000 31075nam a2200697 i 4500
001 8653941
003 IEEE
005 20220712211826.0
006 m o d
007 cr |n|||||||||
008 190417s2019 mau ob 001 eng d
010 _z 2018012509 (print)
020 _a9781119360124
_qelectronic
020 _a9781119360087
_qhardcover
020 _z9781119360087
_qprint
020 _z1119360080
_qhardcover
024 7 _a10.1002/9781119360124
_2doi
035 _a(CaBNVSL)mat08653941
035 _a(IDAMS)0b00006488baa829
040 _aCaBNVSL
_beng
_erda
_cCaBNVSL
_dCaBNVSL
050 4 _aTK3105
_b.T73 2019eb
082 0 0 _a388.3/12
_223
245 0 0 _aTransportation and power grid in smart cities :
_bcommunication networks and services /
_cedited by Hussein T. Mouftah and Melike Erol-Kantarci, Mubashir Husain Rehmani.
264 1 _aHoboken, New Jersey :
_bJohn Wiley & Son Ltd,
_c2019.
264 2 _a[Piscataqay, New Jersey] :
_bIEEE Xplore,
_c[2018]
300 _a1 PDF (xxxi, 651 pages).
336 _atext
_2rdacontent
337 _aelectronic
_2isbdmedia
338 _aonline resource
_2rdacarrier
504 _aIncludes bibliographical references and index.
505 0 _aList 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.
505 8 _a2.4.2 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.
505 8 _a3.6.1 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.
505 8 _a5.5 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.
505 8 _a8.2.1.2 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.
505 8 _a9.3.4 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.
505 8 _a11.1 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.
505 8 _a12.4.1 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.
505 8 _a14.2.3.3 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.
505 8 _a17.2.2 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.
505 8 _a18.2.1.1 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.
505 8 _a18.6 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.
505 8 _a21.1.1 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.
505 8 _a23.3.2 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.
506 _aRestricted to subscribers or individual electronic text purchasers.
520 _aPROVIDES POWERFUL INSIGHTS INTO THE COMMUNICATION NETWORKS AND SERVICES NEEDED TO MAKE SMART CITIES A REALITY 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. A comprehensive guide for researchers, industrial planners, development engineers, and urban planners, among others, Transportation and Power Grid in Smart Cities: Communication Networks and Services: . Uniquely covers both transport systems and electricity grids as they relate to communication networking. Discusses the technologies required for the integration of smart city power grids and intelligent transportation systems in a coherent and consistent manner. Addresses an array of smart city building blocks, including smart power grids, intelligent transportation systems, internet-of-things, electric vehicles, and wireless sensor networks. Bridges the divide between the fields of power systems, wireless communication, and city planning Its broad, yet in-depth coverage makes Transportation and Power Grid in Smart Cities: Communication Networks and Services required reading for researchers, development engineers, urban planners, and public policymakers, as well as undergraduate and graduate students of electrical power systems, transportation management, wireless communication, civil engineering, and sustainable urban planning.
530 _aAlso available in print.
538 _aMode of access: World Wide Web
588 _aDescription based on PDF viewed 04/17/2019.
650 0 _aSmart power grids
_xCommunication systems.
_97819
650 0 _aUrban transportation.
_93558
650 7 _aUrban transportation.
_2fast
_93558
655 0 _aElectronic books.
_93294
700 1 _aMouftah, Hussein T.,
_eeditor.
_914034
700 1 _aErol-Kantarci, Melike,
_eeditor.
_931381
700 1 _aRehmani, Mubashir Husain,
_d1983-
_eeditor.
_931382
710 2 _aIEEE Xplore (Online Service),
_edistributor.
_931383
710 2 _aWiley,
_epublisher.
_931384
856 4 2 _3Abstract with links to resource
_uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=8653941
942 _cEBK
999 _c75050
_d75050