![15
1
Internet of Things Today
1.1 Introduction: Key Trends
The Internet of Things (IoT) is defined as [1]: “the network of
physical objects—devices, vehicles, buildings and other items
which are embedded with electronics, software, sensors, and net-
work connectivity, which enables these objects to collect and ex-
change data.” Forbes defined it as [2]: “a giant network of connect-
ed things (which also includes people)—between people-people,
people-things, and things-things.” A thing, in IoT, can be physical
objects like a bridge, a building, or a transport having sensors like
vibration, temperature, and accelerometer, respectively, or human
beings like a person wearing a smart watch or having a biochip
implant; in IoT, all of them can have an IP address through which
the sensor data can be transferred over a network (including the
Internet). The concept of IoT came into being in 1999. However, the
first reported IoT-like device was built at Carnegie Melon Univer-
sity in the early 1980s; it was a soft drink machine connected to the
Internet allowing availability to be checked online in real time [3].
Why is IoT being regarded as one of the most disruptive tech-
nologies that will drive business digitization? The answer is](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-20-320.jpg)
![16 IoT Technical Challenges and Solutions
simple. Until now, the use of information technology in most of
the business verticals were to improve and automate the business
support services and not the core physical business process. IoT’s
ability to sense the physical world and digitize the physical world
context will be bring fundamental change and digitization to the
existing physical business processes across all verticals leading to
either significant reduction in operation cost or improvement in re-
sponse time and quality or bringing in real-time customization in
the products or service offerings or helping in increasing security
and ensuring compliance. For end consumers, IoT has the potential
to change the way that products and services are consumed; there
will be much more real-time understanding of consumer needs
and subsequent personalization thereof. This will be discussed in
further detail with examples in Section 1.2.
The market for IoT is clearly heating up. According to Gartner,
there were scheduled to be 6.4 billion connected “things” in 2016
(an increase of 30% from 2015), with a projected deployment of
20.8 billion things by 2020 (5.5 million new things to be connected
every day) [4]. Gartner has also reported that, in 2016, IoT would
be instrumental in U.S. dollar spending in total services of $235 bil-
lion and in total end-point spending of $1.4 trillion (which is an in-
crease of 22% from 2015). By 2020, the total IoT end-point spending
was projected to rise to $3 trillion. The mix of consumer compared
with business applications in this spending is also interesting; in
2015, the consumer versus business application share in the overall
spending was 35% to 65%. The consumer spending was projected
to increase over the years and, by 2020, the consumer versus busi-
ness application share in the overall spending would be 51% to
49%.
In a recent global IoT trend study by Tata Consultancy Services
[5] involving more than 3,500 executives across small and large
corporations all over the world, some interesting facts about IoT
trends have come to light:
• Nearly 80% companies surveyed have initiated IoT programs.
On average, such companies had a revenue increase of 16%.
• Nearly half of them either track customers or monitor opera-
tions through mobile applications and IoT technologies.](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-21-320.jpg)
![Internet of Things Today 17
• The spending in IoT seems to be directly proportional to actual
product offering price of the company. Companies whose prod-
ucts’ prices are more than $10 million will spend an average of
$335 million, while those with product prices of $100 or less will
spend an average of only $39 million. This can be attributed to
the fact that the return on investment (ROI) for IoT infrastruc-
ture and systems are more justified when it is used to improve,
optimize, protect, and monitor high-value products.
• There will be varying degree of IoT adoption across the four
major geographies: North America, Europe, Asia-Pacific, and
Latin America. The IoT spending will be more in North Ameri-
can and European companies (with North American companies
spending 0.45% of revenue, and their European counterparts
spending 0.40%). The Asia-Pacific companies are not going
to lag much behind in spending, with IoT-related investment
amounting to 0.34% of revenue, while the Latin American en-
terprises will spend around 0.23% of revenue on IoT.
• Companies who demonstrated largest revenue increases from
IoT initiatives had some common traits:
• They were able to create substantial value for their end cus-
tomers and not just value for themselves.
• They could deliver that value through new business mod-
els focusing on product and service offerings around the IoT
data.
• All of them leveraged the IoT technology to understand
firsthand their product and service performances and usage
patterns with regard to their end customers.
The trends described above are macro business trends being
observed globally in the IoT space. Next, we will explore slightly
deeper details of the application landscape for IoT in different in-
dustry verticals.
Some of the recent successful deployments of IoT that demon-
strate the above traits include [6]:
1. Energy monitoring and usage optimization across facilities for
large enterprises (final value add: reduction in energy cost);](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-22-320.jpg)
![18 IoT Technical Challenges and Solutions
2. Quality monitoring in the food processing chain (final value
add: ensuring same quality and taste for the end user);
3. Predictive analytics of refrigeration systems (final value add:
reduction in overall maintenance cost and increase of operat-
ing life of the machines);
4. Real-time alerting around wellness and activity for elderly
people at home and factory people in hazardous areas (final
value add: quick intervention with the appropriate medical
help).
All of these have one common factor: all of them needed to
demonstrate that the value-add (economic or otherwise) outscores
the cost of infrastructure investments, thereby justifying the ROI.
Almost every imaginable type of company seems to have an IoT
strategy in 2016 [7]. Here are some of the unlikeliest ones to throw
their hat into the IoT ring: the toymaker Mattel with the connected
Barbie doll [8], the gamemaker Atari with smart home products
[9], the nonprofit browser company Mozilla with an operating sys-
tem for connected devices [10], and the credit card company Visa
with integration of payments inside IoT devices [11]. There are a
lot of innovative products and solutions coming out in the mar-
ket in the IoT space. Their sustainability in the marketplace will
depend again on the value-add that they bring to the end custom-
er; however, it is worthwhile to follow these innovations as some
of them are bound to succeed to build a sustainable business [12,
13]. The interesting ones worth mentioning are automated cook-
ing systems, self-charging and wireless-charging platforms, wa-
ter monitoring systems, blindspot-free rear-view mirrors for cars,
three-dimensional (3-D) printing pens, and wearable monitoring
of human physiology. There are also innovative services and busi-
ness models being created around IoT-enabled products; for ex-
ample, instead of selling a washing machine, the washing machine
vendor can give it away for free and charge the consumer per wash
based on the time and the load.
Another interesting trend is the emergence of an ecosystem for
IoT. The chip makers like Intel, Qualcomm, TI, ARM, and Arduino
are providing the core sensing and processing technology with low
power consumption and universal connectivity that can be embed-
ded into the “Things.” The equipment vendors like Cisco, Huawei,](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-23-320.jpg)
![20 IoT Technical Challenges and Solutions
different business verticals. These business insights trigger the re-
sponse cycle, which may range from a completely offline process
change to completely real-time actions taken to mitigate the effects
of the underlying physical event. This sense, extract, analyze, and
respond cycle presented here is found to be common to all possible
IoT applications. The final benefits to the business and stakehold-
ers in an IoT system will invariably come from either reduced cost
due to improved operational efficiency via IoT-based monitoring
or better insights from analytics of IoT data creating value and
competitive advantage for the stakeholders.
There are four core business areas in different verticals where
IoT can be applied to generate value through sensing and subse-
quent analysis and response: facility, product, customer, and sup-
ply chain [14]. The type of applications can be monitoring, control,
optimization, or autonomous depending upon the use case. These
are depicted pictorially in Figure 1.2.
1.2.1 IoT for Facilities
Most of the industry verticals that have fixed assets in form of build-
ings, campuses, infrastructure and heavy equipment will benefit
from deploying IoT for its facilities. There can be monitoring ap-
plications like perimeter security through video surveillance, con-
trol applications like building energy management via smart meter
data analytics or predictive maintenance for machines, and optimi-
zation applications like emergency evacuation via people sensing
and localization. Such applications will generate business value in
terms improved security or compliance, lower cost of operation,
lower cost of maintenance, and improved lifespan of assets in al-
most all industry verticals including travel and hospitality, retail,
energy and utilities, banking, industrial manufacturing, farming,
healthcare, and life sciences that usually have significant real es-
tate or office spaces or infrastructure.
1.2.2 IoT for Products
Using IoT for products can take two forms: it can ensure quality
when the product is built, or it can detect finished product perfor-
mance in the field. The former requires putting sensors into the](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-25-320.jpg)
![28 IoT Technical Challenges and Solutions
fire extinguishers capable of handling gas fire needs to be sent.
The final wisdom layer would be how to design the building infra-
structure to prevent such fires from happening or minimizing the
number of people affected due to such events.
It is quite obvious that as we climb up the pyramid the business
value of the analyzed data becomes more and more useful. How-
ever, the volume of the data to be analyzed continues to reduce as
we move from information to knowledge to insights to wisdom.
1.4 IoT Standardization
It is quite clear from looking at the diversity of the technologies
and vertical domains, that standardization will become one of the
key elements to the success of IoT. All stakeholders of IoT systems
will need to invest substantially into standardization to bring in
interoperability and avoid vendor lock-in. The areas in which stan-
dardization efforts are necessary can be looked upon from differ-
ent perspectives.
In one view, the standardization can happen in IoT domain ap-
plications (including domain-specific physical infrastructures such
as building, road, and traffic), in software services layer [via ap-
plication programming interfaces (APIs) and software as a service
or (SaaS)], the ICT infrastructure (consisting of cloud, Internet and
connectivity), and edge devices (consisting of sensing devices and
embedded systems). This is pictorially depicted in Figure 1.5.
In another view from interoperation perspective, as outlined
by European Telecommunication Standards Institute (ETSI), IoT
standardization can be regarded as a set of intermixing interopera-
tion requirements in technical, syntactic, semantic, and organiza-
tional level [15]. This is represented in Figure 1.6. The technical in-
teroperation requires standardization in basic communication and
computing systems for IoT (distributed systems, physical commu-
nication systems, and embedded systems). The syntactic interop-
eration tries to standardize how these different computing systems
talk to each other using standard communication protocols. The
semantic interoperation brings in the requirement of interpreting
the IoT data in a common vocabulary, which can be either technol-
ogy-specific or physical infrastructure-specific or vertical domain-
specific or a combination of all. The organizational interoperation](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-33-320.jpg)
![30 IoT Technical Challenges and Solutions
systems talking to each other to find out the fastest route for dis-
patching an ambulance).
Many of the notable standardization organizations all over the
world are working in the area of IoT Standardization under the
umbrella of the International Telecommunication Union–Telecom
(ITU-T) [16]. They include TIA (the U.S. Telecommunications In-
dustry Association) [17], ETSI (European Telecommunications
Standards Institute) [18], CCSA(China Communications Standards
Association) [19], TSDSI (Telecommunication Standards Develop-
ment Society, India) [20], ARIB (Association of Radio Industries
and Business, Japan) [21], 3GPP (Third Generation Partnership
Program), and OMA (Open Mobile Alliance) [22]. There are also
independent (or working under other organizations) alliances,
consortia, or standard-making bodies like IPSO Alliance [23], In-
ternet Engineering Task Force (IETF) [24], and OneM2M [25]. All
these bodies are working to create technical standards around
machine-to-machine (M2M) communications in various areas of
physical layer, data link layer, network layer, transport layer, and
application layer protocols and interfaces [26]. Figure 1.7 shows
the association among different M2M standardization organiza-
tions. These standardization efforts mainly take care of technical
and syntactic interoperability outlined in Figure 1.6.
In the semantic interoperation space, quite a bit of work has
been achieved in Open Geo Spatial Consortium (OGC) [27]. The
Sensor Web Enablement (SWE) and Sensor Mark-up Language
(SensorML) from OGC provides rich semantic description of sen-
sor data. These specifications from OGC are being put into stan-
dardization through the World Wide Web Consortium (W3C) [28]
as an Internet Standard for Spatial Data on the Web [29].
In the space of industry-specific interoperability, the Industrial
Internet Consortium (IIC) is doing a lot of work in terms of creat-
ing industry vertical-specific use cases, test beds, and technology
requirements around energy, healthcare, manufacturing, public
sector, and transportation verticals [30]. There are also some initia-
tives to standardize the smart city space, which can be thought of
as a large organization consisting of multiple industry verticals by
the International Organization for Standardization (ISO) [31]. ISO
has tried to identify the types of stakeholder and types of ICT sys-
tems of a smart city and has proposed an ecosystem-based domain](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-35-320.jpg)
![Internet of Things Today 31
model. Similar work has also been done by the U.K. standardiza-
tion body called British Standards Institution (BSI) [32] in the area
of smart city vocabularies.
It should be noted that although a lot of effort is being spent on
the technical interoperation part, given the complexity and diver-
sity of IoT systems, it is not possible or prudent to define standards
for each and every layer and component; each vertical and use case
will have different requirements. Rather, the focus in an IoT system
should be to adopt multiple existing standards in each layer and
define the Interoperation between those standards at the syntactic,
semantic, and organizational levels. There are quite a few consor-
tia trying to work in this space [33], notable among them being
Thread (backing from NEST in the connected home space looking
at the radio layer), Open Interconnect Consortium/IoTivity (back-
ing from Intel looking at the radio layer and upper layer protocols)
[34], AllSeen Alliance/AllJoyn (driven by Qualcomm in connected
home looking at upper layer protocols) [35], ITU-T SG 20 (backed
by ITU-T as the umbrella international standard for IoT and smart
cities/communities [36], IEEE P2413 (complete umbrella of IoT
standards from IEEE supporting 350 existing standards and 110
Figure 1.7 Relationship between different standardization
bodies in M2M space.](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-36-320.jpg)
![32 IoT Technical Challenges and Solutions
new standards) [37], Apple HomeKit (not a standard, but Apple’s
view of connected home) [38], and Google Brillo (Android for IoT)
[39].
1.5 Challenges and Open Problems
There is no disputing the fact that IoT has the potential to disrupt
every business and blur the boundaries between industry verti-
cals. There is no doubt about its potential impact. However, is this
just a hype or are there some real-life deployments in IoT? It is true
that most of the IoT deployments are happening in a pilot scale
and very few have scaled out beyond pilots. We try to explore the
main challenges in IoT that need to be addressed to convert the
hype around IoT to a practical reality. There seems to be five areas
of concern around which the challenges are emerging: (1) gearing
up the ICT infrastructure to the massive scale of IoT sensors and
data, (2) ensuring security of IoT systems and complying to pri-
vacy requirements for IoT data, (3) context-aware analytics of IoT
data leading towards business insights and value-adds, (4) afford-
able implementation and deployment of IoT system to ensure ROI,
and (5) ease of development of IoT analytics systems [40]. This is
depicted pictorially in Figure 1.8 and explained in detail next.
1.5.1 Handling the Scale
With immensely large number of sensors (potentially in billions)
connecting over the Internet, the scalability of the connecting net-
works to transport the sensor data to cloud, the scalability of the
storage systems to store and retrieve the huge volume of data and
the scalability of the computing infrastructure to analyze such huge
volume of data within the required response time of the applica-
tion; all become important considerations. The storage technology
has scaled quite well; however, the network and computing scal-
ability, unless addressed properly can always become a bottleneck
for practical implementation of IoT systems, especially under real-
time constraints on the response and energy constraints on the sen-
sor device side. The storage, although capable of handling very
large data, mainly tries to handle a limited number of large and](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-37-320.jpg)
![Internet of Things Today 33
very large files. In the IoT context, it is more likely to encounter a
huge number of small files; it is expected that the current storage
technology may need some tweaking to cater to this requirement.
The scalability of networks and computing [41, 42] is discussed in
detail in Chapter 2 with some real-world use cases and examples.
1.5.2 Security and Privacy
Security in IoT systems has a different dimension from the impli-
cation perspective. Because we are dealing with physical systems
consisting of sensors placed on real infrastructure and human be-
ings, a security lapse in the ensuing ICT system can potentially put
the infrastructure or human lives at risk. It is not only important to
make IoT systems as much secure as possible, given the constraints
of power, computing, and memory that typical embedded edge
devices have, but also it is important to do a proper security risk
Figure 1.8 Challenges in IoT deployments.](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-38-320.jpg)
![36 IoT Technical Challenges and Solutions
1.6 Conclusions
In this chapter, we first introduced the key macro business trends
that are being observed in the IoT space; this includes creating
value for end customers and new business models through better
understanding of end customers’ requirements. Then we outlined
the main business components in the application landscape that
are affected by IoT; these components include facility, product,
consumer, and supply chain. We covered this from the perspec-
tive of different industry verticals and gave a summary outline of
possible application use cases, including the potentially disrupting
ones. Then we introduced different technologies needed for IoT-
based systems including different architectural components span-
ning sensor devices to cloud systems connected in between via lo-
cal sensor networks, gateway devices, and Internet. Following that,
we introduced the need for standardization and interoperation in
IoT space and discussed how different standardization activities
are trying to address the standardization in terms of technical, syn-
tactic, semantic, and organizational interoperability. Finally, we
identified the real-world challenges in terms of scalability, security,
privacy, affordability, context-awareness, and ease of development
that must be addressed to create and deploy a full-scale IoT system
in the real world. Diving deeper into these challenges, identifying
their issues, and trying to provide feasible and reali�stic solution
approaches to them become the objective for the rest of the book.
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![38 IoT Technical Challenges and Solutions
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Bandyopadhyay, D., and J. Sen, “Internet of Things: Applications and Challenges
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![41
2
Scalability of Networks and Computing
2.1 Introduction
As outlined in Chapter 1, the Internet of Things (IoT) is all about
putting sensors on physical objects and human beings and con-
necting them to monitor, diagnose, or predict physical states and
events. Different studies predict that there will be 40 to 50 billion
IoT devices connected to the Internet by 2020 [1]. However, are the
network and computing infrastructure scalable enough to handle
the deluge of data that will be churned out by these devices? We
try to explore in depth this infrastructure scalability issue in this
chapter.
From a networking perspective, scalability is needed in every
layer of the communication stack. Here scalability means provid-
ing sufficient bandwidth, capacity, and low latency for transport-
ing the sensor data to handle the IoT application requirements. We
also need to keep in mind the coverage of the network to imple-
ment cost-effective systems. Hence, choice of the right communi-
cation technology, network topology, and network protocol has
paramount importance in this respect.](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-46-320.jpg)
![42 IoT Technical Challenges and Solutions
However, value of IoT systems is mainly derived from mean-
ingful analytics of the collected sensor data. The volume of data
generated by sensors being enormous leads us to the computing
scalability problem. Depending upon the application type, the re-
quired analytics need to be computed in real time or in near real
time or offline. The computing infrastructure needs to take cog-
nizance of this varying requirement and implement the required
system accordingly.
In Section 2.2, we present an exhaustive set of possible use cases
for applying IoT across various verticals like transportation, envi-
ronment,energy,water,securitysurveillance,retail,manufacturing,
agriculture, and healthcare. For each of the application use cases,
the requirements for communication bandwidth, network capac-
ity, latency, network coverage, timeliness of analytics, and compu-
tational complexity are tabulated. The analysis of communication
technologies, network architectures, and computing architectures
in subsequent sections draws extensively from these diverse re-
quirements from diverse use cases. In Section 2.3, we outline dif-
ferent communication technologies for IoT in form of personal,
wide area, or cellular networks and application layer protocols. In
Section 2.4, we will cover different network architectures for IoT
that can be built on top of these communication technologies to
provide scalability. These include various network topologies, pro-
tocol design, delay-tolerant networks, and software-defined net-
works. Subsequently, in Section 2.5, we will discuss some practi-
cal considerations for scalable IoT system deployment in form of
real-time and power considerations for sensor data analytics, and
utilizing the edge device for analytics, service-oriented platforms
for IoT application development.
2.2 Use Cases and Requirements
IoT can give rise to several interesting use cases, which hold the
potential to add value to the end user. At a broad level, IoT use
cases can be verticalized into various domains [2]. These use cases
demand and pose a lot of requirements on the infrastructure of
network, storage, and computing. One needs to understand these
use cases first before understanding the infrastructural require-
ments from these use cases. Hence, we start with a list of example](https://image.slidesharecdn.com/113053-250523195500-6bcc442b/85/IoT-Technical-Challenges-and-Solutions-1st-Edition-Arpan-Pal-47-320.jpg)
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- 6. IoT Technical Challenges and Solutions
- 7. For a listing of recent titles in the Artech House Power Engineering Library, turn to the back of this book.
- 8. IoT Technical Challenges and Solutions Arpan Pal Balamuralidhar Purushothaman
- 9. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the U.S. Library of Congress. British Library Cataloguing in Publication Data A catalog record for this book is available from the British Library. ISBN-13: 978-1-63081-111-2 Cover design by John Gomes © 2017 Artech House All rights reserved. Printed and bound in the United States of America. No part of this book may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording, or by any information storage and re- trieval system, without permission in writing from the publisher. All terms mentioned in this book that are known to be trade- marks or service marks have been appropriately capitalized. Artech House cannot attest to the accuracy of this information. Use of a term in this book should not be regarded as affecting the validity of any trademark or service mark. 10 9 8 7 6 5 4 3 2 1
- 10. To the IoT Research and Innovation community at Tata Consultancy Services
- 12. 7 Contents Preface 13 1 Internet of Things Today 15 1.1 Introduction: Key Trends 15 1.2 Application Landscape for IoT 19 1.2.1 IoT for Facilities 20 1.2.2 IoT for Products 20 1.2.3 IoT for Consumers 21 1.2.4 IoT for the Supply Chain 22 1.3 Technologies of IoT 23 1.3.1 Sensor Subsystem 24 1.3.2 Local Sensor Networks 25 1.3.3 Gateway Subsystem 25 1.3.4 Cloud Connectivity Networks 26 1.3.5 Cloud Subsystem 26 1.4 IoT Standardization 28 1.5 Challenges and Open Problems 32 1.5.1 Handling the Scale 32 1.5.2 Security and Privacy 33 1.5.3 Context-Aware Analytics 34 1.5.4 Affordable Implementation and Deployment 34 1.5.5 Ease and Economy of Development 35
- 13. 8 IoT Technical Challenges and Solutions 1.5.6 Realistic Deployments 35 1.6 Conclusions 36 References 36 Selected Bibliography 38 2 Scalability of Networks and Computing 41 2.1 Introduction 41 2.2 Use Cases and Requirements 42 2.2.1 Smart Transportation 43 2.2.2 Smart Environment 43 2.2.3 Smart Energy 44 2.2.4 Smart Water 44 2.2.5 Smart Security and Surveillance 45 2.2.6 Smart Retail and Logistics 45 2.2.7 Smart Manufacturing 46 2.2.8 Smart Farming 46 2.2.9 Smart Home 46 2.2.10 Smart Health 47 2.3 Application Classification Templates 47 2.4 Communication Technologies for IoT 49 2.4.1 Personal/Local Area Network Technologies 50 2.3.2 Technologies for Low-Power Wide Area Networks (LPWAN) 53 2.3.3 Cellular Technology for IoT 54 2.4.4 Application-Level Protocols 55 2.5 Scalable Network Architectures for IoT 56 2.5.1 Network Topologies 57 2.5.2 IoT Protocol Design Space 58 2.5.3 Delay-Tolerant Networks 58 2.5.4 Software-Defined Networking (SDN) 60 2.6 Practical Considerations for Scalable IoT System Implementation 62 2.6.1 Real-Time and Power Considerations for IoT Applications 62
- 14. Contents 9 2.6.2 Utilizing the Edge Devices for Computing 64 2.6.3 Need for a Platform for Application Development and Deployment 65 2.7 Conclusions 67 References 68 Selected Bibliography 68 3 Security and Privacy 73 3.1 IoT Security: A Perspective 73 3.1.1 Business Objectives of Security 75 3.2 IoT Security: Key Requirements 75 3.3 IoT Security Challenges 79 3.3.1 Typical Threats on Various IoT Subsystems 80 3.4 Data Protection 81 3.5 Communication Security 83 3.5.1 Cryptographic Key Management 84 3.6 Identities and Identity Management 86 3.7 Authentication 87 3.8 Access Control 88 3.9 Secure Software Updates 89 3.10 Privacy in IoT Systems 90 3.11 System-Level Security Assessment 92 3.11.1 Risk-Based Security 92 3.11.2 Threat Modeling and Risk Estimation 94 3.12 IoT Security: Practical Guidelines 100 3.13 Summary 103 References 104 Selected Bibliography 105
- 15. 10 IoT Technical Challenges and Solutions 4 Sensor Informatics and Business Insights 109 4.1 Introduction 109 4.2 Sensor Signal Processing 111 4.2.1 Signal Acquisition and Conditioning 111 4.2.2 Signal Representation 114 4.2.3 Feature Extraction and Inference 116 4.3 Semantic Interpretation of Processed Information 119 4.3.1 Machine Learning 119 4.3.2 Rule Engine 123 4.3.3 Reasoning 124 4.4 Business Insights from Interpreted Knowledge 126 4.4.1 Visual Analytics 126 4.4.2 Modeling and Simulation 127 4.4.3 Optimization and Planning 127 4.5 Data and Algorithm Marketplaces as New Business Models 128 References 129 Selected Bibliography 132 5 Mobile Sensing 135 5.1 Introduction 135 5.2 Applications and Use Cases for Mobile Sensing 136 5.2.1 Mobile Sensing for Environmental Monitoring 137 5.2.2 Mobile Sensing for Emergency Response 137 5.2.3 Collaborative Sensing for Urban Transportation 138 5.2.4 Robots in Healthcare 138 5.2.5 Robotic Telesensing and Operation 138 5.2.6 Aerial Robots for Spatial Intelligence 139 5.3 Technologies and Challenges in Mobile Sensing 143 5.3.1 Smartphone-Based Sensing 143
- 16. Contents 11 5.3.2 Robotic Sensor Networks 147 5.3.3 UAV for Aerial Mapping 149 5.4 Economics of Mobile Sensing 152 5.5 Summary 154 References 154 Selected Bibliography 155 6 Democratizing Analytics: Analytics as a Service 157 6.1 The Need for IoT Analytics 157 6.2 The Need for Analytics as a Service 161 6.3 Analytics as a Service for Developers: Model-Driven IoT 165 6.4 An Example of a Model-Driven IoT Framework 168 6.4.1 Domain Concern 168 6.4.2 Development and Orchestration Concern 169 6.4.3 Infrastructure Concern 171 6.5 Summary 172 References 173 Selected Bibliography 174 7 The Real Internet of Things and Beyond 177 7.1 Realistic Internet of Things 177 7.1.1 Key Contributing Factors to Real IoT 178 7.2 Real IoT Is a Network of Trade-Offs 181 7.2.1 Some of the Common Trade-Offs Encountered in IoT Systems and Applications 182 7.2.2 Safety on the Machine Floor: An Illustrative Example 184
- 17. 12 IoT Technical Challenges and Solutions 7.3 Drivers for the Next Wave of IoT 186 7.3.1 Resilient IoT Systems 186 7.3.2 Cognitive IoT 187 7.3.3 Impact of 5G as the Next Wave of Communication Technology in IoT 188 7.4 Concluding Remarks 190 References 191 Selected Bibliography 191 About the Authors 193 Index 195
- 18. 13 Preface We started working on the Internet of Things (IoT) in 2010. Its ear- lier evolution as wireless sensor network was started even earlier. We conceptualized and started building a horizontal IoT platform for multiple applications. As we struggled through the process of building the platform, one thing became clear: IoT is not a mono- lithic technology stack. It is an abstraction for multiple technolo- gies, some of which had existed long ago. There were three key trends that were driving adoption of IoT: the cost of sensor devices were coming down, the cloud technology was becoming mature, and the capacity of the Internet was getting better. However, as we started moving beyond pilot deployments, we realized that it is not a technology play alone. IoT systems must consider business goals and return-on-investment (RoI) aspects early in the design of solutions. This prompted us to look at IoT systems aside from its hype as real IoT systems focusing on a realistic value proposition to businesses and social applications. This needs an integrated ap- proach with abstraction of technology components such as com- munication, computing, storage, mobility, security and privacy, business value-add via analytics, and economy of mobile sensing and automation. This book is an attempt to knit together a prag- matic approach towards real IoT covering all of these. In Chapter 1, we outline the IoT as it is perceived and seen today; we discuss about key trends, IoT application landscape, IoT technologies, and standardization and introduce the challenges and open problems. We devote Chapters 2 and 3 into the core tech- nologies of IoT with regard to communication, computing, stor- age, security, and privacy. In Chapter 4, we introduce sensor in- formatics as the main driving force for IoT for creating business
- 19. 14 IoT Technical Challenges and Solutions value-adds. In Chapter 5, we bring in the concept of mobile sens- ing, where sensors mounted on platforms that are mobile (like mo- bile phones, robots, and drones), can bring in economies of scale during deployment. In Chapter 6, we introduce the concept of au- tomation of the analytics via analytics as a service, which leads to- wards democratization of analytics resulting in easing the special- ized skill requirement for IoT. Finally in Chapter 7, we summarize and outline the real requirements for a realistic IoT deployment and try to provide a glimpse of technologies to come in future. This book would not have been possible without the help of several people. First and foremost, we want to thank Mr. K. Ananth Krishnan, chief technology officer of TCS who encouraged us to deep dive into IoT research. We want to thank various sci- entists and researchers from the Embedded Systems and Robot- ics Research Area of TCS Research and Innovation, whose work provided the backbone of the content of this book. We also thank the editorial staff of Artech House, whose diligent follow-ups and feedback kept us on our toes and helped us in delivering the book on time. Last but not the least, we want to thank our children, wives, and parents without whose support and inspiration the writing of this book would not have been possible. We believe that this book would be useful to a wide audience including practitioners and people who are in general interested in exploring a pragmatic approach to IoT. This is not a technology cookbook that is prescriptive in nature, but a suggestive one dis- cussing practical considerations towards building real IoT.
- 20. 15 1 Internet of Things Today 1.1 Introduction: Key Trends The Internet of Things (IoT) is defined as [1]: “the network of physical objects—devices, vehicles, buildings and other items which are embedded with electronics, software, sensors, and net- work connectivity, which enables these objects to collect and ex- change data.” Forbes defined it as [2]: “a giant network of connect- ed things (which also includes people)—between people-people, people-things, and things-things.” A thing, in IoT, can be physical objects like a bridge, a building, or a transport having sensors like vibration, temperature, and accelerometer, respectively, or human beings like a person wearing a smart watch or having a biochip implant; in IoT, all of them can have an IP address through which the sensor data can be transferred over a network (including the Internet). The concept of IoT came into being in 1999. However, the first reported IoT-like device was built at Carnegie Melon Univer- sity in the early 1980s; it was a soft drink machine connected to the Internet allowing availability to be checked online in real time [3]. Why is IoT being regarded as one of the most disruptive tech- nologies that will drive business digitization? The answer is
- 21. 16 IoT Technical Challenges and Solutions simple. Until now, the use of information technology in most of the business verticals were to improve and automate the business support services and not the core physical business process. IoT’s ability to sense the physical world and digitize the physical world context will be bring fundamental change and digitization to the existing physical business processes across all verticals leading to either significant reduction in operation cost or improvement in re- sponse time and quality or bringing in real-time customization in the products or service offerings or helping in increasing security and ensuring compliance. For end consumers, IoT has the potential to change the way that products and services are consumed; there will be much more real-time understanding of consumer needs and subsequent personalization thereof. This will be discussed in further detail with examples in Section 1.2. The market for IoT is clearly heating up. According to Gartner, there were scheduled to be 6.4 billion connected “things” in 2016 (an increase of 30% from 2015), with a projected deployment of 20.8 billion things by 2020 (5.5 million new things to be connected every day) [4]. Gartner has also reported that, in 2016, IoT would be instrumental in U.S. dollar spending in total services of $235 bil- lion and in total end-point spending of $1.4 trillion (which is an in- crease of 22% from 2015). By 2020, the total IoT end-point spending was projected to rise to $3 trillion. The mix of consumer compared with business applications in this spending is also interesting; in 2015, the consumer versus business application share in the overall spending was 35% to 65%. The consumer spending was projected to increase over the years and, by 2020, the consumer versus busi- ness application share in the overall spending would be 51% to 49%. In a recent global IoT trend study by Tata Consultancy Services [5] involving more than 3,500 executives across small and large corporations all over the world, some interesting facts about IoT trends have come to light: • Nearly 80% companies surveyed have initiated IoT programs. On average, such companies had a revenue increase of 16%. • Nearly half of them either track customers or monitor opera- tions through mobile applications and IoT technologies.
- 22. Internet of Things Today 17 • The spending in IoT seems to be directly proportional to actual product offering price of the company. Companies whose prod- ucts’ prices are more than $10 million will spend an average of $335 million, while those with product prices of $100 or less will spend an average of only $39 million. This can be attributed to the fact that the return on investment (ROI) for IoT infrastruc- ture and systems are more justified when it is used to improve, optimize, protect, and monitor high-value products. • There will be varying degree of IoT adoption across the four major geographies: North America, Europe, Asia-Pacific, and Latin America. The IoT spending will be more in North Ameri- can and European companies (with North American companies spending 0.45% of revenue, and their European counterparts spending 0.40%). The Asia-Pacific companies are not going to lag much behind in spending, with IoT-related investment amounting to 0.34% of revenue, while the Latin American en- terprises will spend around 0.23% of revenue on IoT. • Companies who demonstrated largest revenue increases from IoT initiatives had some common traits: • They were able to create substantial value for their end cus- tomers and not just value for themselves. • They could deliver that value through new business mod- els focusing on product and service offerings around the IoT data. • All of them leveraged the IoT technology to understand firsthand their product and service performances and usage patterns with regard to their end customers. The trends described above are macro business trends being observed globally in the IoT space. Next, we will explore slightly deeper details of the application landscape for IoT in different in- dustry verticals. Some of the recent successful deployments of IoT that demon- strate the above traits include [6]: 1. Energy monitoring and usage optimization across facilities for large enterprises (final value add: reduction in energy cost);
- 23. 18 IoT Technical Challenges and Solutions 2. Quality monitoring in the food processing chain (final value add: ensuring same quality and taste for the end user); 3. Predictive analytics of refrigeration systems (final value add: reduction in overall maintenance cost and increase of operat- ing life of the machines); 4. Real-time alerting around wellness and activity for elderly people at home and factory people in hazardous areas (final value add: quick intervention with the appropriate medical help). All of these have one common factor: all of them needed to demonstrate that the value-add (economic or otherwise) outscores the cost of infrastructure investments, thereby justifying the ROI. Almost every imaginable type of company seems to have an IoT strategy in 2016 [7]. Here are some of the unlikeliest ones to throw their hat into the IoT ring: the toymaker Mattel with the connected Barbie doll [8], the gamemaker Atari with smart home products [9], the nonprofit browser company Mozilla with an operating sys- tem for connected devices [10], and the credit card company Visa with integration of payments inside IoT devices [11]. There are a lot of innovative products and solutions coming out in the mar- ket in the IoT space. Their sustainability in the marketplace will depend again on the value-add that they bring to the end custom- er; however, it is worthwhile to follow these innovations as some of them are bound to succeed to build a sustainable business [12, 13]. The interesting ones worth mentioning are automated cook- ing systems, self-charging and wireless-charging platforms, wa- ter monitoring systems, blindspot-free rear-view mirrors for cars, three-dimensional (3-D) printing pens, and wearable monitoring of human physiology. There are also innovative services and busi- ness models being created around IoT-enabled products; for ex- ample, instead of selling a washing machine, the washing machine vendor can give it away for free and charge the consumer per wash based on the time and the load. Another interesting trend is the emergence of an ecosystem for IoT. The chip makers like Intel, Qualcomm, TI, ARM, and Arduino are providing the core sensing and processing technology with low power consumption and universal connectivity that can be embed- ded into the “Things.” The equipment vendors like Cisco, Huawei,
- 24. Internet of Things Today 19 Ericsson, and Nokia are trying to create new network equipment that address the new challenges posed by IoT, while the large tele- com operators all over the world are trying to create a value-added services model around machine to machine (M2M) communica- tions. Finally, the major software vendors like IBM, Google, Micro- soft, SAP, Accenture, and TCS are focusing on data handling, data analytics, and system integration aspects of IoT. 1.2 Application Landscape for IoT A typical IoT stack is outlined in Figure 1.1. The sensors are put on physical objects and human beings to sense their context. The meaningful information from the sensor signals is extracted and sent over the Internet to the cloud using a gateway device. Fur- ther analysis of the extracted information is done to understand the physical events and business insights are derived thereof in Figure 1.1 Internet of Things stack.
- 25. 20 IoT Technical Challenges and Solutions different business verticals. These business insights trigger the re- sponse cycle, which may range from a completely offline process change to completely real-time actions taken to mitigate the effects of the underlying physical event. This sense, extract, analyze, and respond cycle presented here is found to be common to all possible IoT applications. The final benefits to the business and stakehold- ers in an IoT system will invariably come from either reduced cost due to improved operational efficiency via IoT-based monitoring or better insights from analytics of IoT data creating value and competitive advantage for the stakeholders. There are four core business areas in different verticals where IoT can be applied to generate value through sensing and subse- quent analysis and response: facility, product, customer, and sup- ply chain [14]. The type of applications can be monitoring, control, optimization, or autonomous depending upon the use case. These are depicted pictorially in Figure 1.2. 1.2.1 IoT for Facilities Most of the industry verticals that have fixed assets in form of build- ings, campuses, infrastructure and heavy equipment will benefit from deploying IoT for its facilities. There can be monitoring ap- plications like perimeter security through video surveillance, con- trol applications like building energy management via smart meter data analytics or predictive maintenance for machines, and optimi- zation applications like emergency evacuation via people sensing and localization. Such applications will generate business value in terms improved security or compliance, lower cost of operation, lower cost of maintenance, and improved lifespan of assets in al- most all industry verticals including travel and hospitality, retail, energy and utilities, banking, industrial manufacturing, farming, healthcare, and life sciences that usually have significant real es- tate or office spaces or infrastructure. 1.2.2 IoT for Products Using IoT for products can take two forms: it can ensure quality when the product is built, or it can detect finished product perfor- mance in the field. The former requires putting sensors into the
- 26. Internet of Things Today 21 product to monitor its quality as it is manufactured and to reject or segregate bad quality products and can therefore be regarded as a monitoring application. The latter requries putting sensors on products deployed in the field, thereby having a monitoring appli- cation for generating feedback and future product requirements, a control application for predictive maintenance of such products to improve lifespan, and an optimization application in the form of prescriptive analytics to ensure lower cost of operations and maintenance. All industry verticals that produce and deploy ma- chinery or packaged goods in the field can benefit from such IoT deployments including automotive and transportation, telecom- munications and media, energy and utilities, environment, retail, and consumer packaged goods, industrial manufacturing, farming and healthcare, and life sciences. 1.2.3 IoT for Consumers Using the sensors present in smart phones and wearable and oth- er unobtrusive sensors like a camera, it will be possible to sense, locate, and understand customers, which in turn, will be able to provide enterprises with the invaluable knowledge about the cus- Figure 1.2 IoT application space.
- 27. 22 IoT Technical Challenges and Solutions tomers’ likes and dislikes. This knowledge can be used to create actionable insights around a customer profile, leading towards the personalization of the product or service offering and resulting in cost and experience benefits for the end user. Almost all such ap- plications can be regarded as control or optimization kinds of ap- plications. While this kind of possibility is there in each and every industry vertical with human beings as their ultimate consumers, there are two industries which will have the potential to be dis- rupted via IoT-based consumer monitoring. First, in-store and on- line retail for personalized recommendation-based shopping expe- rience will become possible using various physical, physiological, and biological sensors on consumers deployed in form of mobile phone or wearable. Second, healthcare or life sciences industry can change from an illness-driven industry to a wellness-oriented industry providing personalized wellness plans, diagnosis, treat- ment protocols, medicine, and therapy based on each patient’s health condition sensed through various physical, physiological and biological sensors on consumers deployed in form of mobile phone or wearable or implantable. 1.2.4 IoT for the Supply Chain A supply chain (SC) is a network of supplier, production centers, storage, distribution centers, sellers, and buyers. Tracking an item in the supply chain using sensor technology can provide a near-re- al-time view, which, in turn, can lead to improve of the efficiency of the supply chain via advanced optimization techniques. Almost all industry verticals that have tangible products as offerings typically have supply chains that can be optimized via IoT deployment. In particular, consumer packaged goods and energy and utilities in- dustries are heavily dependent on this supply chain and stand to be disrupted via suitable deployment of sensors and the associated analytics and optimization. Automation in supply chain will also come via technologies like driverless cars that can be regarded as a mix of IoT, robotics, and artificial intelligence; it stands to disrupt the automotive and transportation industry. Table 1.1 details the IoT application landscape described above.
- 28. Internet of Things Today 23 1.3 Technologies of IoT The technology stack is IoT is described in Figure 1.3. Architec- turally, typical IoT system is divided into three subsystems: sen- Table 1.1 IoT Application Landscape Industry Vertical Type of Application Potential Disruption via IoT Facility Product Consumer Supply Chain Automotive and transpor- tation Building or infrastructure surveillance Car monitoring and maintenance Driving behavior monitoring Automation via driverless cars High, through driverless cars and drones Travel and hospitality Building surveillance Room monitoring and maintenance Customer behavior monitoring Optimization of operations, recommender systems Medium, through personalization Retail and CPG Building or infrastructure surveillance End-product sensing and monitoring Customer behavior monitoring Optimization of operations, Recommender Systems High, through personalization Energy and utilities Building or infrastructure surveillance Quality monitoring and control: electricity and water Customer behavior influencing Demand response optimization and peak load management Medium, through peak load management via customer behavior influencing Banking, insurance, and financial services Building or infrastructure surveillance End-product sensing and monitoring Customer behavior monitoring Optimization via risk profiling Medium, through personalization Industrial manufacturing Building, infrastructure, or equipment surveillance End-product quality control and defect inspection Customer feedback monitoring Optimization of Operations High, through improved efficiency Farming Building, infrastructure, equipment surveillance Produce sensing and monitoring Customer feedback monitoring Optimization of operations Medium, through improved efficiency Environment Infrastructure surveillance Environmental sensing and monitoring People health and feedback monitoring Visualization Medium, through real-time views of pollution map Healthcare and life sciences Building, infrastructure surveillance End-product sensing and monitoring Patient prognostics and health risk profiling Optimization of operations High, through personalized healthcare Telecommu- nication and media Building, infrastructure surveillance End-product sensing and monitoring Customer feedback monitoring Recommender systems Medium, through personalized content recom- mendation
- 29. 24 IoT Technical Challenges and Solutions sor subsystem, gateway subsystem, and cloud subsystem with the necessary underlying network connectivity between the subsys- tems. The sensor subsystem is connected to the gateway subsystem via local sensor networks. The gateway subsystem is connected to cloud subsystem via a wide area network like the Internet. Each of these three subsystems connected via two types of net- working are described in brief next. 1.3.1 Sensor Subsystem The sensor subsystem uses connected transducers to covert physi- cal world stimuli into digitized electrical signals. Once digitized, these signals can be transported to gateway devices for further processing via wired or wireless local sensor networks. Signal conditioning of the analog transducer signal followed by analog- to-digital (A/D) conversion and subsequent digital signal pro- cessing of the digitized data is required to produce good-quality sensor data under noisy environments. Dynamic range of the sensor transducer (should accommodate extreme cases of physi- cal world stimuli), sampling frequency for the sensor digitization (as per Nyquist Sampling theory, this should be more than twice the maximum useful frequency of the physical world stimuli), and minimization of energy consumption (this should be as low as pos- Figure 1.3 IoT technology stock.
- 30. Internet of Things Today 25 sible to conserve battery and extend sensor life in the field) are a few of the important technical considerations for sensor subsystem deployment. Sensor subsystems are typically computer memory and power-constrained devices, but advances in semiconductor technology are making sensor devices more and more powerful yet miniaturized in this aspect. The sensors can sense environmen- tal properties like temperature and pressure, physical properties like location, velocity, acceleration, strain, vibration, contact, and proximity, and physiological/biological properties like heart rate, blood pressure, electrocardiogram (ECG), and electroencephalo- gram (EEG). Advances in science of mechanics, electromagnetics, acoustics, thermodynamics and optics, chemistry, and biology are also creating increasingly more new transducers making it pos- sible to sense newer physical events. 1.3.2 Local Sensor Networks Local sensor networks carry the sensor data from sensors to a gate- way device for further processing and transport of the data over the Internet or other public networks to the cloud. They can have fixed network topologies like star, ring, bus tree, or mesh networks or they can be formed in an ad hoc manner. Shared media access protocols using time division multiple access (TDMA), frequency division multiple access (FDMA) or code division multiple access (CDMA) technologies are used on top of the physical network con- nectivity for seamless transportation of the sensor data. Bluetooth and Zigbee (discussed in detail in Chapter 2) are the most popular wireless sensor network technologies, while WiFi also can be used in some scenarios. Depending upon the use case, the sensors can be interconnected using wired network also or can be connected point-to-point to the gateway using serial interfaces like universal serial bus (USB). 1.3.3 Gateway Subsystem Gateway subsystems connect to local sensor networks on one side and public networks like the Internet on the other side. They typi- cally operate as a router, gateway, or switch bridging the two dif- ferent types of physical network and protocol stacks. For exam- ple, the public network is typically Internet Protocol (IP) enabled,
- 31. 26 IoT Technical Challenges and Solutions whereas in most of the cases the local sensor network is not. One of the sensor nodes in local sensor network can become the gateway or there can be dedicated gateway devices. Because typically gate- way devices can have more memory and computing power and in many scenarios are electrically powered, it is possible to execute some of the high sampling rate sensor signal processing and noise cancellation algorithms in the gateway itself so that clean data at a reduced rate goes to the cloud. 1.3.4 Cloud Connectivity Networks The cloud connectivity networks are typically IP networks; in most of the cases, this will be the Internet for IoT systems. However, there may be scenarios where private networks and private clouds are deployed depending upon the use case requirements. Band- width, latency, reliability, and security of this network are critical for viable implementation of these systems. 1.3.5 Cloud Subsystem The cloud subsystem receives the sensor data over IP, stores them, and allows analytics to be run on the stored data. The elastic nature of cloud is needed to cater for uneven demand of processing and storage emanating from fluctuating nature of the sensor data. In some cases, the data is processed even before storing; such systems are known as complex event processing (CEP) systems. The stor- age database needs to handle huge data coming from sensors and hence needs to be Big Data-enabled; there may be limited number of huge files (like video surveillance data) or huge number of small files (coming from a lot of sensors). The processing and analytics engine is the software service available on the cloud to derive busi- ness insights from the sensor data, as outlined earlier. It is clear that the main value delivered by the IoT technolo- gy comes from information extraction and analytics of the sensor data. In Figure 1.4, we present the technology stack for IoT ana- lytics, which can be distributed across sensors, gateways, and the cloud. The raw sensor data at the bottom of the knowledge pyra- mid needs to be processed to create contextual information trying to answer questions like who did what, where, and when. It boils down to summarizing or visualizing the sensor data along with
- 32. Internet of Things Today 27 identity, location, and timestamp information. This contextual in- formation can be applied to build knowledge models of the physi- cal system. The models can either be built on scientific knowledge linking how the physical event generated the information or can be statistical models learned from the data or a hybrid of both. These models help in having a better understanding of the system answering questions like why the physical event has happened. These kinds of insights are extremely useful in business operations helping in business decision-making and value-add. Finally, a col- lection of such understandings can create the true wisdom, which can prescribe what should be done to prevent the physical event (if it is counterproductive to business) or facilitate the physical event (if it is aiding business outcome) or run the current operation fast- er, cheaper, or better. The knowledge pyramid can be explained using a simple ex- ample. Let us assume that a building is installed with tempera- ture and gas sensors. The data from these sensors form the raw data layer. The visualization and reporting of these sensor data in a spatiotemporal map can be the contextual information layer. The knowledge model can be simple, telling that because there is high abnormal temperature in certain zones, the building at that part must have caught fire. The understanding layer can do causal anal- ysis of the temperature sensor data and gas sensor data and infer that the fire has been caused by a gas leak and hence appropriate Figure 1.4 Technology stack for IoT analytics.
- 33. 28 IoT Technical Challenges and Solutions fire extinguishers capable of handling gas fire needs to be sent. The final wisdom layer would be how to design the building infra- structure to prevent such fires from happening or minimizing the number of people affected due to such events. It is quite obvious that as we climb up the pyramid the business value of the analyzed data becomes more and more useful. How- ever, the volume of the data to be analyzed continues to reduce as we move from information to knowledge to insights to wisdom. 1.4 IoT Standardization It is quite clear from looking at the diversity of the technologies and vertical domains, that standardization will become one of the key elements to the success of IoT. All stakeholders of IoT systems will need to invest substantially into standardization to bring in interoperability and avoid vendor lock-in. The areas in which stan- dardization efforts are necessary can be looked upon from differ- ent perspectives. In one view, the standardization can happen in IoT domain ap- plications (including domain-specific physical infrastructures such as building, road, and traffic), in software services layer [via ap- plication programming interfaces (APIs) and software as a service or (SaaS)], the ICT infrastructure (consisting of cloud, Internet and connectivity), and edge devices (consisting of sensing devices and embedded systems). This is pictorially depicted in Figure 1.5. In another view from interoperation perspective, as outlined by European Telecommunication Standards Institute (ETSI), IoT standardization can be regarded as a set of intermixing interopera- tion requirements in technical, syntactic, semantic, and organiza- tional level [15]. This is represented in Figure 1.6. The technical in- teroperation requires standardization in basic communication and computing systems for IoT (distributed systems, physical commu- nication systems, and embedded systems). The syntactic interop- eration tries to standardize how these different computing systems talk to each other using standard communication protocols. The semantic interoperation brings in the requirement of interpreting the IoT data in a common vocabulary, which can be either technol- ogy-specific or physical infrastructure-specific or vertical domain- specific or a combination of all. The organizational interoperation
- 34. Internet of Things Today 29 deals with the domain view to ensure interoperation between two different systems in the same vertical domain and also with a system-of-systems view where two different IoT systems from two different organizations (e.g., transportation and the healthcare system of a city) can talk to each other in a common language so that larger, value-added services can be built using multiple or- ganizational systems (e.g., healthcare systems and transportation Figure 1.5 Services and technology-level standardization for IoT. Figure 1.6 Different levels of interoperability in IoT.
- 35. 30 IoT Technical Challenges and Solutions systems talking to each other to find out the fastest route for dis- patching an ambulance). Many of the notable standardization organizations all over the world are working in the area of IoT Standardization under the umbrella of the International Telecommunication Union–Telecom (ITU-T) [16]. They include TIA (the U.S. Telecommunications In- dustry Association) [17], ETSI (European Telecommunications Standards Institute) [18], CCSA(China Communications Standards Association) [19], TSDSI (Telecommunication Standards Develop- ment Society, India) [20], ARIB (Association of Radio Industries and Business, Japan) [21], 3GPP (Third Generation Partnership Program), and OMA (Open Mobile Alliance) [22]. There are also independent (or working under other organizations) alliances, consortia, or standard-making bodies like IPSO Alliance [23], In- ternet Engineering Task Force (IETF) [24], and OneM2M [25]. All these bodies are working to create technical standards around machine-to-machine (M2M) communications in various areas of physical layer, data link layer, network layer, transport layer, and application layer protocols and interfaces [26]. Figure 1.7 shows the association among different M2M standardization organiza- tions. These standardization efforts mainly take care of technical and syntactic interoperability outlined in Figure 1.6. In the semantic interoperation space, quite a bit of work has been achieved in Open Geo Spatial Consortium (OGC) [27]. The Sensor Web Enablement (SWE) and Sensor Mark-up Language (SensorML) from OGC provides rich semantic description of sen- sor data. These specifications from OGC are being put into stan- dardization through the World Wide Web Consortium (W3C) [28] as an Internet Standard for Spatial Data on the Web [29]. In the space of industry-specific interoperability, the Industrial Internet Consortium (IIC) is doing a lot of work in terms of creat- ing industry vertical-specific use cases, test beds, and technology requirements around energy, healthcare, manufacturing, public sector, and transportation verticals [30]. There are also some initia- tives to standardize the smart city space, which can be thought of as a large organization consisting of multiple industry verticals by the International Organization for Standardization (ISO) [31]. ISO has tried to identify the types of stakeholder and types of ICT sys- tems of a smart city and has proposed an ecosystem-based domain
- 36. Internet of Things Today 31 model. Similar work has also been done by the U.K. standardiza- tion body called British Standards Institution (BSI) [32] in the area of smart city vocabularies. It should be noted that although a lot of effort is being spent on the technical interoperation part, given the complexity and diver- sity of IoT systems, it is not possible or prudent to define standards for each and every layer and component; each vertical and use case will have different requirements. Rather, the focus in an IoT system should be to adopt multiple existing standards in each layer and define the Interoperation between those standards at the syntactic, semantic, and organizational levels. There are quite a few consor- tia trying to work in this space [33], notable among them being Thread (backing from NEST in the connected home space looking at the radio layer), Open Interconnect Consortium/IoTivity (back- ing from Intel looking at the radio layer and upper layer protocols) [34], AllSeen Alliance/AllJoyn (driven by Qualcomm in connected home looking at upper layer protocols) [35], ITU-T SG 20 (backed by ITU-T as the umbrella international standard for IoT and smart cities/communities [36], IEEE P2413 (complete umbrella of IoT standards from IEEE supporting 350 existing standards and 110 Figure 1.7 Relationship between different standardization bodies in M2M space.
- 37. 32 IoT Technical Challenges and Solutions new standards) [37], Apple HomeKit (not a standard, but Apple’s view of connected home) [38], and Google Brillo (Android for IoT) [39]. 1.5 Challenges and Open Problems There is no disputing the fact that IoT has the potential to disrupt every business and blur the boundaries between industry verti- cals. There is no doubt about its potential impact. However, is this just a hype or are there some real-life deployments in IoT? It is true that most of the IoT deployments are happening in a pilot scale and very few have scaled out beyond pilots. We try to explore the main challenges in IoT that need to be addressed to convert the hype around IoT to a practical reality. There seems to be five areas of concern around which the challenges are emerging: (1) gearing up the ICT infrastructure to the massive scale of IoT sensors and data, (2) ensuring security of IoT systems and complying to pri- vacy requirements for IoT data, (3) context-aware analytics of IoT data leading towards business insights and value-adds, (4) afford- able implementation and deployment of IoT system to ensure ROI, and (5) ease of development of IoT analytics systems [40]. This is depicted pictorially in Figure 1.8 and explained in detail next. 1.5.1 Handling the Scale With immensely large number of sensors (potentially in billions) connecting over the Internet, the scalability of the connecting net- works to transport the sensor data to cloud, the scalability of the storage systems to store and retrieve the huge volume of data and the scalability of the computing infrastructure to analyze such huge volume of data within the required response time of the applica- tion; all become important considerations. The storage technology has scaled quite well; however, the network and computing scal- ability, unless addressed properly can always become a bottleneck for practical implementation of IoT systems, especially under real- time constraints on the response and energy constraints on the sen- sor device side. The storage, although capable of handling very large data, mainly tries to handle a limited number of large and
- 38. Internet of Things Today 33 very large files. In the IoT context, it is more likely to encounter a huge number of small files; it is expected that the current storage technology may need some tweaking to cater to this requirement. The scalability of networks and computing [41, 42] is discussed in detail in Chapter 2 with some real-world use cases and examples. 1.5.2 Security and Privacy Security in IoT systems has a different dimension from the impli- cation perspective. Because we are dealing with physical systems consisting of sensors placed on real infrastructure and human be- ings, a security lapse in the ensuing ICT system can potentially put the infrastructure or human lives at risk. It is not only important to make IoT systems as much secure as possible, given the constraints of power, computing, and memory that typical embedded edge devices have, but also it is important to do a proper security risk Figure 1.8 Challenges in IoT deployments.
- 39. 34 IoT Technical Challenges and Solutions assessment of potential implications on the physical infrastructure if a security breach happens. However, because IoT systems sense contextual information, they can contain sensitive personal data like location and health. The end user or the infrastructure own- er should be able to determine or have control over with whom their data can be shared. If appropriate measures are not imple- mented, rich personal data falling into the wrong hands can have catastrophic consequences. In Chapter 3, we discuss in depth the implications of security and privacy in IoT systems and possible approaches towards solving the issues. 1.5.3 Context-Aware Analytics It is important to understand that analytics of IoT systems does not stop at visualizing sensor data in form of charts and graphs in geo- tagged maps. In line with Figure 1.4, it is also imperative for IoT analytics to gain deeper knowledge and insights into the physical system events (how and why). This means building and validating models backed by physical science of physics, chemistry, or biol- ogy or learning statistical models backed by data science. Having a validated model may allow businesses to predict some outcome from the IoT system and use that contextual knowledge for human- intervened action and control. Going one step further, if the system analytics can automatically infer what actions need to be taken to have the desired effect in the business process or operation, then a complete autonomous system can be built that can be regarded as a giant control system. In Chapter 4, we describe different aspects of the sensor informatics and business insights needed to achieve the above objectives. 1.5.4 Affordable Implementation and Deployment A bulk of the cost of IoT deployment goes into the hardware and operational cost of deploying sensors in the field. While many sys- tems will need fixed, dedicated sensor installations and will have requisite value-add coming from the data collected from them jus- tifying their ROI, there will be many cases where such ROI cannot be justified, nor is there a need for putting fixed, dedicated sen- sor installations. Novel methods like ad hoc sensor deployment on mobile platforms like robots and drones or crowdsourcing
- 40. Internet of Things Today 35 physical world data from the sensors of millions of mobile phones carried by people need to be regarded as possible options; oth- erwise, many potential use cases of IoT may not see the light of the day beyond pilots. In Chapter 5, we introduce mobile ad hoc sensing technologies and discuss how they can lead to economy of deployment. 1.5.5 Ease and Economy of Development Just like open application development platforms and applications stores had revolutionized the mobile phone market, such open platforms are also needed for successful adoption of IoT. When more people start writing value-adding applications on the IoT data, then only adoption of IoT will rise significantly and will solve real problems plaguing today’s business and society creating the necessary impact. Today, most of the IoT systems are closed silos, and that needs to change. We call it “democratization” of IoT ana- lytics where different applications can provide different analytics from the IoT data as a service. This, coupled with automation of some parts of the analytics using artificial intelligence, can lead to a cost-effective and easy way to create value from IoT sensor data. In Chapter 6, we introduce this subject and give it a detailed treatment. 1.5.6 Realistic Deployments To realistically deploy IoT-based systems in the field, we need to consider robustness, user-centricity, choice of the right processes and business model, and use of the right ecosystems. This is elabo- rated in detail in Chapter 7, which also outlines how a realistic de- ployment of IoT is always a trade-off between multiple conflicting factors, notable among them being direct hardware sensing versus indirect software sensing, application-specific hardware versus general-purpose hardware, security versus user experience, bat- tery lifetime versus performance, and communication range ver- sus power versus bandwidth. Further resilient and cognitive IoT systems in conjunction with fifth generation (5G) wireless commu- nication systems will pave the way for realistic application deploy- ments in near future.
- 41. 36 IoT Technical Challenges and Solutions 1.6 Conclusions In this chapter, we first introduced the key macro business trends that are being observed in the IoT space; this includes creating value for end customers and new business models through better understanding of end customers’ requirements. Then we outlined the main business components in the application landscape that are affected by IoT; these components include facility, product, consumer, and supply chain. We covered this from the perspec- tive of different industry verticals and gave a summary outline of possible application use cases, including the potentially disrupting ones. Then we introduced different technologies needed for IoT- based systems including different architectural components span- ning sensor devices to cloud systems connected in between via lo- cal sensor networks, gateway devices, and Internet. Following that, we introduced the need for standardization and interoperation in IoT space and discussed how different standardization activities are trying to address the standardization in terms of technical, syn- tactic, semantic, and organizational interoperability. Finally, we identified the real-world challenges in terms of scalability, security, privacy, affordability, context-awareness, and ease of development that must be addressed to create and deploy a full-scale IoT system in the real world. Diving deeper into these challenges, identifying their issues, and trying to provide feasible and reali�stic solution approaches to them become the objective for the rest of the book. References [1] http://www.webopedia.com/TERM/I/internet_of_things.html. [2] ������������������������������������������������������������������ http://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explana- tion-internet-things-that-anyone-can-understand/#14b285316828. [3] http://internetofthingsagenda.techtarget.com/definition/ Internet-of-Things-IoT. [4] http://www.gartner.com/newsroom/id/3165317. [5] http://sites.tcs.com/internet-of-things/. [6] http://www.tcs.com/research/Pages/TCS-Connected-Universe-Platform. aspx. [7] http://www.ioti.com/iot-trends-and-analysis/10-most-important-iot- trends-2016.
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- 46. 41 2 Scalability of Networks and Computing 2.1 Introduction As outlined in Chapter 1, the Internet of Things (IoT) is all about putting sensors on physical objects and human beings and con- necting them to monitor, diagnose, or predict physical states and events. Different studies predict that there will be 40 to 50 billion IoT devices connected to the Internet by 2020 [1]. However, are the network and computing infrastructure scalable enough to handle the deluge of data that will be churned out by these devices? We try to explore in depth this infrastructure scalability issue in this chapter. From a networking perspective, scalability is needed in every layer of the communication stack. Here scalability means provid- ing sufficient bandwidth, capacity, and low latency for transport- ing the sensor data to handle the IoT application requirements. We also need to keep in mind the coverage of the network to imple- ment cost-effective systems. Hence, choice of the right communi- cation technology, network topology, and network protocol has paramount importance in this respect.
- 47. 42 IoT Technical Challenges and Solutions However, value of IoT systems is mainly derived from mean- ingful analytics of the collected sensor data. The volume of data generated by sensors being enormous leads us to the computing scalability problem. Depending upon the application type, the re- quired analytics need to be computed in real time or in near real time or offline. The computing infrastructure needs to take cog- nizance of this varying requirement and implement the required system accordingly. In Section 2.2, we present an exhaustive set of possible use cases for applying IoT across various verticals like transportation, envi- ronment,energy,water,securitysurveillance,retail,manufacturing, agriculture, and healthcare. For each of the application use cases, the requirements for communication bandwidth, network capac- ity, latency, network coverage, timeliness of analytics, and compu- tational complexity are tabulated. The analysis of communication technologies, network architectures, and computing architectures in subsequent sections draws extensively from these diverse re- quirements from diverse use cases. In Section 2.3, we outline dif- ferent communication technologies for IoT in form of personal, wide area, or cellular networks and application layer protocols. In Section 2.4, we will cover different network architectures for IoT that can be built on top of these communication technologies to provide scalability. These include various network topologies, pro- tocol design, delay-tolerant networks, and software-defined net- works. Subsequently, in Section 2.5, we will discuss some practi- cal considerations for scalable IoT system deployment in form of real-time and power considerations for sensor data analytics, and utilizing the edge device for analytics, service-oriented platforms for IoT application development. 2.2 Use Cases and Requirements IoT can give rise to several interesting use cases, which hold the potential to add value to the end user. At a broad level, IoT use cases can be verticalized into various domains [2]. These use cases demand and pose a lot of requirements on the infrastructure of network, storage, and computing. One needs to understand these use cases first before understanding the infrastructural require- ments from these use cases. Hence, we start with a list of example
- 48. Scalability of Networks and Computing 43 IoT application use cases in different verticals that promise to cre- ate a disrupting value to the end-user experience or business. 2.2.1 Smart Transportation 2.2.1.1 Smart Parking Sensor-based parking slot monitoring followed by aggregate park- ing slot monitoring and demand-based parking price modification can go a long way to ease parking woes of busy city downtowns. 2.2.1.2 Traffic Congestion Management Sensor-based localization and tracking of vehicles can provide in- sights into temporal and spatial patterns of traffic, which, in turn, can be used for better traffic planning. In addition, such sensing systems can be used in real time for dynamic congestion control and signaling. 2.2.2 Smart Environment 2.2.2.1 Pollution Control Air quality monitoring and sound level monitoring can easily be done using IoT-based sensing systems. Analysis of such data can produce pollution heat maps, which can either tell people the areas to avoid or help authorities to take control action. 2.2.2.2 Waste Management Urban waste collection until now has been a scheduled regular process; however, production of waste is dynamic in nature and hence sensing of waste content in the dustbins and optimal sched- uling of waste collection vehicles accordingly can lead to a much more efficient system. 2.2.2.3 Forest Fire Detection Forest fires can cause havoc to the environment and can affect the nonurban, suburban, and urban populaces. Timely detection of forest fires when they are small can make them much easier and cheaper to control. Sensor-based systems with analytics for early detection of forest fires can be an impactful application in many locations.
- 49. 44 IoT Technical Challenges and Solutions 2.2.2.4 Natural Hazard Detection and Prediction In many areas, due to their geographical locations, landslide and avalanches are potential life hazards. Similarly, earthquakes and tsunamis are natural hazardous events. Strategic placement of wireless sensors coupled with analysis of the aggregate sensor data can provide early warning for landslide, avalanche, earth- quake, and tsunami, thereby enabling suitable timely evacuation procedures. 2.2.3 Smart Energy 2.2.3.1 Grid Monitoring and Control Continuous monitoring and control of electricity grid parameters can make the grid smart. A smart grid can be considered to be con- sisting smart control centers, smart transmission networks, and smart substations. Sensing critical grid parameters, transmitting them in real time to a central control station, and creating auto- matic actionable insights from the grid sensor data can be seen as the IoT-enabled version of the smart grid. 2.2.3.2 Peak Load Management With the proliferation of smart meters and smart gateways at home and buildings that can be regarded as the electricity sen- sor connected over network, it is now possible to have complex demand-response analysis create suitable policies for peak load management. 2.2.4 Smart Water 2.2.4.1 Water Quality Monitoring Water quality monitoring, both for fresh water and ground water, is an important aspect that is becoming more pertinent in the cur- rent context. Analysis of water content via biological and chemical sensors can provide impactful health benefits for all. 2.2.4.2 Leakage Detection The water distribution network of any city is one of the most com- plex ones and, being typically underground, is quite hard to main- tain. There is significant amount of waste of water that happens due to leakages in water distribution pipes. Such leakages also re-
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