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With the increasing worldwide trend in population migration into urban centers, we are beginning to see the emergence of the kinds of mega-cities which were once the stuff of science fiction. It is clear to most urban planners and developers that accommodating the needs of the tens of millions of inhabitants of those megalopolises in an orderly and uninterrupted manner will require the seamless integration of and real-time monitoring and response services for public utilities and transportation systems. Part speculative look into the future of the world's urban centers, part technical blueprint, this visionary book helps lay the groundwork for the communication networks and services on which tomorrow's "smart cities" will run.
HUSSEIN T. MOUFTAH, PHD, is Canada Research Chair and Distinguished University Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada.
MELIKE EROL-KANTARCI, PHD, is Assistant Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada.
MUBASHIR HUSAIN REHMANI, PHD, is Assistant Professor, Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Cantt, Pakistan.
List of Contributors xxi
Preface xxvii
SECTION I Communication Technologies for Smart Cities 1
1 Energy-Harvesting Cognitive Radios in Smart Cities 3Mustafa Ozger, Oktay Cetinkaya and Ozgur B. Akan
1.1 Introduction 3
1.1.1 Cognitive Radio 5
1.1.2 Cognitive Radio Sensor Networks 5
1.1.3 Energy Harvesting and Energy-Harvesting Sensor Networks 6
1.2 Motivations for Using Energy-Harvesting Cognitive Radios in Smart Cities 6
1.2.1 Motivations for Spectrum-Aware Communications 7
1.2.2 Motivations for Self-Sustaining Communications 7
1.3 Challenges Posed by Energy-Harvesting Cognitive Radios in Smart Cities 8
1.4 Energy-Harvesting Cognitive Internet of Things 9
1.4.1 Definition 9
1.4.2 Energy-Harvesting Methods in IoT 10
1.4.3 System Architecture 12
1.4.4 Integration of Energy-Harvesting Cognitive Radios with the Internet 13
1.5 A General Framework for EH-CRs in the Smart City 14
1.5.1 Operation Overview 14
1.5.2 Node Architecture 15
1.5.3 Network Architecture 16
1.5.4 Application Areas 17
1.6 Conclusion 18
References 18
2 LTE-D2D Communication for Power Distribution Grid: Resource Allocation for Time-Critical Applications 21Leonardo D. Oliveira, Taufik Abrao and Ekram Hossain
2.1 Introduction 21
2.2 Communication Technologies for Power Distribution Grid 22
2.2.1 An Overview of Smart Grid Architecture 22
2.2.2 Communication Technologies for SG Applications Outside Substations 24
2.2.3 Communication Networks for SG 26
2.3 Overview of Communication Protocols Used in Power Distribution Networks 27
2.3.1 Modbus 27
2.3.2 IEC 60870 29
2.3.3 DNP3 31
2.3.4 IEC 61850 32
2.3.5 SCADA Protocols for Smart Grid: Existing State-of-the-Art 35
2.4 Power Distribution System: Distributed Automation Applications and Requirements 36
2.4.1 Distributed Automation Applications 36
2.4.1.1 Voltage/Var Control (VVC) 37
2.4.1.2 Fault Detection, Isolation, and Restoration (FDCIR) 38
2.4.2 Requirements for Distributed Automation Applications 39
2.5 Analysis of Data Flow in Power Distribution Grid 40
2.5.1 Model for Power Distribution Grid 40
2.5.2 IEC 61850 Traffic Model 42
2.5.2.1 Cyclic Data Flow 42
2.5.2.2 Stochastic Data Flow 45
2.5.2.3 Burst Data Flow 46
2.6 LTE-D2D for DA: Resource Allocation for Time-Critical Applications 47
2.6.1 Overview of LTE 47
2.6.2 IEC 61850 Protocols over LTE 48
2.6.2.1 Mapping MMS over LTE 49
2.6.2.2 Mapping GOOSE over LTE 50
2.6.3 Resource Allocation in uplink LTE-D2D for DA Applications 50
2.6.3.1 Problem Formulation 51
2.6.3.2 Scheduler Design 54
2.6.3.3 Numerical Evaluation 55
2.7 Conclusion 60
References 61
3 5G and Cellular Networks in the Smart Grid 69Jimmy Jessen Nielsen, Ljupco Jorguseski, Haibin Zhang, Hervé Ganem, Ziming Zhu and Petar Popovski
3.1 Introduction 69
3.1.1 Massive MTC 70
3.1.2 Mission-Critical MTC 70
3.1.3 Secure Mission-Critical MTC 71
3.2 From Power Grid to Smart Grid 71
3.3 Smart Grid Communication Requirements 74
3.3.1 Traffic Models and Requirements 74
3.4 Unlicensed Spectrum and Non-3GPP Technologies for the Support of Smart Grid 76
3.4.1 IEEE 802.11ah 76
3.4.2 Sigfox's Ultra-Narrow Band (UNB) Approach 79
3.4.3 LoRaTM Chirp Spread Spectrum Approach 80
3.5 Cellular and 3GPP Technologies for the Support of Smart Grid 82
3.5.1 Limits of 3GPP Technologies up to Release 11 82
3.5.2 Recent Enhancements of 3GPP Technologies for IoT Applications (Releases 12-13) 83
3.5.2.1 LTE Cat-0 and Cat-M1 devices 84
3.5.2.2 Narrow-Band Internet of Things (NB-IoT) and Cat-NB1 Devices 85
3.5.3 Performance of Cellular LTE Systems for Smart Grids 86
3.5.4 LTE Access Reservation Protocol Limitations 87
3.5.4.1 LTE Access Procedure 87
3.5.4.2 Connection Establishment 90
3.5.4.3 Numerical Evaluation of LTE Random Access Bottlenecks 91
3.5.5 What Can We Expect from 5G? 93
3.6 End-to-End Security in Smart Grid Communications 94
3.6.1 Network Access Security 95
3.6.2 Transport Level Security 96
3.6.3 Application Level Security 96
3.6.4 End-to-End Security 96
3.6.5 Access Control 97
3.7 Conclusions and Summary 99
References 100
4 Machine-to-Machine Communications in the Smart City-a Smart Grid Perspective 103Ravil Bikmetov, M. Yasin Akhtar Raja and KhurramKazi
4.1 Introduction 103
4.2 Architecture and Characteristics of Smart Grids for Smart Cities 105
4.2.1 Definition of a Smart Grid and Its Conceptual Model 106
4.2.2 Standardization Approach in Smart Grids 112
4.2.3 Smart Grid Interoperability Reference Model (SGIRM) 113
4.2.4 Smart Grid Architecture Model 114
4.2.5 Energy Sources in the Smart Grid 115
4.2.6 Energy Consumers in a Smart Grid 117
4.2.7 Energy Service Providers in the Smart Grid 119
4.3 Intelligent Machine-to-Machine Communications in Smart Grids 120
4.3.1 Reference Architecture of Machine-to-Machine Interactions 120
4.3.2 Communication Media and Protocols 121
4.3.3 Layered Structure of Machine-to-Machine Communications 126
4.4 Optimization Algorithms for Energy Production, Distribution, and Consumption 132
4.5 Machine Learning Techniques in Efficient Energy Services and Management 134
4.6 Future Perspectives 135
4.7 Appendix 136
References 138
5 5G and D2D Communications at the Service of Smart Cities 147Muhammad Usman,Muhammad Rizwan Asghar and Fabrizio Granelli
5.1 Introduction 147
5.2 Literature Review 150
5.3 Smart City Scenarios 153
5.3.1 Public Health 154
5.3.2 Transportation and Environment 155
5.3.3 Energy Efficiency 157
5.3.4 Smart Grid 157
5.3.5 Water Management 158
5.3.6 Disaster Response and Emergency Services 159
5.3.7 Public Safety and Security 159
5.4 Discussion 160
5.4.1 Multiple Radio Access Technologies (Multi-RAT) 160
5.4.2 Virtualization 160
5.4.3 Distributed/Edge Computing 161
5.4.4 D2D Communication 161
5.4.5 Big Data 162
5.4.6 Security and Privacy 163
5.5 Conclusion 163
References 163
SECTION II Emerging Communication Networks for Smart Cities 171
6 Software Defined Networking and Virtualization for Smart Grid 173Hakki C. Cankaya
6.1 Introduction 173
6.2 Current Status of Power Grid and Smart Grid Modernization 174
6.2.1 Smart Grid 174
6.3 Network Softwarerization in Smart Grids 177
6.3.1 Software Defined Networking (SDN) as Next-Generation Software-Centric Approach to Telecommunications Networks 177
6.3.2 Adaptation of SDN for Smart Grid and City 179
6.3.3 Opportunities for SDN in Smart Grid 179
6.4 Virtualization for Networks and Functions 183
6.4.1 Network Virtualization 183
6.4.2 Network Function Virtualization 184
6.5 Use Cases of SDN/NFV in the Smart Grid 185
6.6 Challenges and Issues with SDN/NFV-Based Smart Grid 187
6.7 Conclusion 187
References 188
7 GHetNet: A Framework Validating Green Mobile Femtocells in Smart-Grids 191Fadi Al-Turjman
7.1 Introduction 191
7.2 RelatedWork 192
7.2.1 Static Validation Techniques 194
7.2.2 Dynamic Validation Techniques 195
7.3 System Models 197
7.3.1 Markov Model 199
7.3.2 Service-Rate Model 199
7.3.3 Communication Model 200
7.4 The Green HetNet (GHetNet) Framework 201
7.5 A Case Study: E-Mobility for Smart Grids 206
7.5.1 Performance metrics and parameters 207
7.5.2 Simulation Setups and Baselines 208
7.5.3 Results and Discussion 208
7.5.3.1 The Impact of Velocity on FBS Performance 209
7.5.3.2 The Impact of the Grid Load on Energy Consumption 211
7.6 Conclusion 213
References 213
8 Communication Architectures and Technologies for Advanced Smart Grid Services 217Francois Lemercier, Guillaume Habault, Georgios Z. Papadopoulos, Patrick Maille, NicolasMontavont and Periklis Chatzimisios
8.1 Introduction 217
8.2 The Smart Grid Communication Architecture and Infrastructure 219
8.2.1 DSO-Based Communications 220
8.2.1.1 The Existing AMI Organization 220
8.2.1.2 Communication Technologies used in the AMI 222
8.2.1.3 AMI Limitations 223
8.2.2 Internet-Based Architectures 224
8.2.2.1 IP-Based Architecture Limitations 225
8.2.3 Next-Generation Smart Grid Architecture 225
8.2.3.1 Technical Issues for Next-Generation Smart Grids 227
8.2.3.2 Handing Back the Keys to the User: Energy Management Should Be Separated from the Smart Meter 227
8.2.3.3 To Build an Open Market, Use an Open Network 228
8.2.3.4 Multi-Level Aggregation 228
8.2.3.5 Security Concerns 229
8.2.3.6 Ongoing Research Efforts 229
8.3 Routing Information in the Smart Grid 231
8.3.1 Routing Family of Protocols 231
8.3.1.1 Proactive Routing Protocol 232
8.3.1.2 Topology Management under RPL 232
8.3.1.3 Routing Table Maintenance under RPL 233
8.3.1.4 Routing Strategy: Metrics and Constraints 234
8.3.1.5 Path Computation under RPL 234
8.3.1.6 Summary of the RPL DODAG construction 235
8.3.1.7 Reactive Routing Protocol 236
8.3.1.8 Topology Management under AODV 237
8.3.2 Reactive Routing Protocol in a Constrained Network 238
8.3.2.1 Performance Evaluation 239
8.3.2.2 Summary on Routing Protocols 241
8.4 Conclusion 242
References 243
9 Wireless Sensor Networks in Smart Cities: Applications of Channel Bonding to Meet Data Communication Requirements 247Syed Hashim Raza Bukhari, Sajid Siraj andMubashir Husain Rehmani
9.1 Introduction, Basics, and Motivation 247
9.2 WSNs in Smart Cities 248
9.2.1 WSNs in Underground Transportation 249
9.2.2 WSNs in Smart Cab Services 249
9.2.3 WSNs in Waste Management Systems 249
9.2.4 WSNs in Atmosphere Health Monitoring 249
9.2.5 WSNs in Smart Grids 252
9.2.6 WSNs in Weather Forecasting 252
9.2.7 WSNs in Home Automation 252
9.2.8 WSNs in Structural Health Monitoring 252
9.3 Channel Bonding 253
9.3.1 Channel Bonding Schemes in Traditional Networks 253
9.3.2 Channel Bonding Schemes in Wireless Sensor Networks 254
9.3.3 Channel Bonding Schemes in Cognitive Radio Networks 255
9.3.4 Channel Bonding for Cognitive Radio Sensor Networks 257
9.4 Applications of Channel Bonding in CRSN-Based Smart Cities 258
9.4.1 CRSNs in Smart Health Care 258
9.4.2 CRSNs in M2M Communications 258
9.4.3 CRSNs Multiple Concurrent Deployments in Smart Cities 259
9.4.4 CRSNs in Smart Home Applications 259
9.4.5 CRSNs Smart Environment Control 259
9.4.6 CRSNs-Based IoT 259
9.5 Issues and Challenges Regarding the Implementation of Channel Bonding in Smart Cities 259
9.5.1 Privacy of Citizens 260
9.5.2 Energy Conservation 260
9.5.3 Data Storage and Aggregation 260
9.5.4 Geographic Awareness and Adaptation 260
9.5.5 Interference and Spectrum Issues 260
9.6 Conclusion 261
References 261
10 A Prediction Module for Smart City IoT Platforms 269Sema F. Oktug, Yusuf Yaslan and Halil Gulacar
10.1 Introduction 269
10.2 IoT Platforms for Smart Cities 271
10.2.1 ARM Mbed 271
10.2.2 Cumulocity 271
10.2.3 DeviceHive 273
10.2.4 Digi 273
10.2.5 Digital Service Cloud 274
10.2.6 FiWare 274
10.2.7 Global Sensor Networks (GSN) 274
10.2.8 IoTgo 274
10.2.9 Kaa 275
10.2.10 Nimbits 275
10.2.11 RealTime.io 275
10.2.12 SensorCloud 275
10.2.13 SiteWhere 276
10.2.14 TempoIQ 276
10.2.15 Thinger.io 276
10.2.16 Thingsquare 276
10.2.17 ThingWorx 277
10.2.18 VITAL 277
10.2.19 Xively 277
10.3 Prediction Module Developed 277
10.3.1 The VITAL IoT Platform 278
10.3.2 VITAL Prediction Module 278
10.4 AUse Case Employing the Traffic Sensors in Istanbul 281
10.4.1 Prediction Techniques Employed 282
10.4.1.1 Data Preprocessing 284
10.4.1.2 Feature Vectors 284
10.4.2 Results 285
10.4.2.1 Regression Results 286
10.5 Conclusion 288
Acknowledgment 288
References 289
SECTION III Renewable Energy Resources and Microgrid in Smart Cities 291
11 Integration of Renewable Energy Resources in the Smart Grid: Opportunities and Challenges 293Mohammad UpalMahfuz, Ahmed O. Nasif,MdMaruf Hossain andMd. Abdur Rahman
11.1 Introduction 293
11.2 The Smart Grid Paradigm 294
11.2.1 The Smart Grid Concept 294
11.2.2 System Components of the SG 296
11.3 Renewable Energy Integration in the Smart Grid 298
11.3.1 Resource Characteristics and Distributed Generation 298
11.3.2 Why Is Integration Necessary? 299
11.4 Opportunities and Challenges 299
11.4.1 Energy Storage (ES) 300
11.4.1.1 Key Energy Storage Technologies 300
11.4.1.2 Key Energy Storage Challenges in SG 301
11.4.2 Distributed Generation (DG) 302
11.4.2.1 Key DG Sources and Generators 303
11.4.2.2 Key Parts and Functions of a DG System and Its Distribution 303
11.4.2.3 DG and Dispatch Challenges 304
11.4.3 Resource Forecasting, Modeling, and Scheduling 305
11.4.3.1 Resource Modeling and Scheduling 305
11.4.3.2 Resource Forecasting (RF) 307
11.4.4 Demand Response 308
11.4.5 Demand-Side Management (DSM) 309
11.4.6 Monitoring 310
11.4.7 Transmission Techniques 311
11.4.8 System-Related Challenges 311
11.4.9 V2G Challenges 312
11.4.10 Security Challenges in the High Penetration of RE Resources 314
11.5 Case Studies 314
11.6 Conclusion 315
References 316
12 Environmental Monitoring for Smart Buildings 327Petros Spachos and Konstantinos Plataniotis
12.1 Introduction 327
12.2 Wireless Sensor Networks in Monitoring Applications 329
12.3 Application Requirements and Challenges 330
12.3.1 Monitoring Area 330
12.3.2 Application Scenario and Design Goal 332
12.3.3 Requirements 333
12.3.3.1 Sensor Type 333
12.3.3.2 Real-Time Data Aggregation 335
12.3.3.3 Scalability 335
12.3.3.4 Usability, Autonomy, and Reliability 336
12.3.3.5 Remote Management 336
12.3.4 Challenges 336
12.3.4.1 Power Management 336
12.3.4.2 Wireless Network Coexistence 337
12.3.4.3 Mesh Routing 337
12.3.4.4 Robustness 337
12.3.4.5 Dynamic Changes 337
12.3.4.6 Flexibility 337
12.3.4.7 Size and cost 337
12.4 Wireless Sensor Network Architecture 338
12.4.1 Framework 338
12.4.2 Hardware Infrastructure 339
12.4.3 Data Processing 341
12.4.3.1 Noise Reduction, Data Smoothing, and Calibration 341
12.4.3.2 Packet formation process 342
12.4.3.3 Information Processing and Storage 343
12.4.4 Indoor Monitoring System 343
12.5 Experiments and Results 343
12.5.1 Experimental Setup 343
12.5.2 Results Analysis 347
12.6 Conclusions 350
References 350
13 Cooperative EnergyManagement in Microgrids 355Ioannis Zenginis, John Vardakas, Prodromos-VasileiosMekikis and Christos Verikoukis
13.1 Introduction 355
13.2 The Cooperative Energy Management System Model 357
13.2.1 PV Panel Modeling 359
13.2.2 Energy Storage System 360
13.2.3 Inverter 361
13.2.4 Microgrid Energy Exchange 361
13.3 Evaluation and Discussion 362
13.4 Conclusion 366
Acknowledgment 367
References 368
14 Optimal Planning and Performance Assessment of Multi-Microgrid Systems in Future Smart Cities 371ShouxiangWang, LeiWu, Qi Liu and Shengxia Cai
14.1 Optimal Planning of Multi-Microgrid Systems 372
14.1.1 Introduction 372
14.1.2 Optimal Structure Planning 373
14.1.2.1 Definition of Indices 373
14.1.2.2 Structure Planning Method 375
14.1.3 Optimal Capacity Planning 377
14.1.3.1 Definition of Indexes 377
14.1.3.2 Capacity Planning Method 381
14.1.4 Conclusions 384
14.2 Performance Assessment of Multi-Microgrid System 384
14.2.1 Introduction 384
14.2.2 Comprehensive Evaluation Indexes 386
14.2.2.1 MMGS Source-Charge Capacity Index 386
14.2.2.2 MMGS Energy Interaction Index 388
14.2.2.3 MMGS Reliability Index 390
14.2.2.4 MMGS Economics Index 395
14.2.2.5 Energy Utilization Efficiency Index 398
14.2.2.6 Energy Saving and Emission Reduction Index 398
14.2.2.7 Renewable Energy Utilization Index 399
14.2.3 Performance Assessment 400
14.2.3.1 Performance Assessment of Grid-Connected MMGS 400
14.2.3.2 Performance Assessment of Islanded MMGS 401
14.2.3.3 Annual Performance Assessment of the MMGS 402
14.2.4 Case Studies 403
14.2.4.1 System Description 403
14.2.4.2 Numerical Results 403
14.3 Conclusions 406
Acknowledgment 407
References 407
SECTION IV Smart Cities, Intelligent Transportation Systemand Electric Vehicles 411
15 Wireless Charging for Electric Vehicles in the Smart Cities: Technology Review and Impact 413Alicia Triviño-Cabrera and José A. Aguado
15.1 Introduction 413
15.2 Review of theWireless Charging Methods 415
15.2.1 Technologies SupportingWireless Power Transfer for EVs 415
15.2.2 Operation Modes forWireless Power Transfer in EVs 416
15.3 Electrical Effect of Charging Technologies on the Grid 418
15.3.1 Harmonics Control in EVWireless Chargers 418
15.3.2 Power Factor Control in EVWireless Chargers 419
15.3.3 Implementation of Bidirectionality in EVWireless Chargers 420
15.3.4 Discussion 421
15.4 Scheduling Considering Charging Technologies 421
15.5 Conclusions and Future Guidelines 423
References 424
16 Channel Access Modelling for EV Charging/Discharging Service through Vehicular ad hoc Networks (VANETs) Communications 427Dhaou Said and Hussein T. Mouftah
16.1 Introduction 428
16.2 Technical Environment of the EV Charging/Discharging Process 428
16.2.1 EVSE Overview 429
16.2.2 Inductive Chargers: Opportunities and Potential 429
16.3 Overview of Communication Technologies in the Smart Grid 430
16.3.1 Power Line Communication 430
16.3.2 Wireless Communications for EV-Smart Grid Applications 431
16.4 Channel Access Model for EV Charging Service 432
16.4.1 Overview of VANET and LTE 432
16.4.2 Case Study: Access ChannelModel 433
16.4.3 Simulations Results 438
16.5 Conclusions 440
References 440
17 Intelligent Parking Management in Smart Citie s 443Sanket Gupte andMohamed Younis
17.1 Introduction 443
17.2 Design Issues and Taxonomy of Parking Solutions 445
17.2.1 Design Issues for Autonomous Parking Systems 445
17.2.2 Taxonomy of Parking Solutions 445
17.3 Classification of Existing Parking Systems 447
17.3.1 Sensing Infrastructure 447
17.3.2 Communication Infrastructure 457
17.3.3 Storage Infrastructure 460
17.3.4 Application Infrastructure 461
17.3.5 User Interfacing 463
17.3.6 Comparison of Existing Parking Systems 465
17.4 Participatory Sensing-Based Smart Parking 465
17.4.1 The Components 467
17.4.1.1 Users 467
17.4.1.2 IoT Devices 467
17.4.1.3 Server 468
17.4.1.4 Parking Spots 468
17.4.2 Parking Management Application 469
17.4.2.1 User Interface 469
17.4.2.2 Smart Reporting System 470
17.4.2.3 Leaderboard 470
17.4.2.4 Rewards Store 471
17.4.2.5 Enforcement and Compliance 472
17.4.2.6 External Integration 472
17.4.3 Data Processing and Cloud Support 472
17.4.3.1 Availability Computation 472
17.4.3.2 Reputation System 473
17.4.3.3 Scoring System 474
17.4.3.4 ReservationModel 474
17.4.3.5 Analysis and Learning 474
17.4.4 Implementation and Performance Evaluation 474
17.4.4.1 Prototype Application 474
17.4.4.2 Experiment Setup 475
17.4.4.3 Simulation Results 475
17.4.5 Features and Benefits 477
17.5 Conclusions and Future Advancements 479
References 480
18 Electric Vehicle Scheduling and Charging in Smart Cities 485Muhammmad Amjad, Mubashir Husain Rehmani and Tariq Umer
18.1 Introduction 485
18.1.1 Integration of EVs into Smart Cities 486
18.1.1.1 Enhancing the Existing Power Capacity 486
18.1.1.2 Designing the Communication Protocols to Support the Smart Recharging Structure 486
18.1.1.3 Development of a Well-designed Recharging Architecture 486
18.1.1.4 Considering the Expected Load on the Smart Grid 486
18.1.1.5 Need for Scheduling Approaches for EVs Recharging 486
18.1.2 Main Contributions 487
18.1.3 Organization of the Chapter 487
18.2 Smart Cities and Electric Vehicles: Motivation, Background, and ApplicationScenarios 488
18.2.1 Smart Cities: An Overview 488
18.2.1.1 Provision of Smart Transportation 488
18.2.1.2 Energy Management in Smart cities 488
18.2.1.3 Integration of the Economic and Business Model 488
18.2.1.4 Wireless Communication Needs/Communication Architectures for Smart Cities 489
18.2.1.5 Traffic Congestion Avoidance in Smart Cities 489
18.2.1.6 Support of Heterogeneous Technologies in Smart Cities 489
18.2.1.7 Green Applications Support in Smart Cities 489
18.2.1.8 Security and Privacy in Smart Cities 490
18.2.2 Motivation of Using EVs in Smart cities 490
18.2.3 Application Scenarios 490
18.2.3.1 Avoiding Spinning Reserves 490
18.2.3.2 V2G and G2V Capability 491
18.2.3.3 CO2 Minimization 491
18.2.3.4 Load Management on the Local Microgrid 491
18.3 EVs Recharging Approaches in Smart Cities 491
18.3.1 Centralized EVs Recharging Approach 491
18.3.1.1 Main Contributions and Limitations of Centralized EVs-Recharging Approach 492
18.3.2 Distributed EVs Recharging Approach 493
18.3.2.1 Main Contributions and Limitations of the Distributed EVs-recharging Approach 493
18.4 Scheduling EVs Recharging in Smart Cities 493
18.4.1 Objectives Achieved via Different Scheduling Approaches 494
18.4.1.1 Reduction of Power Losses 494
18.4.1.2 Minimizing Total Cost of Energy for Users 495
18.4.1.3 Maximizing Aggregator Profit 496
18.4.1.4 Frequency Regulation 497
18.4.1.5 Voltage regulation 497
18.4.1.6 Support for Renewable Energy Sources for Recharging of EVs 497
18.4.2 Resource Allocation for EVs Recharging in Smart Cities (Optimization Approaches) 498
18.5 Open Issues, Challenges, and Future Research Directions 498
18.5.1 Support ofWireless Power Charger 499
18.5.2 Vehicle-to-Anything 499
18.5.3 Energy Management for Smart Grid via EVs 499
18.5.4 Advance Communication Needs for Controlled EVs Recharging 499
18.5.5 EVs Control Applications 499
18.5.6 Standardization for Communication Technologies Used for EVs Recharging 500
18.6 Conclusion 500
References 500
SECTION V Security and Privacy Issues and Big Data in Smart Cities 507
19 Cyber-Security and Resiliency of Transportation and Power Systems in Smart Cities 509Seyedamirabbas Mousavian,Melike Erol-Kantarci and Hussein T. Mouftah
19.1 Introduction 509
19.2 EV Infrastructure and Smart Grid Integration 510
19.3 System Model 512
19.3.1 Model Definition and Assumptions 512
19.4 Estimating the Threat Levels in the EVSE Network 513
19.5 Response Model 514
19.6 Propagation Impacts on Power System Operations 515
19.6.1 Cyberattack Propagation in PMU Networks 515
19.6.2 Threat Level Estimation in PMU Networks 515
19.6.3 Response Model in PMU Networks 518
19.6.4 PMU Networks: Experimental Results 521
19.7 Conclusion and Open Issues 525
References 525
20 Protecting the Privacy of Electricity Consumers in the Smart City 529Binod Vaidya and Hussein T. Mouftah
20.1 Introduction 529
20.2 Privacy in the Smart Grid 530
20.2.1 Privacy Concerns over Customer Electricity Data Collected by the Utility 531
20.2.2 Privacy Concerns on Energy Usage Information Collected by a Non-Utility-OwnedMetering Device 532
20.2.3 Privacy Protection 532
20.3 Privacy Principles 532
20.4 Privacy Engineering 535
20.4.1 Privacy Protection Goals 535
20.4.2 Privacy Engineering Framework and Guidelines 538
20.5 Privacy Risk and Impact Assessment 540
20.5.1 System Privacy Risk Model 540
20.5.2 Privacy Impact Assessment (PIA) 541
20.6 Privacy Enhancing Technologies 542
20.6.1 Anonymization 544
20.6.2 Trusted Computation 545
20.6.3 Cryptographic Computation 545
20.6.4 Perturbation 546
20.6.5 Verifiable Computation 547
Acknowledgment 547
References 548
21 Privacy Preserving Power Charging Coordination Scheme in the Smart Grid 555Ahmed Sherif, Muhammad Ismail, Marbin Pazos-Revilla,Mohamed Mahmoud, Kemal Akkaya, Erchin Serpedin and Khalid Qaraqe
21.1 Introduction 555
21.1.1 Smart Grid Security Requirements 555
21.1.2 Charging Coordination Security Requirement 556
21.2 Charging Coordination and Privacy Preservation 558
21.3 Privacy-Preserving Charging Coordination Scheme 560
21.3.1 Network andThreat Models 560
21.3.2 The Proposed Scheme 561
21.3.2.1 Anonymous Data Submission 561
21.3.2.2 Charging Coordination 565
21.4 Performance Evaluation 567
21.4.1 Privacy/Security Analysis 567
21.4.2 Experimental Study 568
21.4.2.1 Setup 568
21.4.2.2 Metrics and Baselines 568
21.4.2.3 Simulation Results 569
21.5 Summary 572
Acknowledgment 573
References 573
22 Securing Smart Cities Systems and Services: A Risk-Based Analytics-Driven Approach 577Mahmoud Gad and Ibrahim Abualhaol
22.1 Introduction to Cybersecurity for Smart Cities 577
22.2 Smart Cities Enablers 579
22.3 Smart Cities Attack Surface 580
22.3.1 Attack Domains 580
22.3.1.1 Communications 580
22.3.1.2 Software 580
22.3.1.3 Hardware 580
22.3.1.4 Social Engineering 580
22.3.1.5 Supply Chain 581
22.3.1.6 Physical Security 581
22.3.2 Attack Me
Taufik Abrao
Department of Electrical Engineering
Londrina State University (DEEL-UEL)
Brazil
Ibrahim Abualhaol
Carleton University
Ottawa
Canada
José A. Aguado
University of Málaga
Spain
Ozgur B. Akan
Koc University
Turkey
Kemal Akkaya
Florida International University
Miami, FL
USA
Fadi Al-Turjman
Antalya Bilim University
Antalya
Muhammmad Amjad
COMSATS Institute of Information Technology
Wah Cantt
Pakistan
Muhammad Rizwan Asghar
Department of Computer Science
The University of Auckland
New Zealand
Ravil Bikmetov
University of North Carolina at Charlotte
NC
Shengxia Cai
Zhou Enlai School of Government
Nankai University
China
Hakki C. Cankaya
Fujitsu Network Communications
Richardson, TX
Oktay Cetinkaya
Next-generation and Wireless Communications Laboratory
Periklis Chatzimisios
Department of Informatics
Thessaloniki
Greece
Lei Chu
Research Center for Big Data Engineering and Technology
State Energy Smart Grid Research and Development Center, Shanghai, China
and
Shanghai Jiaotong University
Shanghai, China
Melike Erol-Kantarci
School of Electrical Engineering and Computer Science
University of Ottawa
Mahmoud Gad
Cognitive Labs Inc
Hervé Ganem
Gemalto
Paris, France
Fabrizio Granelli
Department of Information Engineering and Computer Science
University of Trento
Italy
Halil Gulacar
Department of Computer Engineering
Istanbul Technical University
Sanket Gupte
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County
Baltimore
Guillaume Habault
IMT Atlantique - IRISA
France
Syed Hashim Raza Bukhari
Attock
Xing He
Ekram Hossain
Department of Electrical and Computer Engineering
University of Manitoba
Md Maruf Hossain
Department of Natural and Applied Sciences (Engineering Technology Program)
University of Wisconsin-Green Bay
Muhammad Ismail
Texas A&M University at Qatar
Doha, Qatar
Ljupco Jorguseski
TNO
The Hague
Netherlands
Khurram Kazi
Draper Laboratory
Cambridge MA
Francois Lemercier
Itron and IMT Atlantique - IRISA
Zenan Ling
Haichun Liu
Qi Liu
Key Laboratory of Smart Grid of Ministry of Education
Tianjin University
Mohammad Upal Mahfuz
Wisconsin
Mohamed Mahmoud
Tennessee Technological University
TN, USA
Patrick Maille
Prodromos-Vasileios Mekikis
Department of Informatics and Telecommunication
University of Athens
Athens
Nicolas Montavont
Hussein T. Mouftah
School of Electrical Engineering and Computer Science (EECS)
Seyedamirabbas Mousavian
School of Business
Clarkson University
Potsdam
NY
Ahmed O. Nasif
Department of Engineering Technology
University of Wisconsin-Oshkosh
Jimmy Jessen Nielsen
Aalborg University
Denmark
Sema F. Oktug
Computer Engineering Department
Leonardo D. Oliveira
Department of Telecommunications and Control Engineering
University of Sao Paulo
Mustafa Ozger
Georgios Z. Papadopoulos
Marbin Pazos-Revilla
Konstantinos Plataniotis
University of Toronto
Petar Popovski
Khalid Qaraqe
Robert Qiu
Cookeville TN
Md. Abdur Rahman
Department of Electrical and Electronic Engineering
American International University-Bangladesh
Bangladesh
Mubashir Husain Rehmani
Dhaou Said
Erchin Serpedin
Texas A&M University
College Station
Ahmed Sherif
Tennessee Technological University Cookeville
Sajid Siraj
University of Leeds
United Kingdom
Petros Spachos
School of Engineering
University of Guelph
Alicia Triviño-Cabrera
Tariq Umer
Muhammad Usman
Binod Vaidya
John Vardakas
Iquadrat Informatica
Barcelona
Christos Verikoukis
Telecommunications Technological Centre of Catalonia...
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