patient health monitoring and alarming using
wireless-sensor-network
The main objective of this research is design and realization of real-time monitoring
and alarming system for patient health, especially for patients suffering from diseases
during their normal life. The proposed system has an embedded microcontroller
connected to a set of medical sensors (related to the patient case) and a wireless
communication module (Bluetooth). Each patient is considered as a node in a wireless
sensor network and connected to a central node installed at the medical center through
an internet connection. The embedded microcontroller checks if the patient health
status is going well or not by analyzing the scanned medical signals. If the analysis
results are abnormal, the embedded unit uses the patient's phone to transmit these
signals directly to the medical center. In this case, the doctor will send medical advice
to the patient to save his/her life. The implemented prototype has been tested and
calibrated with standard devices. The experimental results confirm the effectiveness
of the proposed system that is accurate in scanning, clear in monitoring, intelligent in
decision making, reliable in communication, and cheap (about 100 US$).

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S3I_EMB16_005 - Assessment of Robotic Picking Operations Using a 6 Axis
Force/Torque Sensor
This letter presents a novel architecture for evaluating the success of picking
operations that are executed by industrial robots. It is formed by a cascade of machine
learning algorithms (kNN and SVM) and uses information obtained by a 6 axis
force/torque sensor and, if available, information from the built-in sensors of the
robotic gripper. Beyond measuring the success or failure of the entire operation, this
architecture makes it possible to detect in real-time when an object is slipping during
the picking. Therefore, force and torque signatures are collected during the picking
movement of the robot, which is decomposed into five different stages that allows to
characterize distinct levels of success over time. Several trials were performed using
an industrial robot with two different grippers for picking a long and flexible object.
The experiments demonstrate the reliability of the proposed approach under different
picking scenarios since, it obtained a testing performance (in terms of accuracy) up to
99.5% of successful identification of the result of the picking operations, considering
an universe of 400 attempts.
S3I_EMB16_006 - Evaluating User Gestures in Rehabilitation from
Electromyographic Signals
One of the strategies being used over the last years to increase the user commitment
and motivation on rehabilitation systems is the use of virtual reality (VR)
environments. In addition to contributing to motivation, these systems can simulate
real life activities and provide means to measure and evaluate user performance. The
use of natural interaction devices originally conceived to the game market allowed the
development of low-cost and minimally invasive systems. With the advent of
interaction devices based on electromyography, the electromyographic signals of the
user can also be used on the natural interaction process. This work has as goal to
verify if, by using a evaluation model, is possible to evaluate user performance in real
time through gesture recognition by means of an electromyography device attached to
a rehabilitation system.
S3I_EMB16_007 - Implementation of ZigBee-VLC system to support light
control network configuration
In this paper, ZigBee-VLC Transmitter and Receiver are designed, implemented and
tested. By utilizing the ZigBee-VLC Transmitter and Receiver, commissioning
procedures for light control network configuration are simplified and commissioning
time is drastically reduced. With this configuration, lighting control network
configured to use a maximum of 216 lighting is possible. As a result of this research,
the transmitter is complete with ZigBee-VLC features implemented in the Single

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MCU without rising production costs and the 1-board solution receiver including a
ZigBee and VLC functions are implemented. In addition, as a result of the test work
using the light control app, dramatically shortening commissioning time, easy lighting
control is possible was confirmed.
S3I_EMB16_008 - Coexistence of ZigBee-Based WBAN and WiFi for Health
Telemonitoring Systems
The development of telemonitoring via wireless body area networks (WBANs) is an
evolving direction in personalized medicine and home-based mobile health. A WBAN
consists of small, intelligent medical sensors which collect physiological parameters
such as electrocardiogram, electroencephalography, and blood pressure. The recorded
physiological signals are sent to a coordinator via wireless technologies, and are then
transmitted to a healthcare monitoring center. One of the most widely used wireless
technologies in WBANs is ZigBee because it is targeted at applications that require a
low data rate and long battery life. However, ZigBee-based WBANs face severe
interference problems in the presence of WiFi networks. This problem is caused by
the fact that most ZigBee channels overlap with WiFi channels, severely affecting the
ability of healthcare monitoring systems to guarantee reliable delivery of
physiological signals. To solve this problem, we have developed an algorithm that
controls the load in WiFi networks to guarantee the delay requirement for
physiological signals, especially for emergency messages, in environments with
coexistence of ZigBee-based WBAN and WiFi. Since WiFi applications generate
traffic with different delay requirements, we focus only on WiFi traffic that does not
have stringent timing requirements. In this paper, therefore, we propose an adaptive
load control algorithm for ZigBee-based WBAN/WiFi coexistence environments,
with the aim of guaranteeing that the delay experienced by ZigBee sensors does not
exceed a maximally tolerable period of time. Simulation results show that our
proposed algorithm guarantees the delay performance of ZigBee-based WBANs by
mitigating the effects of WiFi interference in various scenarios.
S3I_EMB16_009 - ZigBee network system for observing operating activities of
work vehicles
Observing activities of working vehicles on a work site, such as a factory, is
important in regard to managing the lifetime of vehicles and achieving high
operational availability. However, it is a problem that an administrator cannot
completely grasp the activities of a working vehicle. Existing systems cannot cover a
large area, particularly in an indoor environment. A system is proposed for monitoring
operating activities of working vehicles, regardless of whether they are operating
indoors or outdoors. The system calculates the activity rate of a vehicle by analyzing
the topology of a network configured by the wireless technology ZigBee. In addition,

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it was experimentally verified that network topology and RSSI can be used to estimate
activities of working vehicles.
S3I_EMB16_010 - The Design of Building Fire Monitoring System Based on
ZigBee-WiFi Networks
With the rapid development of wireless communication technology, people's life has
undergone great changes. In recent years, the comfort and safety of the building
environment have become a universal concern. However, building fire is the greatest
threat to building safety. In consideration of the current issues on building security,
the design applies the important part, the wireless sensor network technology to
building fire safety monitoring system and establishes the wireless sensor network by
using ZigBee technology and ZigBee-WiFi gateway which transforms ZigBee
network into WiFi network, In addition, taking advantage of the ZigBee wireless
sensor network locates a fire place so that the fire information is uploaded to the
handheld terminal and the building security personnel work out the retreat and rescue
plan in time. This paper provides a new solution for building fire monitoring system.
S3I_EMB16_011 - A low complex spread spectrum scheme for ZigBee based
smart home networks
One of the biggest challenges that consumers and service providers have is
connecting a wide range of consumer electronics in a smart home environment.
Resource planning and bandwidth allocation for these networks in the license free
Industrial Scientific Medical (ISM) frequency band can not be guaranteed. In this
paper, we propose improvements for ZigBee physical layer in order to cope with
coexistence issue. A detailed MATLAB/Simulink simulator is developed to achieve
our objective. In order to balance the trade-off between multipath effects and receiver
complexity, the spreading gain of the conventional Direct Sequence Spread Spectrum
(DSSS) scheme is limited to 9dB. Unfortunately, this reduces the interference
suppression capability of spread spectrum schemes. Here, we propose a low complex
spread spectrum scheme for the ZigBee physical layer. The proposed scheme is
shown to be robust against multipath fading and interference with a low complexity.
S3I_EMB16_012 - Interference-Mitigated ZigBee-Based Advanced Metering
Infrastructure
An interference-mitigated ZigBee-based advanced metering infrastructure (AMI)
solution, namely IMM2ZM, has been developed for high-traffics smart metering
(SM). The IMM2ZM incorporates multiradios multichannels network architecture and
features an interference mitigation design by using multiobjective optimization. To
evaluate the performance of the network due to interference, the channel-swapping
time (Tcs) has been investigated. Analysis shows that when the sensitivity (PRχ) is

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less than -12 dBm, Tcs increases tremendously. Evaluation shows that there are
significant improvements in the performance of the application-layer transmission rate
(σ) and the average delay (D). The improvement figures are σ > ~300% and D > 70%
in a 10-floor building, σ > ~280 % and D > 65% in a 20-floor building, and σ >
~270% and D > 56% in a 30-floor building. Further analysis reveals that IMM2ZM
results in typically less than 0.43 s delay for a 30-floor building under interference.
This performance fulfills the latency requirement of less than 0.5 s for SMs in the
USA (Magazine of Department of Energy Communications, USA, 2010). The
IMM2ZM provides a high-traffics interference-mitigated ZigBee AMI solution.
S3I_EMB16_013 - Energy-saving IAQ monitoring ZigBee network using VIKOR
decision making method
Indoor Air Quality (IAQ) is an urgent topic nowadays. It is concluded that 90% of
human's life is spent indoor. However, it is commonly known that materials used in
construction or furniture is often detected to release Volatile organic compounds
(VOC) which affect IAQ significantly and lead to dizziness, respiratory irritation,
fatigue, asthma and allergic airway disease and even cancer. As a result, IAQ
monitoring system assists of improving IAQ, and wireless sensor network is an
efficient method for building up the system network. In this paper, a new ZigBee
network for IAQ monitoring system is designed. A Multi-criteria decision-making
method VIKOR is used to figure out the best parameters of the MAC layer and
CSMA/CA mechanism under this environment. The network designed can achieve
35% improvement of energy saving without affecting the latency and throughput
performance compared with the commonly-used TOPSIS method.
S3I_EMB16_014 - A Mobile ZigBee Module in a Traffic Control System
Time is of the essence when ambulances are utilized to save people's lives, but when
an ambulance needs to pass through a junction, its speed often must be reduced due to
traffic. This complicates situations when the patient in the ambulance needs urgent
treatment that can be administered only at a hospital. Due to the unavailability of
advanced medical procedures in an ambulance, there is the possibility for patients to
suffer a loss of life.
S3I_EMB16_015 - Configurable ZigBee-based control system for people with
multiple disabilities in smart homes
Nowadays, home appliances manufacturers are increasingly relying on wireless
sensor network and single chip embedded technologies to build smart environment.
Many existing systems are already in the market, however, they were designed
without envisioning the need of residents with special needs. This work presents a
framework that enables the integration and control of devices within a smart home

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environment for residents with disabilities. The framework supports the integration of
multiple control devices for different residents with different disabilities. Moreover,
the work addresses the safety of the users by providing warnings and notifications in
case of an emergency. A prototype was designed, implemented and tested.
S3I_EMB16_016 - Self-configuration and smart binding control on IoT
applications
The rapid development of wireless communication technology facilitates the
realization of the Internet-of-Things (IoT). Automatic configuration and smart
connection system have become relative important issue in accordance with extensive
applications of IoT, and the energy saving concepts. Therefore, this work presents the
integration of ???Automatic Configuration and Wisdom Connection System??? with
Wireless Sensor Networks (WSN), IoT and ZigBee technology, to actualize automatic
configuration based on a received signal strength indicator (Received Signal Strength
Indicator, RSSI), lighting auto-configuration area, regional allocation, and sub-areas.
The proposed ???Automatic Configuration and Wisdom Connection System???
automatically configures different lightings to the same position within in the range
???3dBm when the RSSI value varies only slightly. The system is configured to the
same lighting site within the experimental environment when the sub-area range set
???3dBm. This study presents a significant contribution to new configuration of
objects in Things (Web of Objects), context awareness control, and optimization of
network control platform.
S3I_EMB16_017 - Accurate Wireless Sensor Localization Technique Based on
Hybrid PSO-ANN Algorithm for Indoor and Outdoor Track Cycling
This paper aims to determine the distance between the mobile sensor node (i.e.,
bicycle) and the anchor node (i.e., coach) in outdoor and indoor environments. Two
approaches were considered to estimate such a distance. The first approach was based
on the traditional channel propagation model that used the log-normal shadowing
model (LNSM), while the second approach was based on a proposed hybrid particle
swarm optimization-artificial neural network (PSO-ANN) algorithm to improve the
distance estimation accuracy of the mobile node. The first method estimated the
distance according to the LNSM and the measured received signal strength indicator
(RSSI) of the anchor node, which in turn used the ZigBee wireless protocol. The
LNSM parameters were measured based on the RSSI measurements in both outdoor
and indoor environments. A feed-forward neural network type and the Levenberg-
Marquardt training algorithm were used to estimate the distance between the mobile
node and the coach. The hybrid PSO-ANN algorithm significantly improved the
distance estimation accuracy more than the traditional LNSM method without
additional components. The hybrid PSO-ANN algorithm achieved a mean absolute

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error of 0.022 and 0.208 m for outdoor and indoor environments, respectively. The
effect of anchor node density on localization accuracy was also investigated in the
indoor environment.
S3I_EMB16_018 - Design and Evaluation of an Open-Source Wireless Mesh
Networking Module for Environmental Monitoring
Wireless mesh networking extends the communication range among cooperating
multiple low-power wireless radio transceivers and is useful for collecting data from
sensors widely distributed over a large area. By integrating an off-the-shelf wireless
design, such as the XBee module, development of sensor systems with mesh
networking capability can be accelerated. This study introduces an open-source
wireless mesh network (WMN) module, which integrates the functions of network
discovery, automatic routing control, and transmission scheduling. In addition, this
design is open source in order to promote the use of wireless mesh networking for
environmental monitoring applications. Testing of the design and the proposed
networking module is reported. The proposed wireless mesh networking module was
evaluated and compared with XBee. The average package delivery ratio and standard
deviation of the proposed WMN module and the XBee are 94.09%, 91.19%, 5.14%,
and 10.25%, respectively, in a 20 node experiment. The proposed system was
demonstrated to have the advantages of low-cost combined with high reliability and
performance, and can aid scientists in implementing monitoring applications without
the complications of complex wireless networking issues.
S3I_EMB16_019 - A smart helmet for air quality and hazardous event detection
for the mining industry
A smart helmet has been developed that is able to detect of hazardous events in the
mines industry. In the development of helmet, we have considered the three main
types of hazard such as air quality, helmet removal, and collision (miners are struck
by an object). The first is the concentration level of the hazardous gases such as CO,
SO2, NO2, and particulate matter. The second hazardous event was classified as a
miner removing the mining helmet off their head. An IR sensor was developed
unsuccessfully but an off-the shelf IR sensor was then used to successfully determine
when the helmet is on the miner's head. The third hazardous event is defined as an
event where miners are struck by an object against the head with a force exceeding a
value of 1000 on the HIC (Head Injury Criteria). An accelerometer was used to
measure the acceleration of the head and the HIC was calculated in software. The
layout of the visualisation software was completed, however the implementation was
unsuccessful. Tests were successfully done to calibrate the accelerometer. PCB's that
were designed and made included a breakout board and a prototype board. A whole
software implementation was done based on Contiki operating system in order to do

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the control of the measuring of sensors and of calculations done with the measured
values. This paper presents the undertaken design detailing solutions to issues raised
in previous research.
S3I_EMB16_020 - Low-Power Wearable ECG Monitoring System for Multiple-
Patient Remote Monitoring
Many devices and solutions for remote electrocardiogram (ECG) monitoring have
been proposed in the literature. These solutions typically have a large marginal cost
per added sensor and are not seamlessly integrated with other smart home solutions.
Here, we propose an ECG remote monitoring system that is dedicated to non-
technical users in need of long-term health monitoring in residential environments and
is integrated in a broader Internet-of-Things (IoT) infrastructure. Our prototype
consists of a complete vertical solution with a series of advantages with respect to the
state of the art, considering both the prototypes with integrated front end and
prototypes realized with off-the-shelf components: 1) ECG prototype sensors with
record-low energy per effective number of quantized levels; 2) an architecture
providing low marginal cost per added sensor/user; and 3) the possibility of seamless
integration with other smart home systems through a single IoT infrastructure.
S3I_EMB16_021 - Development of a distributed disaster data and human life
sign probe system
This paper deals with a novel sensor network system designed for gathering disaster
information including physical environmental information and potential signals of
survivers. The system consists of numerous sensor probes and a central database
server. The sensor probes organize their own ZigBee network, which is managed by
the central database server. The server is connected to the Internet to be able to
provide total disaster information worldwide. In this paper, the authors introduce their
development and show some basic performance test to verify its potential usability.
S3I_EMB16_022 - Characterization of RSS variability for biobot localization
using 802.15.4 Radios
A cyber-physically organized swarm of insect biobots or biological robots can aid
first responders in search-and-rescue scenarios after natural disasters or earthquakes
by establishing an under-rubble sensor network. In such a network, the nodes are
represented by the insect biobots equipped with electronic backpacks utilizing a
system-on-chip. This application requires effective real-time localization of the
mobile sensor nodes. Radio signal strength (RSS) is a measurement of the received
signal power, and can be used in estimating the distance between two nodes, which
then can help localize the biobotic sensor nodes in the future. This paper investigates
RSS variability and its suitability for biobotic localization.

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S3I_EMB16_023 - Evaluation of Ultrasound-Based Sensor to Monitor
Respiratory and Nonrespiratory Movement and Timing in Infants
Goal: To describe and validate a noncontacting sensor that used reflected ultrasound
to separately monitor respiratory, nonrespiratory, and caretaker movements of infants.
Methods: An in-phase and quadrature (I & Q) detection scheme provided adequate
bandwidth, in conjunction with postdetection filtering, to separate the three types of
movement. The respiratory output was validated by comparing it to the electrical
activity of the diaphragm (Edi) obtained from an infant ventilator in 11 infants. The
nonrespiratory movement output was compared to movement detected by miniature
accelerometers attached to the wrists, ankles, and heads of seven additional infants.
Caretaker movement was compared to visual observations annotated in the
recordings. Results: The respiratory rate determined by the sensor was equivalent to
that from the Edi signal. The sensor could detect the onset of inspiration significantly
earlier than the Edi signal (23+/-69 ms). Nonrespiratory movement was identified
with an agreement of 0.9 with the accelerometers. It potentially interfered with the
respiratory output an average of 4.7+/-4.5% and 14.9+/15% of the time in infants not
requiring or on ventilatory support, respectively. Caretaker movements were
identified with 98% sensitivity and specificity. The sensor outputs were independent
of body coverings or position. Conclusion: This single, noncontacting sensor can
independently quantify these three types of movement. Significance: It is feasible to
use the sensor as trigger for synchronizing mechanical ventilators to spontaneous
breathing, to quantify overall movement, to determine sleep state, to detect seizures,
and to document the amount and effects of caretaker activity in infants.
S3I_EMB16_024 - Smart real-time healthcare monitoring and tracking system
using GSM/GPS technologies
Health monitoring systems have rapidly evolved recently, and smart systems have
been proposed to monitor patient current health conditions, in our proposed and
implemented system, we focus on monitoring the patient's blood pressure, and his
body temperature. Based on last decade statistics of medical records, death rates due
to hypertensive heart disease, shows that the blood pressure is a crucial risk factor for
atherosclerosis and ischemic heart diseases; thus, preventive measures should be taken
against high blood pressure which provide the ability to track, trace and save patient's
life at appropriate time is an essential need for mankind. Nowadays, Globalization
demands Smart cities, which involves many attributes and services, such as
government services, Intelligent Transportation Systems (ITS), energy, health care,
water and waste. This paper proposes a system architecture for smart healthcare based
on GSM and GPS technologies. The objective of this work is providing an effective
application for Real Time Health Monitoring and Tracking. The system will track,

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trace, monitor patients and facilitate taking care of their health; so efficient medical
services could be provided at appropriate time. By Using specific sensors, the data
will be captured and compared with a configurable threshold via microcontroller
which is defined by a specialized doctor who follows the patient; in any case of
emergency a short message service (SMS) will be sent to the Doctor's mobile number
along with the measured values through GSM module. furthermore, the GPS provides
the position information of the monitored person who is under surveillance all the
time. Moreover, the paper demonstrates the feasibility of realizing a complete end-to-
end smart health system responding to the real health system design requirements by
taking in consideration wider vital human health parameters such as respiration rate,
nerves signs ... etc. The system will be able to bridge the gap between pat- ents - in
dramatic health change occasions- and health entities who response and take actions
in real time fashion.
S3I_EMB16_025 - Indoor Blind Localization of Smartphones by Means of
Sensor Data Fusion
Locating the nodes in wireless sensor networks (WSNs) is currently a very active
area of research due to their increasing number of potential applications. Wireless
networks composed of smartphones have gained particular interest, mainly due to the
high availability of such devices. This paper presents a novel algorithm for blind
localization of commercial off-the-shelf smartphones in a WSN. The algorithm uses
acoustic signals and inertial sensors to estimate the sensor positions simultaneously.
Estimates of range and direction-of-arrival (DOA) locally obtained in each node are
combined with a maximum likelihood estimator. A tailored optimization algorithm is
also proposed to solve the DOA uncertainty problem. Our proposal obtains low
localization errors without considering any reference node nor any prior
synchronization between nodes.
S3I_EMB16_026 - Low-Overhead and High-Precision Prediction Model for
Content-Based Sensor Search in the Internet of Things
A growing number of Internet-connected sensors have already promoted the advance
of sensor search service. Accessing all available objects to find the sought sensor
results in huge communication overhead, thus a low-overhead and high-precision
prediction model (LHPM) is proposed to improve the sensor search efficiency. We
design the approximation method to lower the reporting energy cost. Then a multistep
prediction method is proposed to accurately estimate the sensor state. Furthermore, a
sensor ranking method is presented to assess the matching probabilities of sensors, so
as to effectively reduce the communication overhead of the search process. Simulation
results demonstrate the validity of the proposed prediction model in the area of
content-based sensor search.

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S3I_EMB16_027 - Preprocessing Design in Pyroelectric Infrared Sensor-Based
Human-Tracking System: On Sensor Selection and Calibration
This paper presents an information-gain-based sensor selection approach as well as a
sensor sensing probability model-based calibration process for multihuman tracking in
distributed binary pyroelectric infrared sensor networks. This research includes three
contributions: 1) choose the subset of sensors that can maximize the mutual
information between sensors and targets; 2) find the sensor sensing probability model
to represent the sensing space for sensor calibration; and 3) provide a factor graph-
based message passing scheme for distributed tracking. Our approach can find the
solution for sensor selection to optimize the performance of tracking. The sensing
probability model is efficiently optimized through the calibration process in order to
update the parameters of sensor positions and rotations. An application for mobile
calibration and tracking is developed. Simulation and experimental results are
provided to validate the proposed framework.
S3I_EMB16_028 - Lightweight Mashup Middleware for Coal Mine Safety
Monitoring and Control Automation
Recently, the frequent coal mine safety accidents have caused serious casualties and
huge economic losses. It is urgent for the global mining industry to increase
operational efficiency and improve overall mining safety. This paper proposes a
lightweight mashup middleware to achieve remote monitoring and control automation
of underground physical sensor devices. First, the cluster tree based on ZigBee
Wireless Sensor Network (WSN) is deployed in an underground coal mine, and
propose an Open Service Gateway initiative (OSGi)-based uniform devices access
framework. Then, propose a uniform message space and data distribution model, and
also, a lightweight services mashup approach is implemented. With the help of
visualization technology, the graphical user interface of different underground
physical sensor devices could be created, which allows the sensors to combine with
other resources easily. Besides, four types of coal mine safety monitoring and control
automation scenarios are illustrated, and the performance has also been measured and
analyzed. It has been proved that our lightweight mashup middleware can reduce the
costs efficiently to create coal mine safety monitoring and control automation
applications.
S3I_EMB16_029 - Improving the Locating Precision of an Active WIFI RFID
System to Obtain Traceability of Patients in a Hospital
It is a challenge to integrate RFID technology into the healthcare sector to increase
security by obtaining traceability of patients during their hospital stay. In this case,
RFID provides arange of technical architectures for implementing an RFID system.

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The installation or use of the WIFI network available in a hospital is a possible
element in system design since a priori with a correct configuration of RFID
components, excellent results in location accuracy can be obtained over other
architectures available in the market. The accuracy of RFID Aeroscout WIFI system
can be improved with the installation of exciters. These are components that assist the
localisation engine in calculating the location of an active RFID tag WIFI. The
precision offered by the localisation engine depends on multiple configurable
parameters set by the engineers responsible for the design and development of an
active RFID WIFI system.
S3I_EMB16_030 - Joint access point and user localization using unlabeled WiFi
RSS data
This paper investigates the problem of joint estimation of a pedestrian user path and
the available WiFi access point locations. The observations are limited to unlabeled
WiFi received signal strength (RSS) values. The problem is formed as a partially
observable Markov decision process and RSS gradients are integrated to estimate and
update the user locations along the path. The RSS data is modeled as a Gaussian
process and gradient vectors are updated for each step based on the motion dynamics.
Realistic assumptions and constraints are introduced to model the user's movement
and reduce the computational complexity.
S3I_EMB16_031 - Water Level Meter for Alerting Population about Floods
The most important thing immediately before, during and after a disaster occurs is the
dissemination of information, a deployment of devices enabled by IoT (Internet of
Things) could bring benefits in terms of giving to people information opportunely for
making decisions in face of this disaster. In this paper, we present a sensor to measure
water level in rivers, lakes, lagoons and streams. For such purpose and to prove our
concept, we designed a pilot project through a micro-model that is constructed with a
water level measurement sensor based on a simple open circuit that closes when in
contact with water and experimentally tested into a water container under a controlled
environment. This micro-model is performed on the basis of a programmable
electronic board (Netduino Plus 2), an electronic circuit connected to electrical
resistances that are located at a specific height, within a water container, when the
water level rises and reaches the resistors, varies the impedance, this shows the actual
water level and so on for different heights. The information from water level sensor is
transmitted via WiFi to a laptop, then this information is also seen in smartphones,
where users can see the water level in rivers. Finally, the micro-model is tested by
experimental tests under a controlled environment and satisfactory results are
obtained.

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S3I_EMB16_032 - Brain-controlled devices: the perception-action closed loop
Future neuroprosthetics will be tightly coupled with the user in such a way that the
resulting system can replace and restore impaired upper limb functions because
controlled by the same neural signals than their natural counterparts. However, robust
and natural interaction of subjects with sophisticated prostheses over long periods of
time remains a major challenge. To tackle this challenge we can get inspiration from
natural motor control, where goal-directed behavior is dynamically modulated by
perceptual feedback resulting from executed actions. Current brain-computer
interfaces (BCI) partly emulate human motor control as they decode cortical
correlates of movement parameters -from onset of a movement to directions to
instantaneous velocity- in order to generate the sequence of movements for the
neuroprosthesis. A closer look, though, shows that motor control results from the
combined activity of the cerebral cortex, subcortical areas and spinal cord. This
hierarchical organization supports the hypothesis that complex behaviours can be
controlled using the low-dimensional output of a BCI in conjunction with intelligent
devices in charge to perform low-level commands. A further component that will
facilitate intuitive and natural control of motor neuroprosthetics is the incorporation of
rich multimodal feedback and neural correlates of perceptual cognitive processes
resulting from this feedback. As in natural motor control, these sources of information
can dynamically modulate interaction.
S3I_EMB16_033 - Experimental investigation of remote control via Android
smart phone of arduino-based automated irrigation system using moisture
sensor
Climate change because of the greenhouse effect has been authenticated. Fallouts
like the 2015 Chennai floods suggest techniques like precision agriculture that
includes automation in the irrigation system are important. This paper suggests an
economical and easy-to-use arduino-based automated irrigation system that utilizes
the Android smart phone for remote control. The system design includes a soil
moisture sensor that provides a voltage signal proportional to the moisture content in
the soil which is compared with a predetermined threshold value obtained by
sampling of various soils and specific crops. The outcome of the comparison is that
appropriate data are fed to the arduino uno processor. The arduino is linked wirelessly
via the HC-05 module to an Android smart phone. The data received by the Android
smart phone from the arduino is displayed on the User Interface (UI) (S2 terminal
application). The UI in the Android smart phone allows the user easy remote control
of the irrigation drive system that involves switching, on and off, of the drive motor
by the arduino, wired to its controller, based on commands from the android smart
phone. Studies conducted on a laboratory prototype suggest that the design is viable
and can be easily adopted for real time application.

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S3I_EMB16_034 - MAGIC: Model-Based Actuation for Ground Irrigation
Control
Lawns make up the largest irrigated crop by surface area in North America, and
carries with it a demand for over 9 billion gallons of freshwater each day. Despite
recent developments in irrigation control and sprinkler technology, state-of-the-art
irrigation systems do nothing to compensate for areas of turf with heterogeneous
water needs. In this work, we overcome the physical limitations of the traditional
irrigation system with the development of a sprinkler node that can sense the local soil
moisture, communicate wirelessly, and actuate its own sprinkler based on a centrally-
computed schedule. A model is then developed to compute moisture movement from
runoff, absorption, and diffusion. Integrated with an optimization framework, optimal
valve scheduling can be found for each node in the space. In a turf area covering over
10,000ft2, two separate deployments spanning a total of 7 weeks show that MAGIC
can reduce water consumption by 23.4% over traditional campus scheduling, and by
12.3% over state-of-the- art evapotranspiration systems, while substantially improving
conditions for plant health. In addition to environmental, social, and health benefits,
MAGIC is shown to return its investment in 16-18 months based on water
consumption alone.
S3I_EMB16_035 - Potential for improving green roof performance through
artificial irrigation
Historically extensive green roofs were designed for natural precipitation with a
plant selection focusing on hardy succulents such as sedums that can survive harsh,
water stressed conditions. Although this seems a convenient solution to establish and
maintain a green roof system, at a much broader level this does not optimize the
functions and performance of the green roof. In this paper the influence of irrigation
on green roof functions and performance is presented for an extensive green roof by
an extensive literature study. Green roof energy saving potential under Sri Lankan
climatic conditions is significant. The average water retention of green roof substrate
under different climatic zone conditions in Sri Lankan context is simulated with
hypothetical twelve extensive green roof types. Results justify the artificial irrigation
requirement and provide key directions to develop water balance model considering
locational factors to maintain set soil moisture target.
S3I_EMB16_036 - Dual Sink Efficient Balanced Energy Technique for
Underwater Acoustic Sensor Networks
Underwater Acoustic Sensor Networks are considered to provide efficient
monitoring tasks in aquatic environment but due to limited battery resource of sensor
nodes, network lifetime collapses. Energy balancing is the major issue in low network

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lifetime. High energy consumption creates energy holes and ultimately leads to
shorter network lifetime. Therefore, energy consumption must be balanced to increase
network life time. To overcome these concerns a technique should be designed that
minimizes the energy consumption and prolong network lifetime. This paper presents
a Dual Sink Efficient and Balanced Energy consumption Technique (DSEBET) for
UASNs. DSEBET overcomes the problem of limited network lifetime and high
energy consumption over long distance. Dual sinks underwater model is established.
DSEBET first establishes links between nodes on the basis of their optimum distance
value and then picks relay nodes on the basis of their minimum distance "Nj" value
for the transmission of data. In the data transmission phase every nodes have equal
energy levels numbers (ELNs). Long distance nodes from one sink will share their
data to other sink if come in range of sink otherwise they will establish a multi hop
path for transmission of data to the respective sink.
IOT BASED IEEE PROJECTS- 2016-2017
ECE – EMBEDDED DEPARTMENT
S3I_IOT16_001 - IoT Healthcare Analytics: The Importance of Anomaly Detection
Healthcare data is quite rich and often contains human survival related information.
Analyzing healthcare data is of prime importance particularly considering the immense potential
of saving human life and improving quality of life. Furthermore, IoT revolution has redefined
modern health care systems and management. IoT offers its greatest promise to deliver excellent
progress in healthcare domain. In this talk, proactive healthcare analytics specifically for cardiac
disease prevention will be discussed. Anomaly detection plays a prominent role in healthcare
analytics. In fact, the anomalous events are to be accurately detected with low false negative
alarms often under high noise (low SNR) condition. An exemplary case of smartphone based
cardiac anomaly detection will be presented..
S3I_IOT16_002 - Effective ways to use Internet of Things in the field of medical
and smart health care
The recent advancements in technology and the availability of the Internet make
it possible to connect various devices that can communicate with each other and share
data. The Internet of Things (IoT) is a new concept that allows users to connect
various sensors and smart devices to collect real-time data from the environment.
However, it has been observed that a comprehensive platform is still missing in the e-
Health and m-Health architectures to use smartphone sensors to sense and transmit
important data related to a patient's health. In this paper, our contribution is twofold.
Firstly, we critically evaluate the existing literature, which discusses the effective
ways to deploy IoT in the field of medical and smart health care. Secondly, we
propose a new semantic model for patients' e-Health. The proposed model named as
`k-Healthcare' makes use of 4 layers; the sensor layer, the network layer, the Internet

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layer and the services layer. All layers cooperate with each other effectively and
efficiently to provide a platform for accessing patients' health data using smart
phones.
S3I_IOT16_003 - A conceptual framework for IoT-based healthcare system
using cloud computing
Internet of Things (IoT) envisions a future in which anything/anyone/anyservice
can be linked by means of appropriate information and communication technologies
which will bring technological revolution in the fields of domestics, smart homes,
healthcare systems, goods monitoring and logistics. This paper presents the
applications of IoT and addresses some essential parameters and characteristics of
each of the applications of IoT. In this paper, we have deeply explored the role of IoT
in healthcare delivery and its technological aspects that make it a reality and examine
the opportunities. A cloud based conceptual framework has been proposed which will
be beneficial to the healthcare industry implementing IoT healthcare solutions.
S3I_IOT16_004 - The Internet of Things in Healthcare: Potential Applications
and Challenges
Exciting new applications of Internet of Things (IoT) technology are arising,
particularly in healthcare, where the leveraging effects can significantly improve
patients' well-being while alleviating the problem of scarce resources. But the hype
around these applications far outpaces the reality. Furthermore, there is a real risk that
these leveraging technologies will disassociate caregivers from patients, potentially
resulting in a loss of caring. In this article, the authors review some of the most
promising applications for IoT in healthcare and the significant challenges ahead.
S3I_IOT16_005 - IoT based smart healthcare kit
The paper presents the design and implementation of an IOT-based health
monitoring system for emergency medical services which can demonstrate collection,
integration, and interoperation of IoT data flexibly which can provide support to
emergency medical services like Intensive Care Units (ICU), using a INTEL
GALILEO 2ND generation development board. The proposed model enables users to
improve health related risks and reduce healthcare costs by collecting, recording,
analyzing and sharing large data streams in real time and efficiently. The idea of this
project came so to reduce the headache of patient to visit to doctor every time he need
to check his blood pressure, heart beat rate, temperature etc. With the help of this
proposal the time of both patients and doctors are saved and doctors can also help in
emergency scenario as much as possible. The proposed outcome of the project is to
give proper and efficient medical services to patients by connecting and collecting
data information through health status monitors which would include patient's heart

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rate, blood pressure and ECG and sends an emergency alert to patient's doctor with his
current status and full medical information.
Electrical / Power Electronics / Power
Systems / Power Factor Correction -
Project Titles 2016-2017
S3I_PED16_001 - A Compact Coupled Inductor for Interleaved Multiphase DC-
DC Converters
A compact coupled inductor structure is proposed for interleaved multiphase
synchronous buck converters used to power computer processors and memories that
require high current and fast current flew rate. The new coupled inductor structure
reduces the winding resistor power loss and makes it possible to utilize Ferrite
magnetic material with low core loss. In the letter, several proposed converter
implementations to achieve inverse inductor coupling are illustrated, and inductance
and coupling coefficient variations are studied through a simplified reluctance model
and Maxwell magnetic simulation. The inductor structure not only can be extended to
multiple phases but also can be simplified to single phase. The operation of the
inductor is experimentally verified in a two-phase synchronous buck converter at
switching frequency of one Mega-Hertz.
S3I_PED16_002 - A Multi-Level Converter with a Floating Bridge for Open-
Ended Winding Motor Drive Applications
This paper presents a dual three phase open end winding induction motor
drive. The drive consists of a three phase induction machine with open stator phase

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windings and dual bridge inverter supplied from a single DC voltage source. To
achieve multi-level output voltage waveforms a floating capacitor bank is used for the
second of the dual bridges. The capacitor voltage is regulated using redundant
switching states at half of the main dc link voltage. This particular voltage ratio (2:1)
is used to create a multi-level output voltage waveform with three levels. A modified
modulation scheme is used to improve the waveform quality of this dual inverter. This
paper also compares the losses in dual inverter system in contrast with single sided
three-level NPC converter. Finally, detailed simulation and experimental results are
presented for the motor drive operating as an open loop v/f controlled motor drive and
as a closed loop field oriented motor controller.
S3I_PED16_003 - Adapted NSPWM for Single DC-Link Dual-Inverter Fed
Open-End Motor with Negligible Low-Order Harmonics and Efficiency
Enhancement
In this paper, the Near-State PWM (NSPWM), adapted to be implemented in
dual-VSI fed open-end motor, is proposed with the aim of mitigating low-order
harmonics (which lead to current THD minimization). Two proposed methods are
studied in detail such as: (a) fixing Phase Angle Displacement (PAD) between two
Voltage Source Inverters (VSIs) to 120° while adjusting Modulation Index (MI); and
(b) fixing MI to the pre-determined value (wherein low-order harmonics are highly
mitigated) while adjusting PAD. Furthermore, the proposed approaches enhance
efficiency by limiting the number of commutations within switching interval. The
paper also presents the mathematical approaches to accurately determine low-order
harmonic components and switching losses for dual-VSI structure. The experimental
setup, including dual-VSI and open-end induction motor, is assembled in the
laboratory to evaluate performance of the proposed method. Finally, the simulation
results, carried out in the MATLAB/Simulink environment, are found to be in a close
agreement with experimental data.
S3I_PED16_004 - Advanced Design and Operation Consideration for Close-
connected Winding Permanent Magnet Brushless DC Machine
Advanced design and operation consideration for the close-connected winding
permanent-magnet (PM) brushless DC machine [1] is proposed in this paper,
including circulating current elimination, fault tolerant control for the breakdown of
power electronic switch, and sensorless control method. With the finite-element
analysis (FEA), the influence of machine design parameters on circulating current are
investigated. By properly controlling the status of power electronic switches, the coils
being connected into the equivalent circuit can be changed to avoid the failure switch.

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The sensorless control is explored by detecting the zero-crossing point of
electromotive force (EMF).
S3I_PED16_005 - An Active Cross-Connected Modular Multilevel Converter
(AC-MMC) for Medium-Voltage Motor Drive
This paper presents an active cross-connected modular multilevel converter
(AC-MMC) based on series-connected half-bridge modules. It is intended for
completely enhancing the performance of a medium-voltage motor drive system in the
full speed range from standstill to rated speed under all load conditions. The proposed
AC-MMC circuit is characterized by the cross connection of upper and lower arm
middle taps through a branch of series-connected half-bridge converters, which have
an identical voltage and current rating with the sub-modules in upper and lower arms.
This cross-connected branch provides a physical power transfer channel for upper and
lower arms. By properly controlling the amount of high-frequency current flowing
through the cross-connected branch, the power balance between the upper and lower
arms is achieved even at a zero/low motor speed under constant torque condition.
Meanwhile, no common-mode voltage is introduced in the whole speed range. A
control strategy with focus on submodule capacitor voltage control is also proposed in
this paper to guarantee the normal converter operation. Simulation results obtained
from a 4160-V 1-MW model verify the feasibility of the proposal. Experiments on a
downscaled prototype also confirm the validity of the novel circuit and the associated
control strategy.
S3I_PED16_006 - Adaptive Maximum Power Point Tracking Control Algorithm
for Wind Energy Conversion Systems
This paper presents an adaptive maximum power point tracking (MPPT)
algorithm for small-scale wind energy conversion systems (WECSs) to harvest more
energy from turbulent wind. The proposed algorithm combines the computational
behavior of hill climb search, tip speed ratio, and power signal feedback control
algorithms for its adaptability over wide range of WECSs and fast tracking of
maximum power point. In this paper, the proposed MPPT algorithm is implemented
by using buck– boost featured single-ended primary inductor converter to extract
maximum power from full range of wind velocity profile. Evaluation of the proposed
algorithm is done on a laboratory-scaled dc motor drive-based WECS emulator.
TMS320F28335, 32-bit floating point digital signal controller, is used to execute the
control schemes of the in-lab experimental setup. Experimental results show that
tracking capability of the proposed algorithm under sudden and gradual fluctuating
wind conditions is efficient and effective.

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S3I_PED16_007 - A Quasi-Z-source Integrated Multi-port Power Converter with
Reduced Capacitance for Switched Reluctance Motor Drives
This paper presents a quasi Z-source integrated multiport converter (ZIMPC)
for switched reluctance motor (SRM) drives to reduce the dc link capacitance. In
conventional SRM drives, employing multi-phase asymmetrical H-bridge (ASHB)
topology, large capacitors are necessary to absorb the transient energy during phase
current commutation. However, electrolytic capacitors would affect the lifetime, cost
and power density of the drive system. With switch multiplexing technique, a Z-
source integrated multiport power converter is derived to achieve power ripple
reduction using relatively small capacitance. Corresponding control method is
designed and developed for the proposed SRM drive. Also, the ZIMPC can boost the
equivalent phase exciting voltage and widen the constant power speed range (CPSR).
At last, simulation and experimental results verify the feasibility of the proposed
ZIMPC and its superior performance with smaller capacitance as compared with
ASHB.
S3I_PED16_008 - A Novel Technique for Two-Phase BLDC Motor to Avoid the
Demagnetization
Conventional permanent magnet motors operate in both magnetizing (pull)
process and reversible demagnetizing (push) process on the recoil line of magnets.
Therefore, thin surface permanent magnets may easily undergo a risk of
demagnetization at the push process under certain fault conditions, which leads to
deterioration of motor performance. Thus, thick magnets, whereas contributing the
significantly high cost, are usually used to minimize this risk in the permanent magnet
motors. In this paper, a novel operation technique, that involves only the pull process,
has been proposed for a unique design of two-phase brushless DC (BLDC) motor to
avoid the irreversible demagnetization of the magnets. The motor, operated only in the
pull process, is kept away from the push process of the operation. Therefore, the
motor sustains its initial magnetic operating point above the knee point during the
normal operation as well as under the short circuit fault conditions. Finite element
analysis is performed to validate the concept of the proposed technique.
S3I_PED16_009 - An Improved Model Predictive Control Scheme for the PWM
Rectifier-Inverter System Based on Power-Balancing Mechanism
The DC-link voltage fluctuation caused by the change of working state of the
load motor has been one of the key issues in the PWM rectifier-inverter system. In
this study, an improved model predictive control (MPC) scheme is proposed to
address this problem. The MPC is applied to both the rectifier stage and the inverter
stage in the system. Direct power control is used in the rectifier stage and the direct
torque control is employed in the inverter stage, with the key novelty of the active

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power reference values being defined by both real-time and periodic compensation
power based on the system-level power balance model. Meanwhile, a MPC algorithm
based on a two-step prediction is introduced to compensate for the delay of a digital
controller. Comparison has been conducted between the proposed scheme and three
other methods. Simulation and experimental results show that the proposed control
scheme exhibits good performance in both the rectifier stage and the inverter stage
with improved dynamic response and suppressed voltage fluctuation of the DC-link
voltage.
S3I_PED16_0010 - Dual Inverter Fed Pole-Phase Modulated Nine-Phase
Induction Motor Drive with Improved Performance
Typical value of rated phase voltage of pole phase modulated multiphase
induction motor (PPMMIM) drives with wider speed range is in the order of few
hundreds of kilo volts. This high value of phase voltage for high power density
applications results in higher dc link voltage requirement and switch voltage rating of
twolevel multiphase inverter. Further, using multiphase space vector pulse width
modulation (SVPWM) yields less dc link utilization. Methods to increase dc link
utilization using SVPWM with offset value of third harmonic order introduces
dominant lower order harmonic currents into phase windings. One more major
problem in high pole mode of PPMMIMs is higher torque pulsation due to decrease in
phase number. To address these problems this paper proposes a dual inverter based
multilevel voltage excitation scheme for nine-phase PPMMIM with 1:3 speed ratio. In
four-pole mode a simple phase grouping technique to eliminate lower order harmonic
currents in the phase windings is proposed. In addition each inverter feeding these
phase groups is modulated using carrier based three-phase SVPWM to achieve higher
dc link utilization. This paper also proposes a multilevel voltage generation scheme
for twelve-pole mode of operation using carrier phase shifted PWM for inherently
available equal voltage profile coils (EVPCs) with the same dual inverter structure.
The torque ripple using phase shifted carriers PWM and single carrier PWM are
compared. Finite element method (FEM) model of ninephase PPMMIM is developed
in Ansys Maxwell twodimension (2-D) and is co-simulated with three threephase dual
inverters in Simplorer environment. Experimental validation is done for linear and
over modulation case on 9ФIM fed from three three-phase dual inverters controlled
using Spartan 6 field programmable gate array (FPGA) board programed in VHDL.
S3I_PED16_0011 - Extremely Sparse Parallel AC-Link Universal Power
Converters
Parallel ac-link universal power converters are a relatively new class of power
converters that can be configured as dc-dc, dc-ac, ac-dc, and ac-ac. These converters
are extensions of a buck-boost converter in which the current of the inductor is

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alternating and the input and output can have any number of phases with any forms,
voltage amplitude, or frequency. By placing a small capacitor in parallel with the link
inductor all the switches can benefit from soft switching. The main limitation of the
parallel ac-link universal power converter is its large number of switches. A three-
phase ac-ac configuration requires 24 unidirectional switches. This paper proposes
two topologies based on the parallel ac-link universal power converters that
significantly reduce the number of switches. One of the proposed topologies reduces
the number of switches in a three-phase ac-ac configuration to 16. The other topology
reduces the number of switches to 10. The latter can offer galvanic isolation with only
a single-phase high frequency transformer. This paper presents the principles of the
operation of the proposed topologies and evaluates them through simulation and
experiments.
S3I_PED16_0012 - Energy Consumption of Geared DC Motors in Dynamic
Applications: Comparing Modeling Approaches
In recent years, many works have appeared which present novel mechanical
designs, control strategies or trajectory planning algorithms for improved energy
efficiency. The actuator model is an essential part of these works, since the
optimization of energy consumption strongly depends of the accuracy of this model.
Nevertheless, various authors follow very different approaches, often neglecting
speed- and load-dependent losses and inertias of components such as the motor and
the gearbox. Furthermore, there is no consensus on how negative power affects power
consumption. Some authors calculate energy consumption by integrating the electrical
power entirely, by integrating its absolute value, or by integrating only positive
power. This paper assesses how well commonly used models succeed in predicting
the energy consumption of an 80 W geared DC motor performing a dynamic task, by
comparing the results they produce to experimental baseline measurements.
S3I_PED16_0013 - Derivation of Dual-Switch Step-Down DC/DC Converters
with Fault-Tolerant Capability
This letter presents a graph theoretic approach to deriving a family of dual-
switch step-down dc/dc converters with fault-tolerant capability. The constraints set in
the derivation process ensure minimum additional component is used to achieve fault-
tolerant operation. The operation of converters derived is flexible. Under normal
operating conditions, one of the two switches can serve as a main switch to control the
power flow (i.e. single-switch converter operation) and the other switch is in stand-by
mode. When a fault occurs on the main switch, the other switch will be activated to
provide an alternate current path to continue converter operation and maintain output
regulation. The fault-tolerant converters are derived by integrating a buck converter
with a buck-boost converter. They share all the components except for the power

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switches. Due to different duty cycles required between the two operating conditions,
a feedback controller is necessary to adjust the duty cycle for tight output regulation.
The derivation procedure and experimental results on fault occurence are reported.
The converter derivation approach is able to identify reported topologies and can be
extended to synthesize other topologies with fault-tolerent capability.
S3I_PED16_0014 - Analysis of the Integrated SEPIC-Flyback Converter as a
Single-Stage Single-Switch Power-Factor-Correction LED Driver
This paper proposes a new isolated single-stage single-switch power-factor-
correction (S4 PFC) driver for supplying light emitting diodes (LEDs) without
electrolytic capacitor. In the proposed LED driver, the switch turns on under zero
current switching (ZCS) condition. Also, it turns on at a voltage less than its nominal
voltage stress and, therefore, the switch capacitive turn-on loss decreases too much
extent. The leakage energy is absorbed, so there are no voltage spikes across the
switch when the switch turns off. In this paper, operating principles of the proposed
driver are discussed and design considerations are presented. Also, a laboratory
prototype for supplying a 21W/30V LED module from 220Vrms/50Hz AC mains is
implemented and experimental results are presented to verify the theoretical analysis.
S3I_PED16_0015 - Design and Implementation of a Novel Multilevel DC-AC
Inverter
In this paper, a novel multilevel DC-AC inverter is proposed. The proposed
multilevel inverter generates seven levels AC output voltage with the appropriate gate
signals design. Also, the low pass filter is used to reduce the total harmonic distortion
of the sinusoidal output voltage. The switching losses and the voltage stress of power
devices can be reduced in the proposed multi-level inverter. The operating principles
of the proposed inverter and the voltage balancing method of input capacitors are
discussed. Finally, a laboratory prototype multilevel inverter with 400 V input voltage
and output 220 Vrms /2 kW is implemented. The multilevel inverter is controlled with
sinusoidal pulse-width modulation (SPWM) by TMS320LF2407 digital signal
processor (DSP). Experimental results show that the maximum efficiency is 96.9%
and the full load efficiency is 94.6%.
S3I_PED16_0016 - BLDC Motor Driven Solar PV Array Fed Water Pumping
System Employing Zeta Converter
This paper proposes a simple, cost effective and efficient brushless DC
(BLDC) motor drive for solar photovoltaic (SPV) array fed water pumping system. A
zeta converter is utilized in order to extract the maximum available power from the
SPV array. The proposed control algorithm eliminates phase current sensors and
adapts a fundamental frequency switching of the voltage source inverter (VSI), thus

S3 INFOTECH +91 988 48 48 198
avoiding the power losses due to high frequency switching. No additional control or
circuitry is used for speed control of the BLDC motor. The speed is controlled
through a variable DC link voltage of VSI. An appropriate control of zeta converter
through the incremental conductance maximum power point tracking (INC-MPPT)
algorithm offers soft starting of the BLDC motor. The proposed water pumping
system is designed and modeled such that the performance is not affected under
dynamic conditions. The suitability of proposed system at practical operating
conditions is demonstrated through simulation results using MATLAB/ Simulink
followed by an experimental validation.
S3I_PED16_0017 - Fault-tolerant Inverter for High-speed 3-Phase BLDC Drives
in Aerospace Applications
The fault tolerant control of BLDC motor is of great importance for its
continuous operating capacity even under the faulty situation. Our proposed work
consists of a fault tolerant topology composed of an additional phase leg which
contains 6 Triacs act as fault Switching Circuit and a fault protective circuit for the
high-speed low-inductance BLDC motor. This fault protective circuit consists of
Mosfet gate that is driven by using PWM technique. Input DC voltage is given to
Buck Boost converter and it working is if the voltage level less than the required
voltage level converter act as boost converter and produce required output. If the Input
voltage level is more converters reduces the voltage obtained from the converter.
Buck Boost converter having switches (Mosfet) which used to isolate fault switches.
The output obtained from fault protector circuit is inverted into 3-Phase DC Output.
This 3-Phase Bridge consists of 6 Mosfet which is driven by Digital Pulses. Based on
this pulses 3-Phase motor rotation happens either clockwise or anticlockwise
direction. Speed of 3-Phase motor is controlled by Pulse Width Modulation and motor
rotation varies based on percentage of duty cycle that we are giving to it. The method
can achieve safe isolation and reconfiguration to avoid the secondary fault caused by
direct switch of the redundant switch and the faulty switch after the fault diagnosis
process.
S3I_PED16_0018 - Integrated DC-DC Converter Design for Electric Vehicle
Powertrains
In this paper, an integrated, reconfigurable DC-DC converter for plugin and
hybrid Electric Vehicles (EV) is proposed. The converter integrates functionality for
both EV powertrain and charging operation into a single unit. During charging, the
proposed converter functions as a DAB converter, providing galvanic isolation. For
powertrain operation, the converter functions as an interleaved boost converter.
During light load powertrain operation, the efficiency of the converter can be further
improved by employing the integrated DAB. The proposed integrated converter does

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not require any extra relays or contactors for charging and powertrain operation. By
using such integration, the overall volume and weight of the power electronics
circuits, passives and associated cooling system can be improved. In addition, the
power flow efficiency from EV battery to the high voltage DC bus for the motor
inverter can be improved. The experimental results of the prototype are presented to
verify the functionality of the proposed converter.
S3I_PED16_0019 - High Step-Up/Step-Down Soft-Switching Bidirectional DC-
DC Converter with Coupled-Inductor and Voltage Matching Control for Energy
Storage Systems
A soft-switching bidirectional DC-DC converter (BDC) with a coupled-
inductor and a voltage doubler cell is proposed for high step-up/step-down voltage
conversion applications. A dual-active half-bridge (DAHB) converter is integrated
into a conventional buck-boost BDC to extend the voltage gain dramatically and
decrease switch voltage stresses effectively. The coupled inductor operates not only as
a filter inductor of the buck-boost BDC, but also as a transformer of the DAHB
converter. The input voltage of the DAHB converter is shared with the output of the
buck-boost BDC. So PWM control can be adopted to the buck-boost BDC to ensure
that the voltage on the two sides of the DAHB converter is always matched. As a
result, the circulating current and conduction losses can be lowered to improve
efficiency. Phase-shift control is adopted to the DAHB converter to regulate the
power flows of the proposed BDC. Moreover, zero-voltage-switching is achieved for
all of the active switches to reduce the switching losses. The operational principles
and characteristics of the proposed BDC are presented in detail. The analysis and
performance have been fully validated experimentally on a 40-60V/400V 1kW
hardware prototype.
S3I_PED16_0020 - On The Robust Control of Parallel-Cascade DC/DC Buck
Converter
This paper presents the design and simulation of robust control for DC/DC
multi-converter based on terms like differential flatness and active disturbance
rejection. The main goal of the control law is to obtain a regulated output voltage for
each converter in cascade, and a balance of currents for parallel converters. The
controller must actively reject the endogenous and exogenous disturbances, i.e.
maintaining a robust output. The effectiveness of the proposed controller was verified
by computer simulations using MATLAB/Simulink.
S3I_PED16_0021 - Single-Phase Grid Connected Motor Drive System with DC-
link Shunt Compensator and Small DC-link Capacitor

S3 INFOTECH +91 988 48 48 198
The single-phase diode rectifier system with small DC-link capacitor shows
wide diode conduction time and it improves the grid current harmonics. By shaping
the output power, the system meets the grid current harmonics regulation without any
power factor corrector or grid filter inductor. However, the system has torque ripple
and suffers efficiency degradation due to the insufficient DC-link voltage. To solve
this problem, this paper proposes the DC-link shunt compensator (DSC) for small
DC-link capacitor systems. DSC is located on DC-node parallel and operates as the
voltage source, improving the system performances. This circuit helps the grid
current-shaping control during grid-connection time, and reduces the flux-weakening
current by supplying the energy to the motor during grid-disconnection time. This
paper presents a power control method and the design guideline of DSC. The
feasibility of DSC is verified by simulation and experimental results.
S3I_PED16_0022 - High Performance Solar MPPT Using Switching Ripple
Identification Based on a Lock-In Amplifier
Photovoltaic (PV) power converters and Maximum Power Point Tracking
(MPPT) algorithms are required to ensure maximum energy transfer between the PV
panel and the load. The requirements for the MPPT algorithms have increased over
the years - the algorithms are required to be increasingly accurate, fast, and versatile,
while reducing the intrusiveness on the overall performance of the PV panel and
converter. The family of Hill-climbing algorithms such as Incremental Conductance
(InCond) and Perturb and Observe (P&O) have gained popularity given their
simplicity and accuracy, but they require the injection of a perturbation that changes
the operating point even in steady-state and are prone to errors during changing
environmental conditions. In recent literature, the use of the switching ripple has been
proposed to replace the perturbation in the hill-climbing algorithms given its inherent
presence in the system and speed. The constant work towards smaller and faster
ripples presents challenges to the signal detection involved in this kind of algorithm.
This paper develops and implements a new InCond MPPT technique based on
switching ripple detection using a digital Lock-In Amplifier (LIA) to extract the
amplitude of the oscillation ripple even in the presence of noise. The use of this
advanced technique allows to push forward the reduction of the ripple in order to
virtually eliminate the oscillation in steady-state maximizing the efficiency. The
accurate detection allows for adaptive-step features for fast tracking of changing
environmental conditions while keeping the efficiency at maximum during the steady-
state. Detailed mathematical analysis of the proposed technique is provided. Overall,
the use of the proposed LIA allows to push the reduction of the ripple even more
while keeping accuracy and delivering superior performance. Simulations and
experimental results are provided for the proposed technique and the InCond
technique in order to validate the proposed approach.

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S3I_PED16_0023 - Zero-sequence Current Suppression for Open-end Winding
Induction Motor Drive with Resonant Controller
Open-end winding topology with a common DC source has path for zero-
sequence current. Zero-sequence current needs to be suppressed because it does not
contribute to the drive torque but has harmful effects such as loss and torque ripple.
Both inverter and motor have sources of the zero-sequence component. The zero-
sequence source on the inverter side is the voltage error due to dead time in switching.
The zero-sequence source on the motor side is the zero-sequence component of the
back EMF which consists of the third harmonic. In this paper, the two zero-sequence
sources are investigated both theoretically and experimentally for an open-end
winding induction motor drive system, and a method to suppress the zero-sequence
current suppression is proposed. The voltage error is compensated by feedforward to
the reference voltage. The zero-sequence component of the back EMF is compensated
by a proportional and resonant controller because its frequency is known while its
amplitude and phase offset are unknown.
S3I_PED16_0024 - High Performance Model Predictive Technique for MPPT of
Gird-tied Photovoltaic System Using Impedance-Source Inverter
This paper presents a maximum power point tracking (MPPT) method using
model predictive technique for a grid-tied photovoltaic harvesting energy system. A
single-stage grid-tied Z-source inverter is used for extracting the maximum available
power and feeding the power to the grid. The proposed technique predicts the future
behavior of the photovoltaic side voltage and current by using a digital observer
which estimates the parameters of the photovoltaic module. The proposed method
features simultaneously reduction in oscillation around maximum power point (MPP)
and fast convergence by adaptively changing the perturbation size using the predicted
model of the system. The experimental results demonstrate fast tracking response
under dynamic weather condition, small steady state error, small oscillation around
MPP at steady state, and high MPPT efficacy for wide range of solar irradiance levels.
Additionally, the grid side current total harmonics distortion (THD) meets the IEEE-
519 standards.
S3I_PED16_0025 - New Equivalent Circuit of the IPM-type BLDC Motor for
Calculation of Shaft Voltage by Considering Electric and Magnetic Fields
Fast switching and common-mode voltage of the space vector pulse width
modulation inverter output create several parasitic capacitances according to the
geometry of the motor. The windings emit the electric and magnetic flux inside of the
motor. They create capacitance links and electromotive force (EMF) in the shaft,
respectively. Those capacitance links and EMF generate a current through two

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bearings of the shaft and directly reduces the bearing lifetime. In this study, we
propose a new design of equivalent circuit taking into account all parasitic capacitor
components.
S3I_PED16_0026 - Grid Current Shaping Method with DC-link Shunt
Compensator for Three-Phase Diode Rectifier-Fed Motor Drive System
This paper proposes grid current shaping method using DC-link shunt
compensator for three-phase diode rectifier system without electrolytic DC-link
capacitor. In the proposed method, the diode rectifier system can satisfy the grid
regulation IEC61000-3-12 without any power factor correction circuit or heavy grid
filter inductor. The proposed grid current shaping method can be realized by applying
DSC with reduced-rating components and without electrolytic capacitor, which is
connected parallel to the DC-link. Since DSC has no electrolytic capacitor, the system
with DSC has high circuit reliability. Furthermore, DSC can enhance the system
efficiency, especially in flux-weakening area, since the motor drive inverter recovers
the reduced modulation index which has been spent in the additional control for small
passive components. This paper presents the control method for DSC and analyzes the
effect of proposed method. The feasibility of proposed method is verified by
simulation and experimental results.
S3I_PED16_0027 - High Performance Predictive Control of Quasi Impedance
Source Inverter
The quasi-Z-source inverter (qZSI) has attracted much attention for motor
drives and renewable energy applications due to its capability to boost or buck in a
single converter stage. However, this capability is associated with different challenges
related to the closed loop control of currents, control the DC capacitor voltage,
produce three-phase AC output current with high dynamic performance and obtain
continuous and low ripple input current. This paper presents a predictive control
strategy for a three-phase qZSI that fulfills these requirements without adding any
additional layers of control loops. The approach is to improve the overall performance
of the converter with a switching strategy that reduces inverter switching losses. The
proposed controller implements a discrete-time model of the qZSI to predict the future
behavior of the circuit variables for each switching state, along with a set of multi-
objective control variables all in one cost function. The quasi impedance network and
the AC load are considered together when designing the controller in order to obtain
stability of the impedance network with a step change in the output reference. A
detailed comparative investigation between the proposed controller and the
conventional PI controller is presented to prove the superiority of the proposed
method over the conventional control method. Simulation and experimental results are
presented.

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S3I_PED16_0028 - Improved Fault-Tolerant Control for Brushless Permanent
Magnet Motor Drives With Defective Hall Sensors
Brushless permanent magnet motor drives based on Hall sensors have received
significant attention in recent years. In this area, the faults of Hall sensors become a
new concern and several fault-tolerant control (FTC) methods have been proposed.
However, most of the state-of-the-art FTC methods require some time to reconstruct
the correct Hall sensor signals, which results in significant transient currents and
speed dip during fault diagnostic process (FDP). In this paper, a new and improved
FTC scheme based on FDP and vector-tracking observer is proposed. A method to
identify the duration of FDP is proposed based on the analysis of acceleration
estimation and the fault diagnosis results. During FDP, the method defaults to an
open-loop observer control, which removes the undesirable current/torque transient.
After that, the close-loop observer is re-enabled and the motor operation is restored.
The proposed FTC is demonstrated in detailed simulations and experimentally on
120° brushless dc motor drives and sinusoidal PM motor drives. For both types of
drives, a significant improvement is achieved in steady state and transient operation
with faults of up to two Hall sensors, which has not been possible with available
alternative FTC approaches (unless a sensorless control is used).
S3I_PED16_0029 - Quasi-Resonant (QR) Controller with Adaptive Switching
Frequency Reduction Scheme for Flyback Converter
A quasi-resonant (QR) controller with an adaptive frequency reduction scheme
has been developed for a flyback converter. While maintaining the valley switching,
the QR controller reduces the switching frequency for lighter load by skipping some
valleys to reduce the power loss and thereby achieve better light load efficiency. If the
QR controller cannot detect any valley due to the damped oscillation of switch
voltage, the valley switching is given up and the non-valley switching is employed to
keep reducing the switching frequency for lighter load. The proposed QR controller
has been implemented in a 0.35-μm 700-V BCDMOS process and applied to a 40-W
flyback converter. The power efficiency of the flyback converter is improved by upto
3.0-% when the proposed QR controller is employed compared to the one employing
the conventional QR controller.
S3I_PED16_0030 - Self-correction of Commutation Point for High-speed
Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF
This paper presents a novel self-correction method of commutation point for
high-speed sensorless brushless dc (BLDC) motors with low inductance and nonideal
back electromotive force (EMF) in order to achieve low steady-state loss of
magnetically suspended control moment gyro (MSCMG). The commutation point

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before correction is obtained by detecting the phase of EMF zero-crossing point and
then delaying 30 electrical degrees. Since the speed variation is small between
adjacent commutation points, the difference of the nonenergized phase’s terminal
voltage between the beginning and the end of commutation is mainly related to the
commutation error. A novel control method based on model-free adaptive control is
proposed, and the delay degree is corrected by the controller in real time. Both the
simulation and experimental results show that the proposed correction method can
achieve ideal commutation effect within the entire operating speed range.
S3I_PED16_0031 - Performance Analysis of the Computational Implementation
of a Simplified PV Model and MPPT Algorithm
In many research centers around the world, had been researched models that
accurately represent the PV modules operation. In this context, this paper presents a
modeling of photovoltaic modules, which aims to simplify the simulation of
photovoltaic systems in MATLAB®/Simulink. The model in question has not been
very explored in the literature and, therefore, this paper has the purpose of evaluating
its results connecting the model to a boost converter, being its main function the
Maximum Power Point Tracking applying one of the most known methods, the P&O
(Perturb & Observe). Furthermore, this converter elevates the voltage generated by
the photovoltaic modules, in order to connect the PV array to the grid through an
inverter. This paper also presents a good correlation between the theoretical and
practical results from the proposed modeling and high efficiency of the implemented
MPPT algorithm as well.
S3I_PED16_0032 - Model Predictive Control Scheme of Five-Leg AC-DC-AC
Converter-Fed Induction Motor Drive
AC-DC-AC converter-fed induction motor drive is mainly realized by back-to-
back three-phase converters. However, fault in a single semiconductor switch will
make it inoperative. To enable continued controllable operation in case of the faults
occurring in the converter, the five-leg converter with a shared leg between the grid
and load sides is a possible solution. However, this topology poses an inherent two-
objective control problem because its grid and load sides should be controlled
simultaneously. More importantly, the potential increase of the shared-leg current may
destroy the converters. In this paper, a new control scheme based on the FCS-MPC
combined with intrinsic characteristic of the five-leg converter is proposed for
independent control of the rectifier and inverter subsystem with the shared-leg
overcurrent constraint. The condition for independent control is analyzed. In order to
give a complete evaluation of the proposed control scheme, the conventional PWM

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control scheme is conducted for comparison. Experimental results are provided to
validate the effectiveness of the proposed scheme.

Final year IEEE 2016-2017 PROJECTS TITLES (IEEE 2016 papers) For ME,M.Tech,BE,B.Tech (.net,java,matlab,vlsi,ns2,mech,power elc & power sys)

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Final year IEEE 2016-2017 PROJECTS TITLES (IEEE 2016 papers) For ME,M.Tech,BE,B.Tech (.net,java,matlab,vlsi,ns2,mech,power elc & power sys)