Transportation and Power Grid in Smart Cities

Communication Networks and Services
 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 28. Dezember 2018
  • |
  • 688 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-36009-4 (ISBN)
 
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.
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HUSSEIN T. MOUFTAH, PHD, is Canada Research Chair and Distinguished University Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada.

MELIKE EROL-KANTARCI, PHD, is Assistant Professor, School of Electrical Engineering and Computer Science, University of Ottawa, Canada.

MUBASHIR HUSAIN REHMANI, PHD, is Assistant Professor, Department of Electrical Engineering, COMSATS Institute of Information Technology, Wah Cantt, Pakistan.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • List of Contributors
  • Preface
  • Section I Communication Technologies for Smart Cities
  • Chapter 1 Energy-Harvesting Cognitive Radios in Smart Cities
  • 1.1 Introduction
  • 1.1.1 Cognitive Radio
  • 1.1.2 Cognitive Radio Sensor Networks
  • 1.1.3 Energy Harvesting and Energy-Harvesting Sensor Networks
  • 1.2 Motivations for Using Energy-Harvesting Cognitive Radios in Smart Cities
  • 1.2.1 Motivations for Spectrum-Aware Communications
  • 1.2.2 Motivations for Self-Sustaining Communications
  • 1.3 Challenges Posed by Energy-Harvesting Cognitive Radios in Smart Cities
  • 1.4 Energy-Harvesting Cognitive Internet of Things
  • 1.4.1 Definition
  • 1.4.2 Energy-Harvesting Methods in IoT
  • 1.4.3 System Architecture
  • 1.4.4 Integration of Energy-Harvesting Cognitive Radios with the Internet
  • 1.5 A General Framework for EH-CRs in the Smart City
  • 1.5.1 Operation Overview
  • 1.5.2 Node Architecture
  • 1.5.3 Network Architecture
  • 1.5.4 Application Areas
  • 1.6 Conclusion
  • References
  • Chapter 2 LTE-D2D Communication for Power Distribution Grid: Resource Allocation for Time-Critical Applications
  • 2.1 Introduction
  • 2.2 Communication Technologies for Power Distribution Grid
  • 2.2.1 An Overview of Smart Grid Architecture
  • 2.2.2 Communication Technologies for SG Applications Outside Substations
  • 2.2.3 Communication Networks for SG
  • 2.3 Overview of Communication Protocols Used in Power Distribution Networks
  • 2.3.1 Modbus
  • 2.3.2 IEC 60870
  • 2.3.3 DNP3
  • 2.3.4 IEC 61850
  • 2.3.5 SCADA Protocols for Smart Grid: Existing State-of-the-Art
  • 2.4 Power Distribution System: Distributed Automation Applications and Requirements
  • 2.4.1 Distributed Automation Applications
  • 2.4.1.1 Voltage/Var Control (VVC)
  • 2.4.1.2 Fault Detection, Isolation, and Restoration (FDCIR)
  • 2.4.2 Requirements for Distributed Automation Applications
  • 2.5 Analysis of Data Flow in Power Distribution Grid
  • 2.5.1 Model for Power Distribution Grid
  • 2.5.2 IEC 61850 Traffic Model
  • 2.5.2.1 Cyclic Data Flow
  • 2.5.2.2 Stochastic Data Flow
  • 2.5.2.3 Burst Data Flow
  • 2.6 LTE-D2D for DA: Resource Allocation for Time-Critical Applications
  • 2.6.1 Overview of LTE
  • 2.6.2 IEC 61850 Protocols over LTE
  • 2.6.2.1 Mapping MMS over LTE
  • 2.6.2.2 Mapping GOOSE over LTE
  • 2.6.3 Resource Allocation in uplink LTE-D2D for DA Applications
  • 2.6.3.1 Problem Formulation
  • 2.6.3.2 Scheduler Design
  • 2.6.3.3 Numerical Evaluation
  • 2.7 Conclusion
  • References
  • Chapter 3 5G and Cellular Networks in the Smart Grid
  • 3.1 Introduction
  • 3.1.1 Massive MTC
  • 3.1.2 Mission-Critical MTC
  • 3.1.3 Secure Mission-Critical MTC
  • 3.2 From Power Grid to Smart Grid
  • 3.3 Smart Grid Communication Requirements
  • 3.3.1 Traffic Models and Requirements
  • 3.4 Unlicensed Spectrum and Non-3GPP Technologies for the Support of Smart Grid
  • 3.4.1 IEEE 802.11ah
  • 3.4.2 Sigfox's Ultra-Narrow Band (UNB) Approach
  • 3.4.3 LoRaT Chirp Spread Spectrum Approach
  • 3.5 Cellular and 3GPP Technologies for the Support of Smart Grid
  • 3.5.1 Limits of 3GPP Technologies up to Release 11
  • 3.5.2 Recent Enhancements of 3GPP Technologies for IoT Applications (Releases 12-13)
  • 3.5.2.1 LTE Cat-0 and Cat-M1 devices
  • 3.5.2.2 Narrow-Band Internet of Things (NB-IoT) and Cat-NB1 Devices
  • 3.5.3 Performance of Cellular LTE Systems for Smart Grids
  • 3.5.4 LTE Access Reservation Protocol Limitations
  • 3.5.4.1 LTE Access Procedure
  • 3.5.4.2 Connection Establishment
  • 3.5.4.3 Numerical Evaluation of LTE Random Access Bottlenecks
  • 3.5.5 What Can We Expect from 5G?
  • 3.6 End-to-End Security in Smart Grid Communications
  • 3.6.1 Network Access Security
  • 3.6.2 Transport Level Security
  • 3.6.3 Application Level Security
  • 3.6.4 End-to-End Security
  • 3.6.5 Access Control
  • 3.7 Conclusions and Summary
  • References
  • Chapter 4 Machine-to-Machine Communications in the Smart City-a Smart Grid Perspective
  • 4.1 Introduction
  • 4.2 Architecture and Characteristics of Smart Grids for Smart Cities
  • 4.2.1 Definition of a Smart Grid and Its Conceptual Model
  • 4.2.2 Standardization Approach in Smart Grids
  • 4.2.3 Smart Grid Interoperability Reference Model (SGIRM)
  • 4.2.4 Smart Grid Architecture Model
  • 4.2.5 Energy Sources in the Smart Grid
  • 4.2.6 Energy Consumers in a Smart Grid
  • 4.2.7 Energy Service Providers in the Smart Grid
  • 4.3 Intelligent Machine-to-Machine Communications in Smart Grids
  • 4.3.1 Reference Architecture of Machine-to-Machine Interactions
  • 4.3.2 Communication Media and Protocols
  • 4.3.3 Layered Structure of Machine-to-Machine Communications
  • 4.4 Optimization Algorithms for Energy Production, Distribution, and Consumption
  • 4.5 Machine Learning Techniques in Efficient Energy Services and Management
  • 4.6 Future Perspectives
  • 4.7 Appendix
  • References
  • Chapter 5 5G and D2D Communications at the Service of Smart Cities
  • 5.1 Introduction
  • 5.2 Literature Review
  • 5.3 Smart City Scenarios
  • 5.3.1 Public Health
  • 5.3.2 Transportation and Environment
  • 5.3.3 Energy Efficiency
  • 5.3.4 Smart Grid
  • 5.3.5 Water Management
  • 5.3.6 Disaster Response and Emergency Services
  • 5.3.7 Public Safety and Security
  • 5.4 Discussion
  • 5.4.1 Multiple Radio Access Technologies (Multi-RAT)
  • 5.4.2 Virtualization
  • 5.4.3 Distributed/Edge Computing
  • 5.4.4 D2D Communication
  • 5.4.5 Big Data
  • 5.4.6 Security and Privacy
  • 5.5 Conclusion
  • References
  • Section II Emerging Communication Networks for Smart Cities
  • Chapter 6 Software Defined Networking and Virtualization for Smart Grid
  • 6.1 Introduction
  • 6.2 Current Status of Power Grid and Smart Grid Modernization
  • 6.2.1 Smart Grid
  • 6.3 Network Softwarerization in Smart Grids
  • 6.3.1 Software Defined Networking (SDN) as Next-Generation Software-Centric Approach to Telecommunications Networks
  • 6.3.2 Adaptation of SDN for Smart Grid and City
  • 6.3.3 Opportunities for SDN in Smart Grid
  • 6.4 Virtualization for Networks and Functions
  • 6.4.1 Network Virtualization
  • 6.4.2 Network Function Virtualization
  • 6.5 Use Cases of SDN/NFV in the Smart Grid
  • 6.6 Challenges and Issues with SDN/NFV-Based Smart Grid
  • 6.7 Conclusion
  • References
  • Chapter 7 GHetNet: A Framework Validating Green Mobile Femtocells in Smart-Grids
  • 7.1 Introduction
  • 7.2 Related Work
  • 7.2.1 Static Validation Techniques
  • 7.2.2 Dynamic Validation Techniques
  • 7.3 System Models
  • 7.3.1 Markov Model
  • 7.3.2 Service-Rate Model
  • 7.3.3 Communication Model
  • 7.4 The Green HetNet (GHetNet) Framework
  • 7.5 A Case Study: E-Mobility for Smart Grids
  • 7.5.1 Performance metrics and parameters
  • 7.5.2 Simulation Setups and Baselines
  • 7.5.3 Results and Discussion
  • 7.5.3.1 The Impact of Velocity on FBS Performance
  • 7.5.3.2 The Impact of the Grid Load on Energy Consumption
  • 7.6 Conclusion
  • References
  • Chapter 8 Communication Architectures and Technologies for Advanced Smart Grid Services
  • 8.1 Introduction
  • 8.2 The Smart Grid Communication Architecture and Infrastructure
  • 8.2.1 DSO-Based Communications
  • 8.2.1.1 The Existing AMI Organization
  • 8.2.1.2 Communication Technologies used in the AMI
  • 8.2.1.3 AMI Limitations
  • 8.2.2 Internet-Based Architectures
  • 8.2.2.1 IP-Based Architecture Limitations
  • 8.2.3 Next-Generation Smart Grid Architecture
  • 8.2.3.1 Technical Issues for Next-Generation Smart Grids
  • 8.2.3.2 Handing Back the Keys to the User: Energy Management Should Be Separated from the Smart Meter
  • 8.2.3.3 To Build an Open Market, Use an Open Network
  • 8.2.3.4 Multi-Level Aggregation
  • 8.2.3.5 Security Concerns
  • 8.2.3.6 Ongoing Research Efforts
  • 8.3 Routing Information in the Smart Grid
  • 8.3.1 Routing Family of Protocols
  • 8.3.1.1 Proactive Routing Protocol
  • 8.3.1.2 Topology Management under RPL
  • 8.3.1.3 Routing Table Maintenance under RPL
  • 8.3.1.4 Routing Strategy: Metrics and Constraints
  • 8.3.1.5 Path Computation under RPL
  • 8.3.1.6 Summary of the RPL DODAG construction
  • 8.3.1.7 Reactive Routing Protocol
  • 8.3.1.8 Topology Management under AODV
  • 8.3.2 Reactive Routing Protocol in a Constrained Network
  • 8.3.2.1 Performance Evaluation
  • 8.3.2.2 Summary on Routing Protocols
  • 8.4 Conclusion
  • References
  • Chapter 9 Wireless Sensor Networks in Smart Cities: Applications of Channel Bonding to Meet Data Communication Requirements
  • 9.1 Introduction, Basics, and Motivation
  • 9.2 WSNs in Smart Cities
  • 9.2.1 WSNs in Underground Transportation
  • 9.2.2 WSNs in Smart Cab Services
  • 9.2.3 WSNs in Waste Management Systems
  • 9.2.4 WSNs in Atmosphere Health Monitoring
  • 9.2.5 WSNs in Smart Grids
  • 9.2.6 WSNs in Weather Forecasting
  • 9.2.7 WSNs in Home Automation
  • 9.2.8 WSNs in Structural Health Monitoring
  • 9.3 Channel Bonding
  • 9.3.1 Channel Bonding Schemes in Traditional Networks
  • 9.3.2 Channel Bonding Schemes in Wireless Sensor Networks
  • 9.3.3 Channel Bonding Schemes in Cognitive Radio Networks
  • 9.3.4 Channel Bonding for Cognitive Radio Sensor Networks
  • 9.4 Applications of Channel Bonding in CRSN-Based Smart Cities
  • 9.4.1 CRSNs in Smart Health Care
  • 9.4.2 CRSNs in M2M Communications
  • 9.4.3 CRSNs Multiple Concurrent Deployments in Smart Cities
  • 9.4.4 CRSNs in Smart Home Applications
  • 9.4.5 CRSNs Smart Environment Control
  • 9.4.6 CRSNs-Based IoT
  • 9.5 Issues and Challenges Regarding the Implementation of Channel Bonding in Smart Cities
  • 9.5.1 Privacy of Citizens
  • 9.5.2 Energy Conservation
  • 9.5.3 Data Storage and Aggregation
  • 9.5.4 Geographic Awareness and Adaptation
  • 9.5.5 Interference and Spectrum Issues
  • 9.6 Conclusion
  • References
  • Chapter 10 A Prediction Module for Smart City IoT Platforms
  • 10.1 Introduction
  • 10.2 IoT Platforms for Smart Cities
  • 10.2.1 ARM Mbed
  • 10.2.2 Cumulocity
  • 10.2.3 DeviceHive
  • 10.2.4 Digi
  • 10.2.5 Digital Service Cloud
  • 10.2.6 FiWare
  • 10.2.7 Global Sensor Networks (GSN)
  • 10.2.8 IoTgo
  • 10.2.9 Kaa
  • 10.2.10 Nimbits
  • 10.2.11 RealTime.io
  • 10.2.12 SensorCloud
  • 10.2.13 SiteWhere
  • 10.2.14 TempoIQ
  • 10.2.15 Thinger.io
  • 10.2.16 Thingsquare
  • 10.2.17 ThingWorx
  • 10.2.18 VITAL
  • 10.2.19 Xively
  • 10.3 Prediction Module Developed
  • 10.3.1 The VITAL IoT Platform
  • 10.3.2 VITAL Prediction Module
  • 10.4 A Use Case Employing the Traffic Sensors in Istanbul
  • 10.4.1 Prediction Techniques Employed
  • 10.4.1.1 Data Preprocessing
  • 10.4.1.2 Feature Vectors
  • 10.4.2 Results
  • 10.4.2.1 Regression Results
  • 10.5 Conclusion
  • Acknowledgment
  • References
  • Section III Renewable Energy Resources and Microgrid in Smart Cities
  • Chapter 11 Integration of Renewable Energy Resources in the Smart Grid: Opportunities and Challenges
  • 11.1 Introduction
  • 11.2 The Smart Grid Paradigm
  • 11.2.1 The Smart Grid Concept
  • 11.2.2 System Components of the SG
  • 11.3 Renewable Energy Integration in the Smart Grid
  • 11.3.1 Resource Characteristics and Distributed Generation
  • 11.3.2 Why Is Integration Necessary?
  • 11.4 Opportunities and Challenges
  • 11.4.1 Energy Storage (ES)
  • 11.4.1.1 Key Energy Storage Technologies
  • 11.4.1.2 Key Energy Storage Challenges in SG
  • 11.4.2 Distributed Generation (DG)
  • 11.4.2.1 Key DG Sources and Generators
  • 11.4.2.2 Key Parts and Functions of a DG System and Its Distribution
  • 11.4.2.3 DG and Dispatch Challenges
  • 11.4.3 Resource Forecasting, Modeling, and Scheduling
  • 11.4.3.1 Resource Modeling and Scheduling
  • 11.4.3.2 Resource Forecasting (RF)
  • 11.4.4 Demand Response
  • 11.4.5 Demand-Side Management (DSM)
  • 11.4.6 Monitoring
  • 11.4.7 Transmission Techniques
  • 11.4.8 System-Related Challenges
  • 11.4.9 V2G Challenges
  • 11.4.10 Security Challenges in the High Penetration of RE Resources
  • 11.5 Case Studies
  • 11.6 Conclusion
  • References
  • Chapter 12 Environmental Monitoring for Smart Buildings
  • 12.1 Introduction
  • 12.2 Wireless Sensor Networks in Monitoring Applications
  • 12.3 Application Requirements and Challenges
  • 12.3.1 Monitoring Area
  • 12.3.2 Application Scenario and Design Goal
  • 12.3.3 Requirements
  • 12.3.3.1 Sensor Type
  • 12.3.3.2 Real-Time Data Aggregation
  • 12.3.3.3 Scalability
  • 12.3.3.4 Usability, Autonomy, and Reliability
  • 12.3.3.5 Remote Management
  • 12.3.4 Challenges
  • 12.3.4.1 Power Management
  • 12.3.4.2 Wireless Network Coexistence
  • 12.3.4.3 Mesh Routing
  • 12.3.4.4 Robustness
  • 12.3.4.5 Dynamic Changes
  • 12.3.4.6 Flexibility
  • 12.3.4.7 Size and cost
  • 12.4 Wireless Sensor Network Architecture
  • 12.4.1 Framework
  • 12.4.2 Hardware Infrastructure
  • 12.4.3 Data Processing
  • 12.4.3.1 Noise Reduction, Data Smoothing, and Calibration
  • 12.4.3.2 Packet formation process
  • 12.4.3.3 Information Processing and Storage
  • 12.4.4 Indoor Monitoring System
  • 12.5 Experiments and Results
  • 12.5.1 Experimental Setup
  • 12.5.2 Results Analysis
  • 12.6 Conclusions
  • References
  • Chapter 13 Cooperative Energy Management in Microgrids
  • 13.1 Introduction
  • 13.2 The Cooperative Energy Management System Model
  • 13.2.1 PV Panel Modeling
  • 13.2.2 Energy Storage System
  • 13.2.3 Inverter
  • 13.2.4 Microgrid Energy Exchange
  • 13.3 Evaluation and Discussion
  • 13.4 Conclusion
  • References
  • Chapter 14 Optimal Planning and Performance Assessment of Multi-Microgrid Systems in Future Smart Cities
  • 14.1 Optimal Planning of Multi-Microgrid Systems
  • 14.1.1 Introduction
  • 14.1.2 Optimal Structure Planning
  • 14.1.2.1 Definition of Indices
  • 14.1.2.2 Structure Planning Method
  • 14.1.3 Optimal Capacity Planning
  • 14.1.3.1 Definition of Indexes
  • 14.1.3.2 Capacity Planning Method
  • 14.1.4 Conclusions
  • 14.2 Performance Assessment of Multi-Microgrid System
  • 14.2.1 Introduction
  • 14.2.2 Comprehensive Evaluation Indexes
  • 14.2.2.1 MMGS Source-Charge Capacity Index
  • 14.2.2.2 MMGS Energy Interaction Index
  • 14.2.2.3 MMGS Reliability Index
  • 14.2.2.4 MMGS Economics Index
  • 14.2.2.5 Energy Utilization Efficiency Index
  • 14.2.2.6 Energy Saving and Emission Reduction Index
  • 14.2.2.7 Renewable Energy Utilization Index
  • 14.2.3 Performance Assessment
  • 14.2.3.1 Performance Assessment of Grid-Connected MMGS
  • 14.2.3.2 Performance Assessment of Islanded MMGS
  • 14.2.3.3 Annual Performance Assessment of the MMGS
  • 14.2.4 Case Studies
  • 14.2.4.1 System Description
  • 14.2.4.2 Numerical Results
  • 14.3 Conclusions
  • Acknowledgment
  • References
  • Section IV Smart Cities, Intelligent Transportation System and Electric Vehicles
  • Chapter 15 Wireless Charging for Electric Vehicles in the Smart Cities: Technology Review and Impact
  • 15.1 Introduction
  • 15.2 Review of the Wireless Charging Methods
  • 15.2.1 Technologies Supporting Wireless Power Transfer for EVs
  • 15.2.2 Operation Modes for Wireless Power Transfer in EVs
  • 15.3 Electrical Effect of Charging Technologies on the Grid
  • 15.3.1 Harmonics Control in EV Wireless Chargers
  • 15.3.2 Power Factor Control in EV Wireless Chargers
  • 15.3.3 Implementation of Bidirectionality in EV Wireless Chargers
  • 15.3.4 Discussion
  • 15.4 Scheduling Considering Charging Technologies
  • 15.5 Conclusions and Future Guidelines
  • References
  • Chapter 16 Channel Access Modelling for EV Charging/Discharging Service through Vehicular ad hoc Networks (VANETs) Communications
  • 16.1 Introduction
  • 16.2 Technical Environment of the EV Charging/Discharging Process
  • 16.2.1 EVSE Overview
  • 16.2.2 Inductive Chargers: Opportunities and Potential
  • 16.3 Overview of Communication Technologies in the Smart Grid
  • 16.3.1 Power Line Communication
  • 16.3.2 Wireless Communications for EV-Smart Grid Applications
  • 16.4 Channel Access Model for EV Charging Service
  • 16.4.1 Overview of VANET and LTE
  • 16.4.2 Case Study: Access Channel Model
  • 16.4.3 Simulations Results
  • 16.5 Conclusions
  • References
  • Chapter 17 Intelligent Parking Management in Smart Cities
  • 17.1 Introduction
  • 17.2 Design Issues and Taxonomy of Parking Solutions
  • 17.2.1 Design Issues for Autonomous Parking Systems
  • 17.2.2 Taxonomy of Parking Solutions
  • 17.3 Classification of Existing Parking Systems
  • 17.3.1 Sensing Infrastructure
  • 17.3.2 Communication Infrastructure
  • 17.3.3 Storage Infrastructure
  • 17.3.4 Application Infrastructure
  • 17.3.5 User Interfacing
  • 17.3.6 Comparison of Existing Parking Systems
  • 17.4 Participatory Sensing-Based Smart Parking
  • 17.4.1 The Components
  • 17.4.1.1 Users
  • 17.4.1.2 IoT Devices
  • 17.4.1.3 Server
  • 17.4.1.4 Parking Spots
  • 17.4.2 Parking Management Application
  • 17.4.2.1 User Interface
  • 17.4.2.2 Smart Reporting System
  • 17.4.2.3 Leaderboard
  • 17.4.2.4 Rewards Store
  • 17.4.2.5 Enforcement and Compliance
  • 17.4.2.6 External Integration
  • 17.4.3 Data Processing and Cloud Support
  • 17.4.3.1 Availability Computation
  • 17.4.3.2 Reputation System
  • 17.4.3.3 Scoring System
  • 17.4.3.4 Reservation Model
  • 17.4.3.5 Analysis and Learning
  • 17.4.4 Implementation and Performance Evaluation
  • 17.4.4.1 Prototype Application
  • 17.4.4.2 Experiment Setup
  • 17.4.4.3 Simulation Results
  • 17.4.5 Features and Benefits
  • 17.5 Conclusions and Future Advancements
  • References
  • Chapter 18 Electric Vehicle Scheduling and Charging in Smart Cities
  • 18.1 Introduction
  • 18.1.1 Integration of EVs into Smart Cities
  • 18.1.1.1 Enhancing the Existing Power Capacity
  • 18.1.1.2 Designing the Communication Protocols to Support the Smart Recharging Structure
  • 18.1.1.3 Development of a Well-designed Recharging Architecture
  • 18.1.1.4 Considering the Expected Load on the Smart Grid
  • 18.1.1.5 Need for Scheduling Approaches for EVs Recharging
  • 18.1.2 Main Contributions
  • 18.1.3 Organization of the Chapter
  • 18.2 Smart Cities and Electric Vehicles: Motivation, Background, and Application Scenarios
  • 18.2.1 Smart Cities: An Overview
  • 18.2.1.1 Provision of Smart Transportation
  • 18.2.1.2 Energy Management in Smart cities
  • 18.2.1.3 Integration of the Economic and Business Model
  • 18.2.1.4 Wireless Communication Needs/Communication Architectures for Smart Cities
  • 18.2.1.5 Traffic Congestion Avoidance in Smart Cities
  • 18.2.1.6 Support of Heterogeneous Technologies in Smart Cities
  • 18.2.1.7 Green Applications Support in Smart Cities
  • 18.2.1.8 Security and Privacy in Smart Cities
  • 18.2.2 Motivation of Using EVs in Smart cities
  • 18.2.3 Application Scenarios
  • 18.2.3.1 Avoiding Spinning Reserves
  • 18.2.3.2 V2G and G2V Capability
  • 18.2.3.3 CO2 Minimization
  • 18.2.3.4 Load Management on the Local Microgrid
  • 18.3 EVs Recharging Approaches in Smart Cities
  • 18.3.1 Centralized EVs Recharging Approach
  • 18.3.1.1 Main Contributions and Limitations of Centralized EVs-Recharging Approach
  • 18.3.2 Distributed EVs Recharging Approach
  • 18.3.2.1 Main Contributions and Limitations of the Distributed EVs-recharging Approach
  • 18.4 Scheduling EVs Recharging in Smart Cities
  • 18.4.1 Objectives Achieved via Different Scheduling Approaches
  • 18.4.1.1 Reduction of Power Losses
  • 18.4.1.2 Minimizing Total Cost of Energy for Users
  • 18.4.1.3 Maximizing Aggregator Profit
  • 18.4.1.4 Frequency Regulation
  • 18.4.1.5 Voltage regulation
  • 18.4.1.6 Support for Renewable Energy Sources for Recharging of EVs
  • 18.4.2 Resource Allocation for EVs Recharging in Smart Cities (Optimization Approaches)
  • 18.5 Open Issues, Challenges, and Future Research Directions
  • 18.5.1 Support of Wireless Power Charger
  • 18.5.2 Vehicle-to-Anything
  • 18.5.3 Energy Management for Smart Grid via EVs
  • 18.5.4 Advance Communication Needs for Controlled EVs Recharging
  • 18.5.5 EVs Control Applications
  • 18.5.6 Standardization for Communication Technologies Used for EVs Recharging
  • 18.6 Conclusion
  • References
  • Section V Security and Privacy Issues and Big Data in Smart Cities
  • Chapter 19 Cyber-Security and Resiliency of Transportation and Power Systems in Smart Cities
  • 19.1 Introduction
  • 19.2 EV Infrastructure and Smart Grid Integration
  • 19.3 System Model
  • 19.3.1 Model Definition and Assumptions
  • 19.4 Estimating the Threat Levels in the EVSE Network
  • 19.5 Response Model
  • 19.6 Propagation Impacts on Power System Operations
  • 19.6.1 Cyberattack Propagation in PMU Networks
  • 19.6.2 Threat Level Estimation in PMU Networks
  • 19.6.3 Response Model in PMU Networks
  • 19.6.4 PMU Networks: Experimental Results
  • 19.7 Conclusion and Open Issues
  • References
  • Chapter 20 Protecting the Privacy of Electricity Consumers in the Smart City
  • 20.1 Introduction
  • 20.2 Privacy in the Smart Grid
  • 20.2.1 Privacy Concerns over Customer Electricity Data Collected by the Utility
  • 20.2.2 Privacy Concerns on Energy Usage Information Collected by a Non-Utility-Owned Metering Device
  • 20.2.3 Privacy Protection
  • 20.3 Privacy Principles
  • 20.4 Privacy Engineering
  • 20.4.1 Privacy Protection Goals
  • 20.4.2 Privacy Engineering Framework and Guidelines
  • 20.5 Privacy Risk and Impact Assessment
  • 20.5.1 System Privacy Risk Model
  • 20.5.2 Privacy Impact Assessment (PIA)
  • 20.6 Privacy Enhancing Technologies
  • 20.6.1 Anonymization
  • 20.6.2 Trusted Computation
  • 20.6.3 Cryptographic Computation
  • 20.6.4 Perturbation
  • 20.6.5 Verifiable Computation
  • Acknowledgment
  • References
  • Chapter 21 Privacy Preserving Power Charging Coordination Scheme in the Smart Grid
  • 21.1 Introduction
  • 21.1.1 Smart Grid Security Requirements
  • 21.1.2 Charging Coordination Security Requirement
  • 21.2 Charging Coordination and Privacy Preservation
  • 21.3 Privacy-Preserving Charging Coordination Scheme
  • 21.3.1 Network and Threat Models
  • 21.3.2 The Proposed Scheme
  • 21.3.2.1 Anonymous Data Submission
  • 21.3.2.2 Charging Coordination
  • 21.4 Performance Evaluation
  • 21.4.1 Privacy/Security Analysis
  • 21.4.2 Experimental Study
  • 21.4.2.1 Setup
  • 21.4.2.2 Metrics and Baselines
  • 21.4.2.3 Simulation Results
  • 21.5 Summary
  • Acknowledgment
  • References
  • Chapter 22 Securing Smart Cities Systems and Services: A Risk-Based Analytics-Driven Approach
  • 22.1 Introduction to Cybersecurity for Smart Cities
  • 22.2 Smart Cities Enablers
  • 22.3 Smart Cities Attack Surface
  • 22.3.1 Attack Domains
  • 22.3.1.1 Communications
  • 22.3.1.2 Software
  • 22.3.1.3 Hardware
  • 22.3.1.4 Social Engineering
  • 22.3.1.5 Supply Chain
  • 22.3.1.6 Physical Security
  • 22.3.2 Attack Mechanisms
  • 22.4 Securing Smart Cities: A Design Science Approach
  • 22.5 NIST Cybersecurity Framework
  • 22.6 Cybersecurity Fusion Center with Big Data Analytics
  • 22.7 Conclusion
  • 22.8 Table of Abbreviations
  • References
  • Chapter 23 Spatiotemporal Big Data Analysis for Smart Grids Based on Random Matrix Theory
  • 23.1 Introduction
  • 23.1.1 Perspective on Smart Grids
  • 23.1.2 The Role of Data in the Future Power Grid
  • 23.1.3 A Brief Account for RMT
  • 23.2 RMT: A Practical and Powerful Big Data Analysis Tool
  • 23.2.1 Modeling Grid Data using Large Dimensional Random Matrices
  • 23.2.2 Asymptotic Spectrum Laws
  • 23.2.3 Transforms
  • 23.2.4 Convergence Rate
  • 23.2.5 Free Probability
  • 23.3 Applications to Smart Grids
  • 23.3.1 Hypothesis Tests in Smart Grids
  • 23.3.2 Data-Driven Methods for State Evaluation
  • 23.3.3 Situation Awareness based on Linear Eigenvalue Statistics
  • 23.3.4 Early Event Detection Using Free Probability
  • 23.4 Conclusion and Future Directions
  • References
  • Index
  • EULA

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