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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

Autorzy
Wydawnictwo John Wiley and Sons Ltd
Data wydania 07/12/2018
Liczba stron 688
Forma publikacji książka w twardej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
Język angielski
ISBN 9781119360087
Kategorie Planowanie urbanistyczne i miejskie, Planowanie transportu i polityka transportowa, Inżynieria komunikacyjna i telekomunikacyjna, Systemy technologii transportu inteligentnego i zorganizowanego
610.00 PLN (z VAT)
$156.85 / €140.35 / £129.99 /
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Opis książki

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.

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

Spis treści

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, Herve 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 Trivino-Cabrera and Jose 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

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