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E-grāmata: From Internet of Things to Smart Cities: Enabling Technologies

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From Internet of Things to Smart Cities: Enabling Technologies explores the information and communication technologies (ICT) needed to enable real-time responses to current environmental, technological, societal, and economic challenges. ICT technologies can be utilized to help with reducing carbon emissions, improving resource utilization efficiency, promoting active engagement of citizens, and more.

This book aims to introduce the latest ICT technologies and to promote international collaborations across the scientific community, and eventually, the general public. It consists of three tightly coupled parts. The first part explores the involvement of enabling technologies from basic machine-to-machine communications to Internet of Things technologies. The second part of the book focuses on state of the art data analytics and security techniques, and the last part of the book discusses the design of human-machine interfaces, including smart home and cities.

Features











Provides an extended literature review of relevant technologies, in addition to detailed comparison diagrams, making new readers be easier to grasp fundamental and wide knowledge





Contains the most recent research results in the field of communications, signal processing and computing sciences for facilitating smart homes, buildings, and cities





Includes future research directions in Internet of Things, smart homes, smart buildings, smart grid, and smart cities





Presents real examples of applying these enabling technologies to smart homes, transportation systems and cities

With contributions from leading experts, the book follows an easy structure that not only presents timely research topics in-depth, but also integrates them into real world applications to help readers to better understand them.
Preface xix
Section I From Machine-to-Machine Communications to Internet of Things
Chapter 1 From Machine-to-Machine Communications to Internet of Things: Enabling Communication Technologies
3(32)
Hamidreza Shariatmadari
Sassan Iraji
Riku Jantti
1.1 Introduction
4(1)
1.2 IoT Applications And Their Requirements
4(2)
1.3 IoT Connectivity Landscape
6(15)
1.3.1 IEEE 802.15.4
7(4)
1.3.2 WiFi
11(1)
1.3.3 Bluetooth
12(2)
1.3.4 RFID and Ambient Backscattering
14(1)
1.3.5 Dedicated Short Range Communications
15(1)
1.3.6 Low Power Wide Area Network
16(2)
1.3.7 Cellular Systems
18(3)
1.4 Challenges And Solutions For Connectivity In 5G Era
21(7)
1.4.1 Low-power Consumption
21(1)
1.4.2 Enhanced Coverage
22(1)
1.4.3 Ultra-reliable Low-latency Communications
23(3)
1.4.4 Massive Number of Devices
26(1)
1.4.5 Handling Small Bursts of Data
27(1)
1.5 Conclusions
28(2)
Bibliography
30(5)
Chapter 2 Power Control for Reliable M2M Communication
35(26)
Ling Wang
Hongwei Zhang
2.1 Introduction
36(3)
2.1.1 History of Power Control in Cellular Networks
36(2)
2.1.2 Objectives
38(1)
2.1.3 Organization
39(1)
2.2 M2M Communication Systems
39(8)
2.2.1 Co-channel Interference and Network Architecture
39(1)
2.2.2 SINR Model and Link Reliability
40(2)
2.2.3 Channel Dynamics and Statistical Models
42(3)
2.2.4 Multiscale and Instantaneous Characteristics
45(2)
2.3 Power Control Theory
47(3)
2.3.1 Feasible and optimal power control
48(1)
2.3.2 Infeasibility of power control
49(1)
2.4 Power Control Approaches For Constant And Fading Channels
50(2)
2.4.1 Conflict Graph-based Power Control for Constant Channels
50(1)
2.4.2 Geometric Programming-based Power Control for Fading Channels
51(1)
2.5 Discussion On Adaptive Power Control For M2M Communication Systems
52(2)
2.6 Extensive Studies On Power Control
54(3)
2.7 Open Challenges And Emerging Trends
57(1)
Bibliography
58(3)
Chapter 3 Enabling Geo-centric Communication Technologies in Opportunistic Networks
61(28)
Yue Cao
De Mi
Tong Wang
Lei Zhang
3.1 Introduction
62(1)
3.2 Background
63(3)
3.2.1 Opportunistic Networks (ONs)
63(1)
3.2.2 Applications of ONs in Smart Cities
63(1)
3.2.2.1 Vehicular Ad Hoc NETworks (VANETs)
63(1)
3.2.2.2 Airborne Networks (ANs)
65(1)
3.2.2.3 Mobile Social Networks (MSNs)
65(1)
3.2.2.4 UnderWater Sensor Networks (UWSNs)
65(1)
3.3 Motivation And Challenges For Geographic Routing In ONs
66(4)
3.3.1 Geo-centric Technologies in Smart Cities
66(1)
3.3.2 Introduction on Geographic Routing
66(1)
3.3.3 Motivation for Geographic Routing in ONs
66(1)
3.3.4 Challenges for Geographic Routing in ONs
67(3)
3.4 Taxonomy And Review Of Geographic Routing In ONs
70(9)
3.4.1 Destination Unawareness Class
71(1)
3.4.2 Destination Awareness Class
72(1)
3.4.2.1 Stationary Destination
72(1)
3.4.2.2 Considering Mobile Destination via Real-time Geographic Information
76(1)
3.4.2.3 Considering Mobile Destination via Historical Geographic Information
76(2)
3.4.3 Hybrid Class
78(1)
3.5 Comparison And Analysis
79(2)
3.6 Future Directions
81(2)
3.7 Conclusion
83(1)
Bibliography
83(6)
Chapter 4 Routing Protocol for Low Power and Lossy IoT Networks
89(30)
Xiyuan Liu
Zhengguo Sheng
Changchuan Yin
4.1 Introduction
90(1)
4.2 RPL: An Overview And Its Key Mechanisms
91(4)
4.2.1 Routing Mechanism of RPL
91(1)
4.2.2 Message Control Mechanism of RPL
92(2)
4.2.3 RPL and Its Counterparts
94(1)
4.3 RPL Topology Generation Methods
95(3)
4.3.1 Objective Functions and Metrics
95(2)
4.3.2 Multi-parents Consideration
97(1)
4.4 RPL Applications
98(2)
4.4.1 RPL Application Overview
98(1)
4.4.2 Application Scenarios
99(1)
4.5 Security Issues In RPL
100(3)
4.6 RPL Performance Evaluation In Large-Scale Networks
103(8)
4.6.1 Simulation Platforms
103(1)
4.6.2 Framework Integration for OMNeT++
103(2)
4.6.3 Configuration Details
105(2)
4.6.4 Simulation of Cross-layer RPL Routing
107(4)
4.7 Challenges And Prospect
111(2)
4.8 Conclusion
113(1)
Bibliography
113(6)
Chapter 5 Resource Allocation for Wireless Communication Networks with RF Energy Harvesting
119(32)
Elena Boshkovska
Derrick Wing Kwan Ng
Robert Schober
5.1 Introduction
120(2)
5.2 Receiver Structure
122(1)
5.3 Swipt Communication Networks
123(10)
5.3.1 Channel Model
123(1)
5.3.2 Non-linear Energy Harvesting Model
124(3)
5.3.3 Channel State Information
127(1)
5.3.4 Achievable System Data Rate
128(1)
5.3.5 Problem Formulation and Solution
128(4)
5.3.6 Numerical Example
132(1)
5.4 Wireless Powered Communication Networks
133(7)
5.4.1 Channel Model
134(2)
5.4.2 Problem Formulation and Solution
136(2)
5.4.3 Numerical Example
138(2)
5.5 Conclusion
140(1)
5.6 Appendix
141(2)
5.6.1 Proof of Theorem 1
141(2)
Bibliography
143(8)
Section II Data Era: Data Analytics and Security
Chapter 6 Distributed Machine Learning in Big Data Era for Smart City
151(28)
Yuan Zuo
Yulei Wu
Geyong Min
Chengqiang Huang
Xing Zhang
6.1 Introduction
152(3)
6.2 The Stochastic Gradient Descent (SGD) In Parallelization
155(5)
6.2.1 Parallelized SGD Based on MapReduce
157(1)
6.2.2 Online SGD in Round-robin
157(1)
6.2.3 Hogwild! for "Lock-free"
158(1)
6.2.4 AsySVRG for asynchronous SGD Variant
159(1)
6.2.5 ASGD with Single-sided Communication
160(1)
6.3 The Newton Method In Parallelization
160(4)
6.3.1 A truncated Newton Method: The Trust Region Newton Method (TRON)
162(1)
6.3.2 The Distributed TRON Based on Spark and MPI
163(1)
6.3.3 General Distributed Implementation of TRON
163(1)
6.3.4 Matrix-vector Product Improvement for Inner Mechanism
164(1)
6.4 The Petuum Framework
164(2)
6.5 The Convex Optimization Decomposition Method
166(4)
6.5.1 An Implementation Example of ADMM
168(1)
6.5.2 Other Work Relevant to Decomposition
169(1)
6.6 Some Other Research Relevant To Distributed Application
170(1)
6.6.1 Evaluation of Parallel Logistic Regression
170(1)
6.6.2 Conjugate Gradient Optimization
171(1)
6.7 A Case Study
171(1)
6.8 Conclusions
172(1)
Bibliography
172(7)
Chapter 7 Security in Smart Grids
179(48)
Julia Sanchez
Agustin Zaballos
Ramon Martin De Pozuelo
Guiomar Corral
Alan Briones
7.1 Introduction To Cybersecurity
181(8)
7.1.1 Key Security Aspects for Any System
181(2)
7.1.2 Network Vulnerability Assessment
183(1)
7.1.2.1 Security Vulnerabilities
183(1)
7.1.2.2 Security Policies and Standards
184(1)
7.1.2.3 Security Methodologies and Procedures
186(1)
7.1.2.4 Security Assessments
187(1)
7.1.2.5 Network Security Testing Tools
187(1)
7.1.2.6 Summary
188(1)
7.2 Automated Security Assessment
189(5)
7.2.1 Global Architecture of an Automated Security Assessment
189(1)
7.2.1.1 Base System Module
190(1)
7.2.1.2 Management Module
190(1)
7.2.1.3 Analysis Module
191(1)
7.2.1.4 Testing Modules
192(1)
7.2.2 The Communications Protocol
192(1)
7.2.3 Other Considerations
193(1)
7.3 Security Concerns, Trends And Requirements In Smart Grids
194(6)
7.3.1 Requirements of the Smart Grid
196(1)
7.3.2 Smart Grid Security Requirements Definition
197(3)
7.4 Security In A Cloud Infrastructure And Services For Smart Grids
200(10)
7.4.1 Security Concerns and Requirements in a Cloud Environment
200(1)
7.4.1.1 Security Threats
202(1)
7.4.1.2 Security Issues
202(1)
7.4.1.3 Security Requirements
204(1)
7.4.2 Use Case Analysis-FINESCE Cloud for Smart Grid Distribution
205(1)
7.4.2.1 Use Case Description
205(1)
7.4.2.2 Security Requirements Analysis
207(1)
7.4.2.3 Security Audit
208(1)
7.4.3 Summary
209(1)
7.5 The Smart Grid As An IoT
210(2)
7.6 Towards A Secure And Sustainable Smart Grid Management
212(6)
7.6.1 A Sustainable Smart Grid Management
212(1)
7.6.2 Software-Defined Network
213(1)
7.6.3 Service Composition Paradigm
213(1)
7.6.4 The Proof of Concept: A First Approach to Orchestrate Secured Smart Metering
214(4)
7.7 Conclusions
218(1)
7.8 Acknowledgements
219(1)
Bibliography
219(8)
Chapter 8 Secret Key Generation under Active Attacks
227(30)
Wenwen Tu
Lifeng Lai
8.1 Introduction
228(3)
8.2 Basic Models For Key Generation With A Passive Adversary
231(4)
8.2.1 Key Generation with Side Information at the Adversary
231(2)
8.2.2 Key Generation with a Helper
233(1)
8.2.3 Basic Model for Key Generation in Wireless Setting
234(1)
8.3 Key Generation With Public Discussion Attacked
235(4)
8.3.1 Model Modification
235(1)
8.3.2 All or Nothing Result
236(2)
8.3.3 Efficiently Checking the Simulatability Condition
238(1)
8.4 Key Generation With Contaminated Sources
239(8)
8.4.1 Two-Way Relay Channel Model
239(2)
8.4.2 Efficiency of the Key Generation Algorithm
241(2)
8.4.3 Attack Strategy and Power Allocation
243(1)
8.4.3.1 Optimal Attack Strategy
243(1)
8.4.3.2 Optimal Attack Power Allocation
245(2)
8.5 Key Generation With A Byzantine Helper
247(4)
8.5.1 System Model with a Byzantine Helper
247(1)
8.5.2 Key Generation Scheme against the Byzantine Helper
248(1)
8.5.2.1 A Key Generation Scheme Example
249(2)
8.6 Conclusion
251(1)
8.7 Acknowledgement
251(1)
Bibliography
251(6)
Section III Towards Smart World from Interfaces to Homes to Cities
Chapter 9 Applying Human-Computer Interaction Practices to IoT Prototyping
257(38)
Salim Haniff
Markku Turunen
Roope Raisamo
9.1 Introduction
258(7)
9.1.1 Internet of Things
258(1)
9.1.2 Human-Computer Interaction
259(6)
9.2 HCI Methodology
265(2)
9.3 Use Cases
267(23)
9.3.1 Smart Energy Monitoring
267(1)
9.3.1.1 User's Requirements
267(1)
9.3.1.2 Hardware Implementation
268(1)
9.3.1.3 Software Implementation
270(1)
9.3.1.4 Discussion
273(3)
9.3.2 Smart Lighting
276(1)
9.3.2.1 User's Requirements
277(1)
9.3.2.2 Hardware Implementation
277(1)
9.3.2.3 Software Implementation
279(1)
9.3.2.4 Discussion
283(1)
9.3.3 Seamless Home Automation
284(1)
9.3.3.1 User's Requirements
285(1)
9.3.3.2 Hardware Implementation
286(1)
9.3.3.3 Software Implementation
287(1)
9.3.3.4 Discussion
289(1)
9.4 Conclusion
290(1)
Bibliography
291(4)
Chapter 10 Inclusive Product Interfaces for the Future: Automotive, Aerospace, IoT and Inclusion Design
295(28)
Patrick M. Langdon
10.1 The Background To The Problems
296(4)
10.1.1 The Ubiquity of IoT Technology, and Importance of Inclusive Design
297(1)
10.1.2 What Is the Need for Inclusion?
298(1)
10.1.3 The Inclusive Design Response
299(1)
10.1.4 Health Induced and Situationally Induced Impairment
299(1)
10.2 Advanced Interaction Interfaces
300(2)
10.2.1 The State of the Art
301(1)
10.2.2 Solutions and Issues with User Modelling
301(1)
10.3 The Inclusive Adaption Approach
302(1)
10.4 Case Study 1: Future Automotive
303(4)
10.4.1 Key Future HMI Design Elements
304(3)
10.4.2 Visualisation of Key Concepts
307(1)
10.5 Case Study 2: Future Aerospace
307(3)
10.5.1 Need for Multimodal Solutions
308(2)
10.5.2 Multimodal Interface Experiments
310(1)
10.6 Case Study 3: Predictive Pointing In Automotive Touch Screens
310(2)
10.7 Case Study 4: Adaptive Mobile Applications
312(3)
10.7.1 The IU-ATC Project
314(1)
10.7.2 Mobile Interfaces
314(1)
10.8 Discussion
315(2)
Bibliography
317(6)
Chapter 11 Low Power Wide Area (LPWA) Networks for IoT Applications
323(34)
Kan Zheng
Zhe Yang
Xiong Xiong
Wei Xiang
11.1 Overview On 5G IoT
324(2)
11.2 Overview On Low Power Wide Area Networks (LPWANS)
326(13)
11.2.1 Application Scenarios of LPWANs
326(2)
11.2.2 Classification of LPWANs
328(1)
11.2.2.1 LPWAN Based on NB-IoT
329(1)
11.2.2.2 LPWAN Based on IEEE 802.15.4k
335(4)
11.3 Implementation Of LPWAN Based On IEEE 802.15.4K
339(4)
11.3.1 Access Point (AP)
339(1)
11.3.2 Devices
340(2)
11.3.3 Experimental Results
342(1)
11.4 LPWA-Based Air Quality Monitoring System
343(8)
11.4.1 System Architecture
345(1)
11.4.1.1 Sensing Layer
345(1)
11.4.1.2 Network Layer
347(1)
11.4.1.3 Application Layer
347(1)
11.4.2 Experimental Results and Analysis
348(1)
11.4.2.1 Experimental Configurations
348(1)
11.4.2.2 Results and Analysis
349(2)
11.5 Conclusion And Outlook
351(2)
Bibliography
353(4)
Chapter 12 A Data-centered Fog Platform for Smart Living
357(22)
Jianhua Li
Jiong Jin
Dong Yuan
Marimuthu Palaniswami
Klaus Moessner
12.1 Introduction
358(8)
12.1.1 Smart City
358(2)
12.1.2 Internet of Things
360(2)
12.1.3 Smart Living
362(1)
12.1.4 Ad Hoc IoT
362(1)
12.1.5 Gateway or Proxy Based IoT
363(1)
12.1.6 Cloud Computing Based IoT
364(1)
12.1.7 Fog Computing
364(2)
12.2 EHOPES Elements And Dataflow
366(3)
12.2.1 EHOPES and Dataflow
366(2)
12.2.2 Summary
368(1)
12.3 Fog Platform For EHOPES
369(2)
12.3.1 State of the Art
369(1)
12.3.2 Fog Edge Node (FEN)
369(1)
12.3.3 Fog Server (FS)
370(1)
12.3.4 Foglet (Middleware)
371(1)
12.4 Case Study And Evaluation
371(4)
12.4.1 The Scenario
371(2)
12.4.2 The Simulation
373(1)
12.4.3 Simulation Results
374(1)
12.5 Conclusion
375(1)
Bibliography
375(4)
Chapter 13 Resources and Practical Factors in Smart Home and City
379(20)
Bo Tan
Lili Tao
Ni Zhu
13.1 Introduction
380(1)
13.2 Novel Usage Of Radio Resources
380(6)
13.2.1 Current Situation and Challenges
380(1)
13.2.2 Use of Outdoor Radio Signals
381(2)
13.2.3 Use of Indoor Signals
383(2)
13.2.4 The Trend
385(1)
13.3 Video Resources
386(4)
13.3.1 Introduction
386(1)
13.3.2 Applications and Current Systems
387(2)
13.3.3 Future Trends
389(1)
13.4 Practical Considerations
390(3)
13.4.1 Pervasive Sensing
390(1)
13.4.2 Smart Cities in Reality
391(2)
Bibliography
393(6)
Index 399
Hongjian Sun (S07-M11-SM15) received his Ph.D. degree from the University of Edinburgh (U.K.) in 2011 and then took postdoctoral positions at Kings College London (U.K.) and Princeton University (USA). Since 2013, he has been with the University of Durham, U.K., as a Lecturer (now Assistant Professor) in Smart Grid. His research mainly focuses on: (i) Smart grid: communications and networking, (ii) Smart grid: demand side management and demand response, and (iii) Smart grid: renewable energy sources integration. He is on the Editorial Board of the Journal of Communications and Networks, and EURASIP Journal on Wireless Communications and Networking. He also served as Guest Editor for IEEE Communication Magazine for 2 Feature Topics. To date, he has published over 80 papers in refereed journals and international conferences; He has made contributions to and coauthored the IEEE 1900.6a-2014 Standard; He has published five book chapters, and edited IET book "Smarter Energy: from Smart Metering to the Smart Grid" (ISBN: 978-1-78561-104-9).









Chao Wang received the B.E. degree from the University of Science and Technology of China, China, in 2003 and the M.Sc. and Ph.D. degrees from the University of Edinburgh, U.K., in 2005 and 2009, respectively. In 2008, he was a Visiting Student Research Collaborator with Princeton University, USA. From 2009 to 2012, he was a Postdoctoral Research Associate with KTH-Royal Institute of Technology, Sweden. He is currently an Associate Professor with the School of Electronics and Information Engineering, Tongji University, China. His research interests mainly lie in information/communication theory and signal processing for wireless networks, as well as their applications to next-generation mobile communication systems. So far Dr. Wang has authored/co-authored 50+ scientific papers. In 2014, he was awarded as the Shanghai Pujiang Talent by the Shanghai government. He was invited to serve as the symposium TPC chair at CHINACOM15, workshop Panel Co-Chair at IEEE VTC16 Spring, Publicity Co-Chair at IEEE CYBCONF17, and TPC member at 23 international conferences.
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