Combining cloud and sensors in a smart city environment

EDITORIAL Open Access
Combining Cloud and sensors in a smart city
environment
Nathalie Mitton1
, Symeon Papavassiliou2
, Antonio Puliafito3*
and Kishor S Trivedi4
Abstract
In the current worldwide ICT scenario, a constantly growing number of ever more powerful devices (smartphones,
sensors, household appliances, RFID devices, etc.) join the Internet, significantly impacting the global traffic volume
(data sharing, voice, multimedia, etc.) and foreshadowing a world of (more or less) smart devices, or “things” in the
Internet of Things (IoT) perspective. Heterogeneous resources can be aggregated and abstracted according to
tailored thing-like semantics, thus enabling Things as a Service paradigm, or better a “Cloud of Things”. In the
Future Internet initiatives, sensor networks will assume even more of a crucial role, especially for making smarter
cities. Smarter sensors will be the peripheral elements of a complex future ICT world. However, due to differences
in the “appliances” being sensed, smart sensors are very heterogeneous in terms of communication technologies,
sensing features and elaboration capabilities. This article intends to contribute to the design of a pervasive
infrastructure where new generation services interact with the surrounding environment, thus creating new
opportunities for contextualization and geo-awareness. The architecture proposal is based on Sensor Web
Enablement standard specifications and makes use of the Contiki Operating System for accomplishing the IoT.
Smart cities are assumed as the reference scenario.
Keywords: Internet of Things, sensor networks, Cloud computing, smart cities
Introduction
As suggested by current ICT trends (Future Internet),
sensing and actuation resources can be involved in the
Cloud, in our view not exclusively as simple endpoints,
but they should be dealt with in the same way as comput-
ing and storage resources usually are in more traditional
Cloud stacks: abstracted, virtualized, and grouped in
Clouds. Moreover, by adding sensors and actuators into
the mix, new opportunities arise for contextualization
and geo-awareness. Following the naming conventions
for (virtualized) computing resources (“Infrastructure as
a Service”—IaaS) and storage resources (“Data as a Ser-
vice”), we may define such an approach by the phrase
“Sensing and Actuation as a Service” (SAaaS). Beyond
enabling fixed infrastructure, the resulting scenario is
highly dynamic since it may also involve volatile mobile
devices. Thus, a workable plan to address such issues
suitably is to resort to the volunteer contribution model
as an underlying approach.
A remarkable point of contact for both sensing envir-
onments and Clouds is the Internet of Things (IoT),
where underlying physical items can be further
abstracted according to thing-like semantics. Indeed, the
outlined infrastructure could be the workbench on top
of which such an abstraction would be implemented,
where “things” handlers, pointing to physical items (e.g.,
documents, cars, products, parts, etc.), can be discov-
ered, selected, and allocated. Things/objects become
communicant and can also store information on and in
their surrounding environment. They also become a gate
to interact with our environment. According to a recent
Gartner report there will be 30 billion devices connected
by 2020. In this way, we can assume such a scenario as a
plethora, an ecosystem, a constellation of generic devices
and sensor networks (SNs) that are interconnected on
the Internet. It is therefore natural to think about pos-
sible ways and solutions to face an all-encompassing
challenge, where such an ecosystem of geographically
distributed sensors and actuators may be discovered,
* Correspondence: apuliafito@unime.it
3
University of Messina, Messina, Italy
Full list of author information is available at the end of the article
© 2012 Mitton et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly cited.
Mitton et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:247
http://jis.eurasipjournals.com/content/2012/1/247

selected according to the functionalities they provide,
interacted with, and may even cooperate for pursuing a
specific goal.
This scenario has been envisaged, from many different
perspectives, along several research trends (Future Inter-
net, IoT), on which institutions and governments are
spending huge efforts, having already identified such
topics as strategic ones. This is also in line with the
technological trend, i.e., identifying personal and mobile
Clouds as the hottest Cloud topics of 2012 [1]. Comput-
ing, storage, and sensing therefore become complemen-
tary aspects in the big picture, and a comprehensive
approach from the sensing/actuation perspective is
needed to optimally coordinate their interactions, thus
creating a pervasive infrastructure interacting with the
surrounding environment.
An emerging category of devices at the edge of the
Internet are consumer-centric mobile sensing and com-
puting devices, such as smart phones and in-vehicle sen-
sors. These devices will fuel the evolution of the IoT as
they feed sensor data to the Internet at a societal scale.
Individuals with sensing and computing devices collect-
ively share data and extract information to measure and
map phenomena of common interest.
Today, people are increasingly capable of creating and
sharing written and recorded content via the Internet.
Through the use of sensors (e.g., cameras, motion sen-
sors, and GPS) built into mobile devices and web ser-
vices to aggregate and interpret the assembled
information, a new collective capacity is emerging—one
in which people participate in sensing and analyze
aspects of their lives that were previously invisible. This
trend, often named Participatory Sensing and/or Mobile
Crowdsensing, is primarily concerned with data collec-
tion, processing, and interpretation. This essentially
emphasizes the involvement of users and community
groups in social networks, documenting different aspects
of their lives.
In such a context, mechanisms and tools for discovery
and selection of virtual sensors and actuators according
to both functional and nonfunctional properties
expressed in terms of specific (QoS/SLA) constraints,
actions while taking into account sustainability and en-
ergy efficiency issues of energy-constrained (battery pow-
ered) devices and SNs, are required.
Other issues to be addressed are related to the hetero-
geneous resource mashups, i.e., how to orchestrate
assorted sensing, actuation, computing, storage resources
of volunteer-based sensing Clouds with those of existing
public/private computing and storage Clouds. The afore-
mentioned objectives lead to what are to be considered
as two independent solutions, as an SAaaS Cloud may
provide its own, standalone service, that can be either
mashed up or not, and a mashup provider may as well
mash up resources without necessarily involving volun-
teer SAaaS Clouds.
Vision and paradigm
In this way, our perspective moves towards the Cloud of
Things (CoT) as compared to the IoT and Web of
Things paradigms. A CoT implies much more than just
interconnecting and hyperlinking things. A CoT pro-
vides services by abstracting, virtualizing, and managing
things according to the needs and the requirements spe-
cified by users, negotiated and agreed to by the parties
through specific SLA agreements/procedures. The pur-
pose is to implement services to provide indexing and
querying methods applied to things, i.e., heterogeneous
(sensing, actuation, computing, storage, and energy
sources) resources aggregated according to a given
thing-like semantics and provided to final users, develo-
pers, SaaS providers, etc., as a service, thus named TaaS.
In this context, needed background and enabling tech-
nologies to implement this stack are: resources and
things abstraction and virtualization, with proper seman-
tics in relation to the domain under consideration (pri-
marily sensors, actuators, and IoT); volunteer techniques
and mechanisms for autonomous enrolment and distrib-
uted coordination; Cloud-like, service-oriented inter-
faces, and fruition (on-demand adaptive “elastic” tools);
interoperability and federation techniques, standards and
tools to enable heterogeneous resource/Cloud mashups;
business logic; security and trustworthiness policies.
Sensed information is generally acquired by independ-
ent administrations deploying their own monitoring in-
frastructure and software architecture. Sharing such
information can be strategic, not only offering advanced
services in Smart Cities, but also processing them for
making correlations on data can be very complex. The
idea of such a massive scale data sharing is leading to-
wards the concept of system of systems, which aims to
achieve task-oriented integration of different “systems”
provided by independent public and private organiza-
tions, offering new levels of effectiveness and efficiency.
An example of its applications is the World-Wide Smart
Cities initiatives that involves many Administrations and
is a concrete reality [2].
According to the systems integration idea, the IoT
allows a high level of interoperability and a certain de-
gree of flexibility. It enables seamless communication
flows between heterogeneous devices, hiding the com-
plexity of the end-to-end heterogeneity from the com-
munication services. However, the complexity of
technologies and the plethora of heterogeneous inter-
connected networks limit integration strategies.
Therefore, much research by the scientific community
is still necessary. For example, IBM India has recently
funded a new research activity, for considering Sensor
Mitton et al. EURASIP Journal on Wireless Communications and Networking 2012, 2012:247 Page 2 of 10

Web technologies in the context of smart cities (SENSIT
[3]). The project is specifically aimed at the low-cost
sensor-based solution to assist India with rainfall moni-
toring and flood forecasting.
In this article, we present a new architecture that pro-
vides to Internet users the capability to obtain any type
of data acquired from different heterogeneous sensing
infrastructures (SIs), exposed in a uniform way. The data
provisioning is very flexible and it meets the user
requirements. This result is achieved by accomplishing a
high level of abstraction of sensing technologies and
sensed data. The architecture has been designed consid-
ering the following main purposes:
The provisioning of data has to be performed with
high reactivity and high level of scalability.
The system has to provide a rapid setup of deployed
sensors and an easy integration of new sensors in
the sensing environment.
To earn these requirements, specific design strategies
have been developed. In particular, a hierarchical
organization of the architectural components allowing to
separately manage a high-level intelligence, achieving the
abstraction of data and the fulfillment of clients requests.
Furthermore, a strong interaction of the system with
sensors has been accomplished through a peripheral
decision-maker who is able to analyze, filter and aggre-
gate sensed information. The data abstraction layer of
our architecture has been developed according to the
Sensor Web Enablement (SWE) standard defined by the
Open Geospatial Consortium [4]. Nevertheless, our solu-
tion overcomes the limitations of SWE, only conceived
for the Web use of sensors. The layer for the interaction
with the SIs makes use of Contiki [5], an Operating Sys-
tem designed for sensors and embedded systems. It gives
a uniform platform for communicating with heteroge-
neous sensors.
The remaining of the article is organized as follows.
We first provide a background information of related
ideas in the following section. After that, the whole pro-
posed framework and its components are explained in
Section “Reference scenario and proposed architecture”.
Implementation details are given in Section “Implemen-
tation issues”, while Section “Case study: smart cities”
discusses a case study related to services development in
smart cities. Finally, this study is concluded with the
suggestions on future works.
Related work
In the sensor technology domain, virtualization has been
proposed with the goal of enabling seamless interoper-
ability and scalability of sensor node platforms from dif-
ferent vendors via uniform management, with the
interposition of an abstraction layer between the applica-
tion logic and the sensor driver [6] (also in the IoT con-
text [7,8]). Virtualization can also be performed by
forming virtual sensor networks, enabling multi-purpose,
collaborative, and resource-efficient exploitation of the
physical infrastructure that may involve dynamically
varying subset of sensors. Software abstraction layers are
used to address interoperability and manageability issues
[9] and to allow the dynamic reconfiguration of sensor
nodes within the WSN, for whichever purpose [10], and
the combination of sensor data [11].
Regarding the description and implementation of fra-
meworks for efficient representation, annotation and
processing of sensor data, the goal of the OGC SWE
[12] initiative is the definition of Web service interfaces
and data encodings to make sensors discoverable and ac-
cessible on the WWW, able to receive requests. On the
other hand, the W3C Semantic Sensor Network Incuba-
tor Group aims at extending this syntactic level inter-
operability to a semantic level (CSIRO [13,14]).
Significant research on sensing, actuation, and IoT is
directed towards the efficient semantic annotation of
sensor data. In [15], an approach is proposed to make
sensor data and metadata publicly accessible by storing
it in the Linked Open Data Cloud. Similarly, in [16] an
infrastructure called SensorMasher provides the ability
for non-technical users to access and manipulate sensor
data on the Web, while in [17-19] different ontologies
and semantic models are presented for sensor data rep-
resentation, such as SUMO, Ontosensor, and LENS. A
detailed survey of existing sensor ontologies is available
in [20]. Also a European FP7 project, SENSEIa
, was
launched from 2008 to 2010 to deal with these aspects.
Some great industrials such as Ericssonb
position them-
selves on SmartCities as well showing it as the next
challenge.
A promising research field is the IoT [21,22] that aims
at meshing a networked environment, where the nodes
may also semantically be tagged as things from physical
world items. Although the resources in the Cloud could
be useful to overcome certain constraints of smart
devices in IoT scenarios, absence of context-awareness
in the Cloud widens the gap between elastic resources
and mobile devices. Several bridging approaches exist
[23] but bindings are required to handle mappings be-
tween physical environments in IoT and virtual environ-
ments in the Cloud [24], as those described in [7].
Forming Clouds of sensors and other mobile devices
shows similarities to existing technologies developed in
the area of dynamic services [25]. Service registries are
to act as repositories for metadata concerning services.
They can be architecturally centralized or distributed
and, for information retrieval, keyword-based, signature-
based, semantic based, context-based and quality based

[26]. Service monitoring and tracking facilities are
devised in order to deal with the inherently unreliable
nature of services, that cannot be assumed “always on”,
as mobile-powered ones may go offline in one location
and turn up again somewhere else, and the availability of
some services may swing steadily in a unpredictable way.
In literature, some works deal extensively with issues
related to Smart Cities. The authors of [27] highlight
how the cities of the future will need to collect data
form a lot of urban sensors, such as smart water, electric
meters, GPS devices, building sensors, weather sensors,
and so on. Many of them are low-cost sensors with a
high level of noise and unreliable communication equip-
ments. The key idea for getting high-quality services
from such cheap sensors is the cross-correlation of
sensed data from several sensors and their analysis with
sophisticated algorithms. In South Korea, there are sev-
eral initiatives to move from Ubiquitous City (U-City) to
UEco-City, that is a city designed with consideration of
environmental impacts. For example, the authors of [28]
present a platform for managing urban services that in-
clude Convenience, Health, Safety, and Comfort. Also,
the differences between “smart city” and “digital city” are
detailed in [29].
To understand how Smart Cities may benefit from
Sensor Web technologies, Hernandez-Munoz et al. [30]
presented an extension of their framework, called Ubi-
quitous Sensor Networks [31] that leverages the SWE
along with the SIP protocol. One of the main problems
of the SIP protocol is related to the network constraints.
Usually, network administrators limit the Internet com-
munications using firewalling policies, and the SIP heav-
ily suffers from this limitation [even more if a Network
Address Translator (NAT) is present].
The authors of [32] propose a solution that, exploiting
the XMPP protocol to deploy SWE solutions, is immune
to such a problem, in fact it is able to overcome Fire-
walls and NATs locking. Interesting research papers are
related to Contiki a tiny Operating System for Sensors.
The designers of Contiki are looking at how to make
interoperable SNs using IPv6 [33] and how to efficiently
store data inside sensors [34]. In particular, the authors
of [34] have introduced a tiny database, called Antelope.
The article shows how sensors are becoming even smal-
ler parts of the current Internet (in terms of used proto-
cols and added capabilities). Nevertheless in our view
the abstraction should be unrelated with the underlying
sensing technology.
The problem to define an abstraction of sensed data
representation was also identified in [35]. The focus of
this article is on the mechanisms for evaluating context-
ualizing rules. For example, in processing of spatial
objects, the authors analyze the concepts of proximity,
adjacency, and containment. They even introduced the
contexts of data representation with different dynamics.
Furthermore, a global model is introduced with dynamic
interoperability without taking into consideration how
the global view should be accomplished. The decision
maker should evaluate several incoming data, but it is
not clear how to address such a problem (i.e., scalability
problems).
Reference scenario and proposed architecture
Our main objective is to design an innovative architec-
ture, able of adding new sensing capabilities with Zero-
Conf approaches, abstracting sensing data, conferring to
world wide systems a high reactivity and high level of
scalability. To realize this, we need to manage each sys-
tem by using a cluster-based approach, as it is shown in
Figure 1, and originally discussed in [36]. We assume
that a system is mapped on sites that could be of differ-
ent scales. In the scenario of Smart Cities, a site can be a
building, a factory, city roads, or also a whole city. Each
site is an autonomous system (AS), in which Clients and
Services interact with each other. The AS has its own
SIs (data producers) and Clients interested in sensed
data (data consumers). SI could be a single sensor or
possibly a full multi-hop wireless SN. They are possibly
mobile and thus connect at different points of the cloud
along time. Data gathered by SIs into a site (for example,
SIAi in the site A) are stored through a Database (DB)
Manager in a local relational database, in order to offer
an efficient retrieval of data to internal clients (in the ex-
ample, ClientAi). This database could be delocalized and
distributed in order to share and store information in a
smart way and in a transparent manner for the users
[37]. It could even include sensors themselves. However,
the same data can be useful for Client in other sites,
such as sites B and C (see Figure 1). To also offer such a
service to external clients without wasting services per-
formance of a site, the DB Manager publishes sensed
data on a global distributed column-based database
(such as Cassandra [38]) accessible from all sites.
Whenever a client requests data, according to
authorization rules and existing agreements among sites,
the DB Manager checks both, either if the client
requests can be satisfied within a site or if it needs exter-
nal information. In the latter case, it enables the DB Ser-
vice Manager to perform a query on the distributed
database. This approach allows many sites to cooperate
with each other, sharing data and services, at the cost of
a higher complexity architecture.
In order to build such an ambitious architecture, i.e., a
Cloud of Sensors based on the SAaaS paradigm, in Fig-
ure 2 we introduce the whole stack and a high-level
schema of the architectural modules, identifying three
main components, from the bottom up: Hypervisor,
Autonomic Enforcer, and VolunteerCloud Manager. As

discussed in [39], the lowest block, the Hypervisor,
works at the level of a single node, where to abstract
away either embedded sensors available on a personal
device (e.g., smartphone) or standalone sensors, smart or
otherwise, belonging to a network (WSNs). Among its
duties are relaying commands and data retrieval, abstrac-
tion of devices and capabilities, virtualization of
abstracted resources, semantic labeling and thing-
enabled services. The Adapter enables the communica-
tion directly with sensing/actuation devices and keeps
track of resources connectivity. It translates application
commands and forwards them to the underlying physical
resources, using the native communication protocol of
the resource.
The Cloud modules, under the guise of an Autonomic
Enforcer and a VolunteerCloud Manager, deal with
issues related to the interaction among nodes, belonging
to a single Cloud, for generating a Cloud of Sensors: the
former is tasked with enforcement of policies, local ver-
sus global (i.e., relayed) policy tie-breaking, subscription
management, cooperation on overlay instantiation,
where possible through autonomic approaches, while
the latter is in charge of exposing the generated Cloud it
hides by means of Web Service interfaces, framing re-
ward mechanisms and policies in synergy with SLA
matching, to be mediated by QoS metrics and monitor-
ing, as well as indexing duties to allow for efficient dis-
covery of resources.
One envisioned paradigm is to enable every traditional
entity (node, user, and provider) to be exposed and con-
sumed as a Service that provides and requests content
information. Applications use user-generated content
from fixed and/or mobile devices gathered in collabor-
ation with its owner/operator. Such a model requires
fundamentally novel algorithms for the data collection,
aggregation, analysis, and composition of different ser-
vices. Moreover, it entails novel application-level
mechanisms in order to enable those who request or
provide services to share data, while respecting the priv-
acy of those involved.
Cloud-Based Services (CBServices) can be any “heavy”
type of services that needs more resources and infra-
structure in order to function properly. Streaming video,
music on-the-go, social networks, web browsing, are
among most popular applications in cloud environ-
ments. From the server side, all these services have sev-
eral and usually intensive requirements in resources,
middleware software, and infrastructure. CBServices can
use traditional publish-subscribe model into Cloud en-
vironment in order to be used by other users or services,
however use advanced features/properties of Cloud en-
vironment/platform to allow elastic services scalability
and global delivery on-demand.
Mobile-Based Services on the other hand include mo-
bile nodes that are moving in a non-structural way and
provide any type of services and information from their
current location. A user with a mobile phone or a tablet
device can provide various types of location-based infor-
mation depending on the application. The advantage of
these kinds of services is that they exchange content that
is user-generated and often very dynamic. A service ask-
ing for traffic information along a route can receive
Figure 1 Logical organization of the system in a world wide scenario.

dynamic and updated information from other users/ser-
vices along the same route without necessarily requiring
the support of a heavy centralized system. Such an archi-
tecture provides information to the user in a flexible and
fast way.
Implementation issues
In this section, we report and describe the status of the
current implementation of the SAaaS Cloud framework.
Although this is still a work in progress, here we hope to
provide proof of feasibility for the architecture depicted
in Figure 2 and present available underlying solutions.
Hypervisor
The Hypervisor block implements the abstraction of
sensing and actuation resources, providing functionality
at the level of a single node, explicitly defined as a man-
agement domain, either an SN controlled by a specific
gateway, or a standalone/set of sensors within a device.
In SNs, a node may be less easily identifiable with re-
spect to the one-to-one relationship we have for smart-
phones and other personal smart devices: more
specifically we may have an SN made up of thousands of
sensors, yet only exposing few sinks, where only part of
the stack (i.e., nodal components) can be deployed. A
modular architecture of the Hypervisor identifies the fol-
lowing three components: Adapter, Node Manager, Ab-
straction, and Virtualization Unit.
The lowest component, the Adapter, was developed by
means of modifications to CLEVER, an IaaS stack with a
flexible framework for internode/cloud communication
and event notification [40]. This fork, CLEVERSens [41],
works over a common baseline environment, having
chosen Contiki [5] as open source platform to deploy on
gateways and other sensing hardware for development
and field testing, leveraging, in line with the OGC-
mandated SWE framework, the set of XML-based lan-
guages and Web service interface specifications they
defined.
Among many, the following SWE standards have been
implemented in the Adapter to ease the discovery, ac-
cess and search over sensor data:
SensorML—models and XML schemas for
describing sensors systems and processes; it provides
information needed for discovery of sensors,
location of sensor observations, processing of low-
level sensor observations and task-oriented listing of
properties;
OM (Observation and Measurements)—models
and XML schemas for encoding observations and
measurements from an SN;
SOS (sensor observation service)—interface for
requesting, filtering, and retrieving observations and
sensor system information.
The internals consist of the following three layers,
from the top down:
REST APIs as interface, which allow on-demand
interactions with clients, applications and/or other
services;
an SOS Agent, which faces up the abstraction of
sensed data according to SOS specifications,
supporting all mechanisms for describing sensors
and observations, setting new observations and
gathering measurements from SNs. It makes use of
SensorML, for describing sensor systems and sensed
data, and the OM standard, for modeling sensor
observations;
a Sensor Manager (SM), able to interact with
sensors, coordinates their activities and collect data
for the upper layers. It provides a uniform
management of heterogeneous sensors.
Moreover, we are planning to extend it further to
cover Node Manager capabilities, for instance in relation
to power consumption self-tuning, to be implemented
with hooks also at runtime and OS layer under Contiki.
We also intend to exploit Abstraction Virtualization
capabilities, engaging the OGC actively to expand on
existing standards and propose new specifications for
Node
Cloud
Hypervisor
Adapter
Autonomic Enforcer
Devices
Volunteer Cloud
Manager
SAaaS Providers
Figure 2 Architectural schema and modules.

composition of advanced virtual sensors, unbundling of
resources from complex devices, and instantiation of
abstracted resources with proper reliability and sandbox-
ing mechanisms, in line with typical (IaaS) hypervisor-
driven capabilities.
Autonomic enforcer
The bridge between virtualized nodes and SAaaS Clouds
is the Autonomic Enforcer. It is a module that, first and
foremost, allows the node to join a Cloud, thus exposing
its resources as services through the Internet. Further-
more, the Autonomic Enforcer locally manages the node
resources considering both higher level Cloud policies
and local requirements and needs, e.g., power manage-
ment on mobiles. This is therefore implemented in a
collaborative and decentralized way, making decisions by
interacting with neighboring nodes, and adopting auto-
nomic approaches. The Autonomic Enforcer is to be
deployed into each node of the SAaaS infrastructure in
order to apply the policies of the VolunteerCloud Man-
agement module, self-adaptively.
In combination with the Hypervisor Node Manager,
the Autonomic Enforcer makes up a hierarchical,
decoupled, two-level autonomic management system en-
tirely deployed and working on the node. The former
operates at device level, more specifically within an SN
domain, while the latter enforces higher level Cloud tar-
gets. To this purpose, four main blocks have been identi-
fied in the Autonomic Enforcer functional schema: a
Policy Actuator below, Policy Manager and Subscription
Manager above it, and Cloud Overlayer on top of them.
The heart of the Autonomic behavior for the Enforcer
lies in its ability to leverage an architectural model and a
runtime infrastructure where cooperating agents, the
SelfLets [42,43], can provide services, and consume
those offered by other SelfLets as well, being able to
make decisions based on local knowledge of the sur-
rounding environment.
A SelfLet can easily be tuned in terms of both default
behaviors and autonomic policies. The idea is that, by
keeping tabs on local resources, each SelfLet settles on
whether to carry out certain global optimization actions,
such as redirecting requests, teaching policies (and the
implementations of related mechanisms, if the need
arises) to other SelfLets, or learning from others as well.
In our ongoing efforts, we are taking into account rev-
enues and costs, to be relayed to the Reward System in
the Volunteer-Cloud Manager, generated as a result of
demand for service in a SelfLet-driven environment (i.e.,
the Enforcer Managers) according to concurrent, even-
tually contrasting, requests, i.e., when originating from
subscriptions of a single node to several Clouds. After
evaluating a set of candidate optimization policies, inclu-
sive of (eventual) subscription tuning, each SelfLet can
pass its choices to the Actuator and inform its neigh-
bors, following a greedy strategy, or a non-greedy one,
depending on the state of surrounding SelfLets.
VolunteerCloud manager
In the development of the relevant modules, we strongly
based our study on the results provided by the Cloud@-
Home project [44]. The VolunteerCloud manager aims
to consolidate volatile, ad hoc, dynamic resources and
services, such as volunteer-contributed sensors, in a
Cloud environment. The main focus is on methods alle-
viating the effects of resource churn, where their per-
formance is largely dynamic, their lifespan is short,
nodes are mobile and heterogeneous, and information
on their status is partial and typically out of date. While
this layer operates on largely unreliable and unpredict-
able resources, it provides services featuring increased
dependability either to the Cloud layer or to other peer
Cloud systems. The VolunteerCloud Manager defines
and imposes management strategies at the Cloud level,
through a continuous interaction with each single device
belonging to the constituted sensing Cloud. Such pol-
icies have to be therefore acted upon at node level by
the corresponding Autonomic Enforcer. The Volunteer-
Cloud Management builds upon nodes, through the
Autonomic Enforcer, a volunteer-based sensing Cloud,
and implements services for interacting with it. The
functionalities have been grouped into five components:
Indexing Discovery service, Reward System, SLA
Manager, QoS Manager, WS Frontend.
With regards to the Indexing Discovery Service, for
the time being, such component is designed and imple-
mented through a register service which receives and
manages the requests for registration from node owners,
collecting the corresponding description files into a
database, under a steady flow of updates and, optionally,
distributed, for increased fault tolerance. An alternative
design could be hinged on DHTbased algorithms for
P2P establishment, tracking the providers’ statuses to
spot those that may offer better support to fault toler-
ance, and a simpler way to keep the status about the
chosen provider up-to-date.
The Reward System implementation is based on the
solutions provided by BOINC [45] and EDGI [46].
Credit-reward systems are used here to reward coopera-
tive and fair behaviors and to motivate resource provi-
ders, or donors. BOINC for instance employs a credit
system where volunteers are awarded credits based on
donated CPU and GPU time.
More specifically we are working on a hierarchical so-
lution that implements an overlay credit system on top
of volunteer credit systems (e.g., BOINC) adapted to
sensing and actuation resource metrics, i.e., primarily
the contribution time. The higher layer in the overlay

assigns (further) QoS credits rewarded for donated re-
source time. These credits can be reused and spent by
the contributor into the SAaaS infrastructure, for allo-
cating sensing and actuation resources.
The QoS Manager can be considered the counterpart
to the Resource QoS Manager (RQM) in the Cloud@-
Home architecture. It is in charge of tracking resources,
logging all the requests and their status, and is com-
posed of a core system (RQMcore) together with inter-
faces to all the other components.
Similarly, the SLA Manager corresponds to the
Cloud@Home SLA Management module. It is in charge
of the negotiation, monitoring, and enforcement of
SLAs, and cooperates with the RQM component for
QoS aspects, specifying and applying the policies related
to whole Cloud Management.
For the time being our work consists of converting
and adapting the current Cloud@Home implementation
(RQM and SLA Management module) into the corre-
sponding components of the SAaaS-VolunteerCloud
Manager framework (QoS and SLA Managers).
Case study: smart cities
We guess that in Smart Cities, smart sensors with high
processing power and multi-tier/IP capabilities will be
deployed. Sensors are deployed everywhere, in street to
measure the traffic, in gaz or water pipes for monitoring
and management, for pollution detection purposes, etc.
In this scenario, we assume that sensors will be
equipped with a lightweight operative system for SN
nodes. Two major operating systems lead the way on
firmware development for motes: Contiki and TinyOS
[47]. Contiki is an open source, highly portable, multi-
tasking operating system for memory-efficient net-
worked embedded systems and SNs. Contiki is designed
for microcontrollers with small amounts of memory (a
typical Contiki configuration is 2 kB of RAM and 40 kB
of ROM). Contiki has been used in many projects, such
as road tunnel fire monitoring, intrusion detection, wild-
life monitoring, and in surveillance networks. One of the
biggest features of Contiki is the very light implementa-
tion of the IP stack, called uIP, with 6LoWPAN support.
This implementation was awarded the IPv6 ready silver
seal from the IPv6 Ready Logo Program. For this reason
and because Contiki uses C-like programming (versus
the nesC used by TinyOS), in our architecture we
selected ContikiOS against TinyOS. In particular, the
Hypervisor Module makes use of Contiki commands to
manage sensors and perform their specific functional-
ities. In coordination with the Autonomic Enforcer, it
tracks sensors as nodes, detecting if they are moving,
entering or leaving the system. It periodically runs an
Initialization process to detect changes in the nodes con-
figuration (e.g., their position) and in their availability. It
is responsible for extracting data from packets sent by
sensors, and makes them available to the SOS Agent.
In a site, to guarantee the scalability of the architec-
ture, multiple instances of SMs for different SIs are con-
nected to a single SOS Agent, as shown in Figure 3.
They are independent processes, which can be executed
even in different hosts. Inside the SIs, different types of
nodes organization can be considered: only one SN
managed by an administration, a set of several independ-
ent devices, or both.
Figure 3 Hierarchical relationship between a SOS Agent and one or more SMs.

In fact, the SM abstracts the hardware features of
sensing devices, the communication technologies, and
the topologies for communications among sensors. The
communication between SMs and the SOS Agent is
based on the XMPP communication protocol. XMPP is
widely used (see GTalk chatting protocol of Google) and
very flexible (contrary to other messaging/signalling pro-
tocols, e.g., SIP) since it offers:
decentralization of the communication system (i.e.,
no central server exists);
flexibility to maintain system interoperability;
fault-tolerance and scalability in the management of
connected entities;
native security features based on the use of channel
encryption and/or XML encryption;
NAT and Firewall pass-through capabilities.
Conclusions
This article intends to shift the boundaries towards a
Cloud of sensors and the like, where sensors and actua-
tors not only can be discovered and aggregated, but also
dynamically provide as a service, applying the Cloud
provisioning model. Having in mind the (agreed) user
requirements, it is thus possible to establish Sensors and
Actuators as Service providers. The SAaaS envisages
new scenarios and innovative, ubiquitous, value-added
applications, disclosing the sensing and actuation world
to any user, a customer and at the same time a potential
provider as well, thus enabling an open marketplace of
sensors and actuators.
This requires an ad hoc infrastructure that has to deal
with the management of sensing and actuation resources
provided by both mobiles and SNs, addressing the vola-
tility of mobiles through volunteer-based techniques, in
a SAaaS perspective.
A possible area of application of such idea could be
the IoT. To this purpose, it is necessary to deal with
things, exploiting the well-known ontologies and seman-
tic approaches shared and adopted by users, customers,
and providers to detect, identify, map, and transform
sensing resources. In this article, we identify and outline
the roadmap to implement this challenging vision. A
high-level modular architecture has been defined, identi-
fying blocks to deal with all the issues herein discussed.
Such architecture offers data gathered from many het-
erogeneous SIs to Internet clients in a uniform way, by
using an abstraction layer designed according to the spe-
cification of the SWE standard. To support different
types of sensors, the interaction with heterogeneous sen-
sors has been accomplished using the Contiki Operating
System.
Many topics are still open problems and challenges,
thus material for future work. We specifically aim to
develop advanced services for data filtering and aggrega-
tion, in order to apply them to a specific Smart City use
case.
Endnotes
a
www.ict-sensei.org/. b
http://www.slideshare.net/skrip-
nikov/ericsson-smart-city-vision.
Competing interests
The authors declare that they have no competing interests
Author details
1
Inria, Paris, France. 2
National Technical University of Athens, Athens, Greece.
3
University of Messina, Messina, Italy. 4
Duke University, Durham, USA.
Received: 13 July 2012 Accepted: 13 July 2012
Published: 8 August 2012
References
1. http://www.datamation.com/cloud-computing/7-hot-cloud-
computingtrends-for-2012-1.html
2. The Top 10 Smart Cities On The Planet, http://www.fastcoexist.com/1679127/
the-top-10-smart-citieson-the-planet
3. Sensing troubled waters in a Smarter City, http://www.ibm.com/ibm100/us/
en/service/ grants/centennial grant indian inst tech 2011.html
4. Sensor Web Enablement, Sensor Web Enablement, 2011. Available: http://
www.opengeospatial.org/standards
5. A. Dunkels, B. Grnvall, T. Voigt, Contiki—a lightweight and flexible operating
system for tiny networked sensors, in Proceedings of the 29th Annual IEEE
International Conference on Local Computer Networks (LCN ’04) (, Tampa,
Florida (USA), 2004), pp. 455–462. 2004
6. M. Iqbal, D. Yang, T. Obaid, T.J. Ng, H.B. Lim, Demo abstract: a service-
oriented application programming interface for sensor network virtualization,
in Information Processing in Sensor Networks (IPSN),10th International
Conference (, Chicago, Illinois (USA), 2011), pp. 143–144. 2011
7. S. Alam, M. Chowdhury, J. Noll, Senaas: an event-driven sensor virtualization
approach for internet of things cloud, in IEEE International Conference on
Networked Embedded Systems for Enterprise Applications (NESEA) (, Suzhou
(China), 2010), pp. 1–6. 2010
8. H.B. Lim, M. Iqbal, T.J. Ng, A virtualization framework for heterogeneous sensor
network platforms, in Proceedings of the 7th ACM Conference on Embedded
Networked Sensor Systems (SenSys’09) Berkeley (USA, 2009), pp. 319–320.
doi:10.1145/1644038.1644080
9. M. Yuriyama, T. Kushida, Sensor-cloud infrastructure—physical sensor
management with virtualized sensors on cloud computing, in 13th
International Conference on Network-Based Information Systems (NBiS) (,
Takayama (Japan), 2010), pp. 1–8. 2010
10. A.P. Jayasumana, Q. Han, T.H. Illangasekare, Virtual sensor networks—a
resource efficient approach for concurrent applications, in Fourth International
Conference on Information Technology. ITNG ’07 (, Las Vegas, Nevada (USA),
2007), pp. 111–115. 2007
11. K. Aberer, M. Hauswirth, A. Salehi, Infrastructure for data processing in large-
scale interconnected sensor networks, in 2007 International Conference on
Mobile Data Management (, Mannheim (Germany), 2007), pp. 198–205
12. C. Reed, M. Botts, J. Davidson, G. Percivall, Ogc(r) sensor web enablement:
overview and high level architecture, in IEEE Autotestcon (, Baltimore,
Maryland (USA), 2007), pp. 372–380. 2007
13. H. Neuhaus, M. Compton, The semantic sensor network ontology: a generic
language to describe sensor assets, in AGILE Workshop Challenges in
Geospatial Data Harmonisation (, Hannover (Germany), 2009), p. 112117
14. M. Compton, H. Neuhaus, K. Taylor, K.N. Tran, Reasoning about sensors and
compositions, in Proceedings of the Semantic Sensor Networks (, Washington
DC (USA), 2009), pp. 33–48. 2009
15. H. Patni, C. Henson, A. Sheth, Linked sensor data, in International Symposium
on Collaborative Technologies and Systems (CTS) (Illinois (USA), Chicago,
2010), pp. 362–370
16. D.L. Phuoc, M. Hauswirth, Proceedings of the 2nd International Workshop on
Semantic Sensor Networks (SSN09) in Conjunction with ISWC, in Linked open

data in sensor data mashups, ed. by D.D. Kerry Taylor (, vol. 522 (CEUR)
2009)
17. C. Henson, J. Pschorr, A. Sheth, K. Thirunarayan, Semsos: semantic sensor
observation service, in International Symposium on Collaborative Technologies
and Systems. CTS ’09 (, Baltimore, Maryland (USA), 2009), pp. 44–53. 2009
18. M. Eid, R. Liscano, A. El Saddik, A universal ontology for sensor networks data,
in IEEE International Conference on Computational Intelligence for
Measurement Systems and Applications, CIMSA 2007 (, Ostuni (Italy), 2007),
pp. 59–62
19. C. Goodwin, D. Russomanno, An ontology-based sensor network prototype
environment, in Proceedings of the 5th International Conf. on Information
Processing in Sensor Networks (, Nashville, Tennessee (USA), 2006), pp. 1–2
20. M. Compton, C. Henson, L. Lefort, H. Neuhaus, A. Sheth, A survey of the
semantic specification of sensors. Networks 522, 17 (2009). http://ceur-ws.
org/Vol-522/p6.pdf
21. K. Kumar, Y.-H. Lu, Cloud computing for mobile users: can offloading
computation save energy? Computer 43(4), 51–56 (2010)
22. J. Bourcier, A. Diaconescu, P. Lalanda, J.A. McCann, Autohome: an
autonomic management framework for pervasive home applications. ACM
Trans. Auton. Adapt. Syst 6(1), 8:1–8:10 (2011). http://doi.acm.org/
1921641.1921649
23. T.F. Bissyande, L. R’eveill’ere, Y.-D. Bromberg, J.L. Lawall, G. Muller, Bridging
the gap between legacy services and web services, in Proceedings of the ACM/
IFIP/USENIX 11th International Conference on Middleware (ser. Middleware ’10
(Springer-Verlag, 2010), Bangalore (India), 2010), pp. 273–292
24. R. Golchay, F.L. Mouel, S. Frénot, J. Ponge, Towards bridging IOT and cloud
services: proposing smartphones as mobile and autonomic service
gateways. CoRR abs/1107.4786 (2011)
25. F. Hao, T.V. Lakshman, S. Mukherjee, H. Song, Enhancing dynamic cloud-
based services using network virtualization. SIGCOMM Comput. Commun.
Rev 40(1), 67–74 (2010). doi:10.1145/1672308.1672322
26. M.S. Lew, N. Sebe, C. Djeraba, R. Jain, Content-based multimedia
information retrieval: state of the art and challenges. ACM Trans. Multimed.
Comput. Commun. Appl. 2(1), 1–19 (2006). doi:10.1145/1126004.1126005
27. M. Naphade, G. Banavar, C. Harrison, J. Paraszczak, R. Morris, Smarter cities
and their innovation challenges. Computer 44(6), 32–39 (2011)
28. J. Lee, S. Baik, C. Lee, Building an integrated service management platform
for ubiquitous cities. Computer 44(6), 56–63 (2011)
29. K. Su, J. Li, H. Fu, Smart city and the applications, in International Conference
on Electronics, Communications and Control (ICECC) (, Ningbo (China), 2011),
pp. 1028–1031. 2011
30. J.M. Hernandez-Munoz, J.B. Vercher, L. Munoz, J.A. Galache, M. Presser, L.A.H.
Gomez, J. Pettersson, in The future internet, in Smart Cities at the Forefront of
the Future Internet, ed. by J. Domingue, A. Galis, A. Gavras, T. Zahariadis, D.
Lambert (Springer-Verlag, Berlin, 2011, ), pp. 447–462. http://dl.acm.org/
citation.cfm?id=1983741.1983773
31. J.M. Hernandez-Munoz, J.B. Vercher, L. Munoz, J.A. Galache, Presser,
Ubiquitous sensor networks in ims: an ambient intelligence telco platform (ICT
Mobile Summit, Stockholm, 2008), pp. 10–12
32. M. Fazio, M. Paone, A. Puliafito, M. Villari, Heterogeneous sensors become
homogeneous things in smart cities, in International Workshop on Extending
Seamlessly to the Internet of Things (esIOT12) (Palermo, Italy, 2012)
33. J. Ko, J. Eriksson, N. Tsiftes, S. Dawson-haggerty, J. Vasseur, M. Durvy, A.
Terzis, A. Dunkels, D. Culler, Beyond interoperability pushing the
performance of sensor network ip stacks. Networks 19(2), 112–113 (2011).
http://www.ncbi.nlm.nih.gov/pubmed/15664783
34. N. Tsiftes, A. Dunkels, A database in every sensor, in Proceedings of the 9th
ACM Conference on Embedded Networked Sensor Systems, ser. SenSys ’11 (,
New York (USA), 2011), pp. 316–332. 10.1145/2070942.2070974
35. D. Ballari, M. Wachowicz, M.A. Manso, Metadata behind the interoperability
of wireless sensor network. Sensors 9, 3635–3651 (2009)
36. M. Fazio, M. Paone, A. Puliafito, M. Villari, Heterogeneous Sensors Become
Homogeneous Things in Smart Cities, in International Workshop on Extending
Seamlessly to the Internet of Things (, Palermo, Italy, esIoT 2012), p. 315319.
46 –July 2012
37. A.C. Viana, N. Mitton, L. Schmidt, M. Vecchio, A k-layer self-organizing
structure for product management in stock-based networks, in 7th IEEE
International Conference on e-Business Engineering (, Shanghai, China, ICEBE
2010), p. 263269. 2010
38. The Apache Cassandra Project Develops a Highly Scalable Second-Generation
Distributed Database, http://cassandra.apache.org
39. S. Distefano, G. Merlino, A. Puliafito, Sensing and Actuation as a Service: a
new development for Clouds, in 11th
IEEE International Symposium on Network
Computing and Applications (IEEE NCA12) (, Boston, Massachussets (USA),
Aug 2012), pp. 153157–2324
40. F. Tusa, M. Paone, A. Celesti, M. Villari, An innovative open source middleware
for managing virtual resources in federated clouds (Published on the BOOK of
Open Source Cloud Computing Systems: Practices and Paradigms IGI
Global, 2011), p. 6189. http://cloudtechnologies.morfeo-project.org/archives/
open-source-cloud-systems-call-for-chapters
41. M. Fazio, M. Paone, A. Puliafito, M. Villari, HSCLOUD: cloud architecture for
supporting homeland security. Int. J. Smart Sensing Intell. Syst. 5(1), 246–276
(2012)
42. B. Caprarescu, N.M. Calcavecchia, E.D. Nitto, D.J. Dubois, SOS cloud: self-
organizing services in the cloud, in Bionetics’10: Bio-Inspired Models of Network,
Information and Computing Systems (, Boston (USA), 2010), pp. 151–158
43. N.M. Calcavecchia, D. Ardagna, E. Nitto, Run-time Models for Self-managing
Systems and Applications, ser. Autonomic Systems, in The emergence of load
balancing in distributed systems: the selflet approach, ed. by D. Ardagna, L.
Zhang (Springer, Basel, 2010, ), pp. 97–124. doi:10.1007/978-3-0346-0433-8/
_5
44. S. Distefano, A. Puliafito, M. Rak, S. Venticinque, U. Villano, A. Cuomo, G.D.
Modica, O. Tomarchio, Qos management in cloud@home infrastructures, in
International Conference on Cyber-Enabled Distributed Computing and
Knowledge Discovery (CyberC, Beijing ( China), 2011), p. 190196
45. T. Anderson, The BOINC credit system, Tech. Report (, University of California,
2012). http://boinc.berkeley.edu/trac/wiki/CreditNew
46. P. Kacsuk, J. Kovacs, Z. Farkas, A.C. Marosi, Z. Balaton, Towards a powerful
european dci based on desktop grids. J. Grid Comput. 9(2), 219–239 (2011).
doi:10.1007/s10723-011-9186-z
47. P. Levis, S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay,
J. Hill, M. Welsh, E. Brewer, D. Culler, Ambient Intelligence, in TinyOS: an
operating system for sensor networks ambient intelligence, ed. by W. Weber, J.
M. Rabaey, E. Aarts (Springer, Berlin, 2005, ), pp. 115–148
doi:10.1186/1687-1499-2012-247
Cite this article as: Mitton et al.: Combining Cloud and sensors in a
smart city environment. EURASIP Journal on Wireless Communications and
Networking 2012 2012:247.
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