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Transportation and Power Grid in Smart Cities: Communication Networks and Services

Transportation and Power Grid in Smart Cities: Communication Networks and Services

Hussein T. Mouftah , Melike Erol-Kantarci , Mubashir Husain Rehmani

ISBN: 978-1-119-36008-7

Nov 2018

688 pages

Select type: Hardcover

In Stock

$150.00

Description

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.

Written by a uniquely well-qualified author team, this book provides detailed insights into the technical requirements for the wireless sensor and actuator networks required to make smart cities a reality.

List of Contributors xxi

Preface xxvii

SECTION I Communication Technologies for Smart Cities 1

1 Energy-Harvesting Cognitive Radios in Smart Cities 3
Mustafa 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 21
Leonardo 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 69
Jimmy 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 103
Ravil 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 147
Muhammad 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 173
Hakki 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 191
Fadi 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 217
Francois 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 247
Syed 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 269
Sema 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 293
Mohammad 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 327
Petros 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 355
Ioannis 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 371
ShouxiangWang, 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 413
Alicia 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 427
Dhaou 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 443
Sanket 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 485
Muhammmad 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 509
Seyedamirabbas 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 529
Binod 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 555
Ahmed 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 577
Mahmoud 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 Mechanisms 582

22.4 Securing Smart Cities: A Design Science Approach 582

22.5 NIST Cybersecurity Framework 583

22.6 Cybersecurity Fusion Center with Big Data Analytics 585

22.7 Conclusion 587

22.8 Table of Abbreviations 587

References 588

23 Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory 591
Robert Qiu, Lei Chu, Xing He, Zenan Ling and Haichun Liu

23.1 Introduction 591

23.1.1 Perspective on Smart Grids 591

23.1.2 The Role of Data in the Future Power Grid 594

23.1.3 A Brief Account for RMT 595

23.2 RMT: A Practical and Powerful Big Data Analysis Tool 596

23.2.1 Modeling Grid Data using Large Dimensional Random Matrices 596

23.2.2 Asymptotic Spectrum Laws 598

23.2.3 Transforms 600

23.2.4 Convergence Rate 601

23.2.5 Free Probability 603

23.3 Applications to Smart Grids 608

23.3.1 Hypothesis Tests in Smart Grids 609

23.3.2 Data-DrivenMethods for State Evaluation 609

23.3.3 Situation Awareness based on Linear Eigenvalue Statistics 612

23.3.4 Early Event Detection Using Free Probability 621

23.4 Conclusion and Future Directions 626

References 629

Index 635