Transportation and Power Grid in Smart Cities

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