Transportation and Power Grid in Smart Cities E-Book

Hussein T. Mouftah

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 1294

Veröffentlichungsjahr: 2018

Beschreibung

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.

Leseprobe

Table of Contents

Cover

List of Contributors

Preface

Section I: Communication Technologies for Smart Cities

Chapter 1: Energy‐Harvesting Cognitive Radios in Smart Cities

1.1 Introduction

1.2 Motivations for Using Energy‐Harvesting Cognitive Radios in Smart Cities

1.3 Challenges Posed by Energy‐Harvesting Cognitive Radios in Smart Cities

1.4 Energy‐Harvesting Cognitive Internet of Things

1.5 A General Framework for EH‐CRs in the Smart City

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

2.3 Overview of Communication Protocols Used in Power Distribution Networks

2.4 Power Distribution System: Distributed Automation Applications and Requirements

2.5 Analysis of Data Flow in Power Distribution Grid

2.6 LTE‐D2D for DA: Resource Allocation for Time‐Critical Applications

2.7 Conclusion

References

Chapter 3: 5G and Cellular Networks in the Smart Grid

3.1 Introduction

3.2 From Power Grid to Smart Grid

3.3 Smart Grid Communication Requirements

3.4 Unlicensed Spectrum and Non‐3GPP Technologies for the Support of Smart Grid

3.5 Cellular and 3GPP Technologies for the Support of Smart Grid

3.6 End‐to‐End Security in Smart Grid Communications

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

4.3 Intelligent Machine‐to‐Machine Communications in Smart Grids

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

4.9 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

5.4 Discussion

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

6.3 Network Softwarerization in Smart Grids

6.4 Virtualization for Networks and Functions

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

7.3. System Models

7.4 The Green HetNet (GHetNet) Framework

7.5 A Case Study: E‐Mobility for Smart Grids

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

8.3 Routing Information in the Smart Grid

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

9.3 Channel Bonding

9.4 Applications of Channel Bonding in CRSN‐Based Smart Cities

9.5 Issues and Challenges Regarding the Implementation of Channel Bonding in Smart Cities

9.6 Conclusion

References

Chapter 10: A Prediction Module for Smart City IoT Platforms

10.1 Introduction

10.2 IoT Platforms for Smart Cities

10.3 Prediction Module Developed

10.4 A Use Case Employing the Traffic Sensors in Istanbul

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

11.3 Renewable Energy Integration in the Smart Grid

11.4 Opportunities and Challenges

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

12.4 Wireless Sensor Network Architecture

12.5 Experiments and Results

12.6 Conclusions

References

Chapter 13: Cooperative Energy Management in Microgrids

13.1 Introduction

13.2 The Cooperative Energy Management System Model

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

14.2 Performance Assessment of Multi‐Microgrid System

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

15.3 Electrical Effect of Charging Technologies on the Grid

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

16.3 Overview of Communication Technologies in the Smart Grid

16.4 Channel Access Model for EV Charging Service

16.5 Conclusions

References

Chapter 17: Intelligent Parking Management in Smart Cities

17.1 Introduction

17.2 Design Issues and Taxonomy of Parking Solutions

17.3 Classification of Existing Parking Systems

17.4 Participatory Sensing–Based Smart Parking

17.5. Conclusions and Future Advancements

References

Chapter 18: Electric Vehicle Scheduling and Charging in Smart Cities

18.1 Introduction

18.2 Smart Cities and Electric Vehicles: Motivation, Background, and Application Scenarios

18.3 EVs Recharging Approaches in Smart Cities

18.4 Scheduling EVs Recharging in Smart Cities

18.5 Open Issues, Challenges, and Future Research Directions

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

19.4 Estimating the Threat Levels in the EVSE Network

19.5 Response Model

19.6 Propagation Impacts on Power System Operations

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

20.3 Privacy Principles

20.4 Privacy Engineering

20.5 Privacy Risk and Impact Assessment

20.6 Privacy Enhancing Technologies

Acknowledgment

References

Chapter 21: Privacy Preserving Power Charging Coordination Scheme in the Smart Grid

21.1 Introduction

21.2 Charging Coordination and Privacy Preservation

21.3 Privacy‐Preserving Charging Coordination Scheme

21.4 Performance Evaluation

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

22.4 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

23.2 RMT: A Practical and Powerful Big Data Analysis Tool

23.3 Applications to Smart Grids

23.4 Conclusion and Future Directions

References

Index

End User License Agreement

List of Tables

Chapter 01

Table 1.1 Comparison of Energy‐Harvesting Techniques.

Chapter 02

Table 2.1 Comparison among wired and wireless candidate technologies for SG applications (Adapted from (Kuzlu et al., 2014)).

Table 2.2 Transfer time classes according to IEC 61850 standard (Adapted from (IEC, 2015)).

Table 2.3 Requirements for different DA applications.

Table 2.4 Message types and performance requirements in a substation network (Adapted from (IEC, 2015; Das et al., 2012)).

Table 2.5 Simplified time delivery requirements for communication inside and outside substations (SS) (Adapted from (IEEE, 2005)).

Table 2.6 Standardized QCI characteristics for LTE (Adapted from (3GPP, 2016)).

Table 2.7 LTE uplink SC‐FDMA physical layer parameters (Sesia et al., 2011).

Table 2.8 Main system parameters adopted in the simulations.

Table 2.9 SINR range to CQI mapping, based on (Jar and Fettweis, 2012).

Table 2.10 Size of data generation, in [bytes], for different users.

Chapter 03

Table 3.1 Derived Smart Meter Traffic Model.

Table 3.2 Derived Traffic Model of Distribution Grid WAMS Node from SUNSEED Field Trial.

Table 3.3 Comparison of IEEE 802.11ah, LoRa™, and Sigfox with 3GPP IoT Developments.

Chapter 04

Table 4.1 Domains and Actors in the NIST Smart Grid Conceptual Model (NIST, 2012).

Table 4.2 Communications Networks Defined for the SG in the IEEE 2030 CT‐IAP (IEEE, 2011; IEEE Smart Grid, 2015).

Table 4.3 Typical Applications of EMS within the Users Domain.

Table 4.4 Protocol Stack in M2M/IoT Communication System (Elmangoush, 2016).

Table 4.5 Telecommunication Media and Protocols for SGs.

Table 4.6 M2M Wireless Technologies and Standards Implemented in a SG (López et al., 2014; Elmangoush, 2016).

Table 4.7 Standards identified by NIST “Framework and Roadmap for Smart Grid Interoperability” (NIST, 2014).

Chapter 05

Table 5.1 An Overview of New Features of LTE‐A Networks in 3GPP Releases 12 to 14 to Enable Different Smart City Applications.

Table 5.2 Supported ProSe Functions in 3GPP Release 12 to Enable D2D Communication in Public Safety and Non‐Public Safety Applications.

Table 5.3 The proposed 5G Architectures in the Literature Can Be Divided into Broad Categories: Architectures Based on Information‐Centric Networks (ICN) and Architectures Based on User‐Centric Networks.

Chapter 07

Table 7.1 A Summary of the Aforementioned Related Works in the Literature.

Table 7.2 Summary of Symbols.

Table 7.3 Specifications of a Femtocell Base Station parameters [[43]].

Table 7.4 Components of the considered use‐case scenario [[43]].

Table 7.5 Parameters of the Simulated Networks.

Chapter 08

Table 8.1 Communication technologies for the AMI last‐hop.

Table 8.2 LOADng / RPL comparison.

Chapter 09

Table 9.1 Applications of WSNs in smart cities.

Chapter 10

Table 10.1 Comparisons of IoT‐Based Smart City Platforms.

Table 10.2 VITAL Prediction Module Access Method.

Table 10.3 RMSE for Different Methods without Neighbor Speed Information.

Table 10.4 RMSE for Different Methods with Observations of the Previous Sensor.

Table 10.5 RMSE for Different Methods with Observations of the Previous Two Sensors.

Table 10.6 RMSE for Different Methods with Observations of the Previous Two Sensor.

Chapter 11

Table 11.1 Overview of Storage Systems and Their Parameters (Jilek et al., 2015; Breeze, 2005).

Table 11.2 Types of Generators (Su and Gamal, 2011).

Chapter 12

Table 12.1 iAQ‐2000 specifications.

Table 12.2 OPM

specifications.

Chapter 13

Table 13.1 Case Study Input Parameters.

Table 13.2 Energy Prices from the Spanish Energy Retailer.

Table 13.3 Cost Results for the Three Scenarios.

Chapter 14

Table 14.1 DG Characteristics.

Table 14.2 DG Installation Capacity in Each Microgrid.

Table 14.3 Microgrid Load Characteristics.

Table 14.4 Energy Exchange Related Metrics of MMGS.

Table 14.5 Reliability Related Metrics of the MMGS.

Table 14.6 Reliability Related Metrics of the MMGS.

Table 14.7 Economic Related Metrics of the MMGS.

Chapter 15

Table 15.1 Summary of the Wireless Power Transfer Technologies

Chapter 16

Table 16.1 Frequencies Reserved by the CENELECT Standard for Smart Grid Communication [[17]].

Table 16.2 Mobile Technologies Data Rate Ranges Comparison.

Table 16.3 Simulation Parameters.

Table 16.4 Percentage of Success EV Connection for EV Charging Service Comparison between Standard IEEE802.11p and Adaptive CW.

Chapter 17

Table 17.1 Comparative Summary of Different Sensors for Parking Management.

Table 17.2 Comparison of Existing Parking Solutions.

Chapter 18

Table 18.1 Scheduling approaches used for EVs recharging in smart cities with different objectives, main constraints, recharging approach, and solver tools.

Chapter 19

Table 19.1 Summary of notations.

Table 19.2 Summary of notations.

Table 19.3 6‐bus test system – Nodal distances.

Table 19.4 Candidate responses for the 6‐bus test system.

Table 19.5 6‐bus system – Optimal response

Chapter 20

Table 1 Five Privacy‐Aware Principles for the Demand Response Platform.

Table 20.2 Privacy Engineering Activities and Methods.

Table 20.3 Privacy Principles and Corresponding Recommendations for Smart Grid High‐Level Consumer‐to‐Utility Privacy Impact Assessment.

Chapter 22

Table 22.1 Attack Mechanisms on Smart Cities Infrastructure Systems.

Table 22.2 NIST Cybersecurity Framework Core.

Table 22.3 NIST Framework Definitions.

Table 22.4 USA Energy Sector Case Study (US DOE, 2015).

Table 22.5 A Smart City Cybersecurity Fusion Center.

Chapter 23

Table 23.1 Series of Events.

Table 23.2 Indicator of Various LESs at Each Stage.

Table 23.3 System status and sampling data.

List of Illustrations

Chapter 01

Figure 1.1.1 Smart city architecture.

Figure 1.5.1 Operation overview of EH‐CR in the smart city.

Figure 1.5.2 Node architecture of EH‐CR node.

Figure 1.5.3 Ad hoc network architecture of EH‐CR in the smart city.

Figure 1.5.4 Clustered network architecture of EH‐CR in the smart city.

Chapter 02

Figure 2.1 Conventional power system with unidirectional communication (Khan and Khan, 2013).

Figure 2.2 Layers of an SG system.

Figure 2.3 Modbus communication stack.

Figure 2.4 General Modbus frame.

Figure 2.5 Message structure under IEC 60870‐5‐101 communication.

Figure 2.6 IEC 60870 communication stack.

Figure 2.7 DNP3 communication stack.

Figure 2.8 Message structure for DNP3 (Clarke et al., 2004).

Figure 2.9 Structure of the IEC 61850 standard (IEC, 2015; Mackiewicz, 2006).

Figure 2.10 Message communication stack of the IEC 61850 standard (Parikh et al., 2013).

Figure 2.11 Single line diagram of different bays (Adapted from (Zhang et al., 2015)).

Figure 2.12 Data flow diagram for a substation: (a) in normal circumstances; (b) during fault occurrence; and (c) for communication outside substations (Adapted from (Zhang et al., 2015)).

Figure 2.13 Example of the generation of cyclic messages for outside substation communication with IEC 61850.

Figure 2.14 Example of stochastic messages generated for communication in the power distribution grid.

Figure 2.15 Example of burst data generated for communication outside substations in DA.

Figure 2.16 MMS message stack (Adapted from (IEC, 2004), (Kalalas, 2014) and (Pham, 2013)).

Figure 2.17 GOOSE message stack in OSI‐7 layer (IEC, 2012; Kalalas, 2014).

Figure 2.18 Example of MUEs and CUEs distributed in a LTE cell.

Figure 2.19 Cdf of the delay for IEC 61850 messages.

Figure 2.20 Average latency of the conventional users as a function of the number of SG devices.

Chapter 03

Figure 3.1 Today's power grid where the DSO is operating blindly, i.e., without real‐time feedback of costumers' consumption.

Figure 3.2 Smart distribution grid with real‐time measurements of consumption and production at prosumer locations.

Figure 3.3 AID structure proposed in the IEEE 802.11ah.

Figure 3.4 Collecting hidden node information using the IEEE 802.11ah MAC.

Figure 3.5 Security bootstrap of Lora device (LoraWAN).

Figure 3.6 LoRa device security bootstrap using separate network and application server.

Figure 3.7 A high‐level overview of new machine‐type communications related functionality with the subsequent 3GPP releases.

Figure 3.8 Simplified illustration of downlink and uplink subframe organization in a 1.4 MHz system (Nielsen, 2017).

Figure 3.9 Message exchange between a smart meter and the eNodeB (Nielsen, 2017).

Figure 3.10 Outage comparison for only ARP and data transmission (ARP + Data) and full message exchange (ARP + Signaling + Data; Nielsen, 2017).

Figure 3.11 Probability of outage in LTE with respect the number of M2M arrivals per second in a 1.4 MHz and 5 MHz system for different models and payload sizes (Nielsen, 2017).

Figure 3.12 Illustration of different types of data protection schemes.

Figure 3.13 Illustration of access control delegation for a smart‐grid application.

Chapter 04

Figure 4.1 A functional architecture of the smart city concept. Sensing, Interconnecting, data, and service are four layers of this infrastructure. Data collection from lower layers to higher layers is shown by arrows.

Figure 4.2 Transition from traditional grids to smart grids.

Figure 4.3 NIST Smart Grid Conceptual Model.

Figure 4.4 General architecture of the customer domain.

Figure 4.5 General architecture of the markets domain.

Figure 4.6 General architecture of the service provider domain.

Figure 4.7 General architecture of the operations domain.

Figure 4.8 General architecture of the generation domain.

Figure 4.9 General architecture of the transmission domain.

Figure 4.10 General architecture of the distribution domain.

Figure 4.11 Scope of IEEE 2030 standardization process.

Figure 4.12 Smart Grid architecture model.

Figure 4.13 Main domains of the M2M reference architecture according to European Telecommunications Standards Institute.

Figure 4.14 General architecture of M2M communication system in the smart city's infrastructure.

Figure 4.15 Schematic topology of a hybrid passive optical network (HPON) for an advanced metering infrastructure (AMI). HPON is implemented as a backbone network. Wireless, microwave, and free space optics technologies are considered for distribution and access networks.

Figure 4.16 The structure of machine‐to‐machine (M2M) network for smart grids according to the European Telecommunications Standards Institute (ETSI). M2M domains are mapped onto the smart grid main layers.

Figure 4.17 Overall system architecture, highlighting the relation with the standardization work.

Figure 4.18 Mapping of the proposed M2M communications architecture onto the European Telecommunications Standards Institute (ETSI) M2M architecture applied to the Smart Grid.

Figure 4.19 Mapping of the proposed M2M communications architecture onto the power distribution infrastructure.

Figure 4.20 A typical architecture of a smart grid in the smart city paradigm.

Chapter 05

Figure 5.1 Conventional cellular communication (left side) versus direct D2D communication (right side): D2D communication minimizes data transmission in radio access networks, which improves spectrum efficiency.

Figure 5.2 An abstract view of smart city applications and its pillars: the part above the horizontal line represents various smart city application scenarios, which are connected to data acquisition and storage center through the Internet. The lower part of the figure represents different pillars of a smart city (adapted from Khan et al., 2013).

Figure 5.3 D2D communication as an aggregator for IoT traffic: home appliances are connected with a smartphone over a D2D link. The smartphone aggregates traffic from different sensing nodes and sends it to base stations when it has sufficient data to be transferred.

Chapter 06

Figure 6.1 Traditional power grid.

Figure 6.2 SDN framework.

Figure 6.3 Generic SDN benefits.

Figure 6.4 SDN‐based smart grid communication infrastructure.

Chapter 07

Figure 7.1 A typical grid‐based HetNet and a set of mobile FBSs in e‐mobility.

Figure 7.2 The queuing system considered with FBS departures and arrivals.

Figure 7.3 A finite state diagram for the HetNet system. It describes all the FBSs' states and their transitions in a smart grid setup where not only users are mobile but also the FBS itself.

Figure 7.4 Mobile FBS serving mobile/static users in a smart grid setup.

Figure 7.5 The effect of FBS velocity on MQL.

Figure 7.6 The effect of velocity of mobile users on throughput.

Figure 7.7 The effect of the FBS velocity on response time.

Figure 7.8 Energy spent per hour vs. the average MQL.

Figure 7.9 Energy spent per hour vs. the average service rate

μ

.

Figure 7.10 Response time and energy consumed as a function of the service rate.

Chapter 08

Figure 8.1 The current distribution network topology.

Figure 8.2 Illustration of an AMI.

Figure 8.3 Illustration of the next‐generation smart grid system.

Figure 8.4 Example of a DAG and a DODAG.

Figure 8.5 Example of an upward route construction with RPL.

Figure 8.6 Example of an AODV route detection between node A and G.

Figure 8.7 Example of a route construction with LOADng.

Figure 8.8 End‐to‐end delay comparison.

Figure 8.9 Data delivery ratio comparison.

Chapter 09

Figure 9.1 Applications of WSNs in Smart Cities.

Chapter 10

Figure 10.1 The VITAL platform architecture.

Figure 10.2 The VITAL prediction module extension.

Figure 10.3 D100 road segment and deployed speed sensors.

Figure 10.4 Neighbour sensor correlation: Correlation between speed observations at time t of sensor in the row and speeds at time t − 30 of sensor in the column. Sensors are sequential in direction from (a) Europe to Asia and (b) from Asia to Europe.

Figure 10.5 Prediction model training and online speed prediction for each sensor.

Figure 10.6 Training and test data set splitting. Two‐month training months (blue) and one‐month test (green) sets.

Chapter 11

Figure 11.1 Conceptual diagrams of a regular power grid and the smart grid.

Figure 11.2 Challenge areas of the RE‐integrated SG system.

Chapter 12

Figure 12.1 An outdoor self‐powered wireless sensor network.

Figure 12.2 The

floor of Bahen Centre for Information Technology building at the University of Toronto, Canada. It is a complex indoor environment with a number of classrooms, offices, and open areas. The materials are categorized into six types: thin, medium, and thick concrete, metal, glass, and wood. Each material has a different effect on the wireless signals.

Figure 12.3 A region of the

floor of Bahen Centre for Information Technology building at the University of Toronto, Canada, that was used for experimentation.

Figure 12.4 System framework of indoor

monitoring system.

Figure 12.5 iAQ‐2000 sensor.

Figure 12.6 OPM

relay node.

Figure 12.7 The indoor version of the prototype with (a) the assembly kit of the monitoring board, where a sensor unit is connected to a radio module through (b) a simple electric circuit.

Figure 12.8 Data packet format.

Figure 12.9 Packet process and transmission/reception.

Figure 12.10 Overview of the monitoring system.

Figure 12.11 An indoor

detection and monitoring system deployed at the 7th floor of Bahen Centre for Information Technology at the University of Toronto. The sensor units have been deployed in four rooms: office ‐1, office ‐2, laboratory, and meeting room. The sensor units transmit the monitoring data to the control room through the relay nodes.

Figure 12.12 Time series representation of air quality measurements at office ‐ 2 over

hours before and after smoothing.

Figure 12.13 Time series representation of air quality measurements at meeting room over March 2015 before and after smoothing.

Figure 12.14 Time series representation of air quality measurements over

hours.

Figure 12.15 Time series representation of air quality measurements over March 2015.

Chapter 13

Figure 13.1 Microgrid scenario for the proposed cooperative energy management model.

Figure 13.2 Energy exchange plan for the three buildings during a weekend day in February.

Figure 13.3 Energy exchange plan for the three buildings during a week day in February.

Figure 13.4 Energy exchange plan for the three buildings during a weekend day in July.

Figure 13.5 Energy exchange plan for the three buildings during a week day in July.

Chapter 14

Figure 14.1 Nested MMGS.

Figure 14.2 Parallel MMGS.

Figure 14.3 Series MMGS.

Figure 14.4 Dispersed MMGS.

Figure 14.5 Flowchart of the improved immune genetic algorithm.

Figure 14.6 Flowchart of optimal structure planning of MMGS using the improved immune genetic algorithm.

Figure 14.7 Flowchart of optimal capacity planning of MMGS using the improved immune genetic algorithm.

Figure 14.8 Flowchart of the annual performance assessment of an MMGS.

Figure 14.9 The structure of the MMGS which includes five networked MGs.

Chapter 15

Figure 15.1 Scheme of a wireless charger for EV.

Chapter 16

Figure 16.1 Channel allocation in WAVE (DSRC).

Figure 16.2 System overview.

Figure 16.3 Interaction V2I between EVs and RSU.

Figure 16.4 Back‐off counter Markov chain model.

Figure 16.5 Percentage of successful EV connection for EV charging service in dense scenarios (100 EVs).

Chapter 17

Figure 17.1 Taxonomy and general categorization of parking systems.

Figure 17.2 Illustrating the use of active and passive infrared sensors.

Figure 17.3 Occupancy detection by installing ultrasonic sensor above each spot to detect the presence of a vehicle.

Figure 17.4 Inductive loop detector installed at the entrance of a parking lot.

Figure 17.5 A magnetometer installed under a parking spot detects whether a large metal object, i.e., a vehicle, is present.

Figure 17.6 Illustrating the “around view monitor” support technology.

Figure 17.7 The RFID setup for parked vehicle detection, as proposed by Rahman et al. (2009).

Figure 17.8 Multi‐modal sensing architecture for parked vehicle detection. Picture courtesy of Barone et al. (2014).

Figure 17.9 An articulation of communication infrastructure with gateways nodes from car‐park‐network architecture proposed in Pham et al. (2015).

Figure 17.10 Implementation scenario of a hybrid network architecture for the parking systems of Cervantes et al. (2007).

Figure 17.11 A local system may be just a road sign showing occupancy for a specific parking lot.

Figure 17.12 An example distributed‐VANET architecture as suggested by Yamashita et al. (2014).

Figure 17.13 A functional diagram of the framework of parallel system of Wang et al. (2016).

Figure 17.14 User interfaces for smart phone app, (a) “Park Me” (ParkMeApp, 2016) and (b) “SF Park” (SFMTA, 2016).

Figure 17.15 Functional organization of the parking management application.

Figure 17.16 Screenshots of our initial parking Android app.

Figure 17.17 Flowchart description of the basic spot‐reporting operation.

Figure 17.18 Sample screenshot of leaderboard on the QuizUp Android game.

Figure 17.19 Flowchart for spot availability computation.

Figure 17.20 Average accuracy of system.

Figure 17.21 Number of accurate spot status under varying number of reports.

Figure 17.22 Effect of reputation scores on number of spots with accurate and unknown status.

Figure 17.23 Number of accurate spot reports with varying user truthfulness.

Chapter 18

Figure 18.1 EVs in smart cities are recharged through a centralized or distributed approach using different optimization approaches.

Figure 18.2 Main optimization approaches used to achieve various objectives during the scheduling of EVs‐recharging approaches.

Chapter 19

Figure 19.1 The 6‐bus test system

Figure 19.2 Threat levels in case of no response action

Figure 19.3 Effect of

on threat level of

Figure 19.4 Comparison of two potential responses

Figure 19.5 Threat level of

for different computational times

Chapter 20

Figure 20.1 Seven foundational principles of PbD.

Figure 20.2 The relationship among OECD Privacy Framework, ISO/IEC 29100:2011 Privacy Framework, and AICPA GAPPs.

Figure 20.3 Mapping between UTI and DPM triads.

Figure 20.4 Mapping of the fundamental privacy engineering activities into stages of the typical SELC (MITRE‐CoP, 2014).

Figure 20.5 Overall privacy‐aware engineering flow.

Figure 20.6 Equation expression for a system privacy risk model.

Chapter 21

Figure 21.1 Network model under consideration.

Figure 21.2 Illustration of the privacy‐preserving charging coordination scheme.

Figure 21.3 Anonymous data submission.

Figure 21.4 Permutation of requests.

Figure 21.5 Average number of expired requests without full charge versus charging request rate (

).

Figure 21.6 Average satisfaction index versus charging request rate.

Figure 21.7 Average entropy in case of 15% noise addition versus charging request rate.

Figure 21.8 Average entropy in case of 30% noise addition versus charging request rate.

Figure 21.9 Average entropy and satisfaction versus noise with

.

Figure 21.10 Average entropy and satisfaction versus noise with

.

Chapter 22

Figure 22.1 Smart city services.

Figure 22.2 Five‐step design process for securing smart cities.

Figure 22.3 Big data fusion center with the NIST framework.

Chapter 23

Figure 23.1 Topologies of Grid Network.

Figure 23.2 Data flows and energy flows for three generations of power systems. The single lines, double lines, and triple lines indicate the flows of G1, G2, and G3, respectively.

Figure 23.3 Data management systems and work modes for three ages of power systems. The above, middle, and below parts indicate the data management systems and the work modes of G1, G2, and G3, respectively. For G1, each grid works independently. For G2, global and local control centers are operating under the team‐work mode. For G3, the group‐work mode breaks through the regional limitation for energy.

Figure 23.4 Smart grid with 4Vs data and its SA.

Figure 23.5 Conceptual representation of the structure of the spatiotemporal PMU data.

Figure 23.6 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

according to our algorithm, with the histogram of eigenvalues for

, for

.

are free semicircular elements and

are independent standard Gaussian random matrices.

Figure 23.7 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

, for

.

are of free Poisson elements, and

are Wishart random matrices.

Figure 23.8 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

, for

.

is of free semicircular elements and

free Poisson ones.

is an independent standard Gaussian random matrix, and

is a Wishart matrix.

Figure 23.9 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

, for

.

are of free Poisson elements, and

are Wishart random matrices.

Figure 23.10 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

, for

.

are of free Poisson elements, and

are Wishart random matrices.

Figure 23.11 Comparison of the distribution of

according to our algorithm, with the histogram of eigenvalues for

, for

.

are of free semicircular elements and

free Poisson ones.

is an independent standard Gaussian random matrix, and

is a Wishart matrix.

Figure 23.12 Parameter learning of the IEEE 118‐bus system.

Figure 23.13 Data analysis of the realistic 34‐PMU power flow around events occurrence.

Figure 23.14 SA and its methodology.

Figure 23.15 Partitioning network for the IEEE 118‐node system.

Figure 23.16 Assumed event, data source, and category for case.

Figure 23.17 Anomaly detection result.

Figure 23.18 Illustration of various LES indicators.

Figure 23.19 Anomaly detection using LUE matrices.

Figure 23.20 Anomaly detection using GUE matrices.

Figure 23.21 The

curve and

curve.

Figure 23.22 RMT‐based results for voltage stability evaluation.

Figure 23.23 Sensitivity analysis based on concatenated matrix.

Figure 23.24 The event assumptions on time series.

Figure 23.25 Data fusion using multivariate linear polynomial

.

Figure 23.26 Data fusion using multivariate nonlinear polynomial

.

Guide

Cover

Table of Contents

Begin Reading

Pages

C1

xxi

xxii

xxiii

xxiv

xv

xvi

xvii

xxvii

xxviii

xxix

xxx

xxxi

xxxii

1

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

21

22

23

24

25

26

27

28

29

30

31

32

33

34

35

36

37

38

39

40

41

42

43

44

45

46

47

48

49

50

51

52

53

54

55

56

57

58

59

60

61

62

63

64

65

66

67

68

69

70

71

72

73

74

75

76

77

78

79

80

81

82

83

84

85

86

87

88

89

90

91

92

93

94

95

96

97

98

99

100

101

102

103

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

139

140

141

142

143

144

145

146

147

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

171

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

191

192

193

194

195

196

197

198

199

200

201

202

203

204

205

206

207

208

209

210

211

212

213

214

215

216

217

217

218

219

220

221

222

223

224

225

226

227

228

229

230

231

232

233

234

235

236

237

238

239

240

241

242

243

244

245

246

247

248

249

250

251

252

253

254

255

256

257

258

259

260

261

262

263

264

265

266

267

268

269

269

270

271

272

273

274

275

276

277

278

279

280

281

282

283

284

285

286

287

288

289

290

291

291

293

294

295

296

297

298

299

300

301

302

303

304

305

306

307

308

309

310

311

312

313

314

315

316

317

318

319

320

321

322

323

324

325

326

327

328

329

330

331

332

333

334

335

336

337

338

339

340

341

342

343

344

345

346

347

348

349

350

351

352

353

354

355

355

356

357

358

359

360

361

362

363

364

365

366

367

368

369

370

371

372

373

374

375

376

377

378

379

380

381

382

383

384

385

386

387

388

389

390

391

392

393

394

395

396

397

398

399

400

401

402

403

404

405

406

407

408

409

410

411

413

414

415

416

417

418

419

420

421

422

423

424

425

426

427

427

428

429

430

431

432

433

434

435

436

437

438

439

440

441

442

443

443

444

445

446

447

448

449

450

451

452

453

454

455

456

457

458

459

460

461

462

463

464

465

466

467

468

469

470

471

472

473

474

475

476

477

478

479

480

481

482

483

484

485

486

487

488

489

490

491

492

493

494

495

496

497

498

499

500

501

502

503

504

505

506

507

507

509

510

511

512

513

514

515

516

518

517

519

520

521

522

523

524

525

526

527

528

529

530

531

532

533

534

535

536

537

538

539

540

541

542

543

544

545

546

547

548

549

550

551

552

553

554

555

555

556

557

558

559

560

561

562

563

564

565

566

567

568

569

570

571

572

573

574

575

576

577

577

578

579

580

581

582

583

584

585

586

587

588

589

590

591

592

593

594

595

596

597

598

599

600

601

602

603

604

605

606

607

608

609

610

611

612

613

614

615

616

617

618

619

620

621

622

623

624

625

626

627

628

629

630

631

632

633

634

636

637

638

639

640

641

642

643

644

645

646

647

648

649

650

651

652

E1

Transportation and Power Grid in Smart Cities

Communication Networks and Services

Edited by

Hussein T. Mouftah and Melike Erol-Kantarci

University of OttawaOttawa, Canada

Mubashir Husain Rehmani

COMSATS Institute of Information TechnologyWah Cantt, Pakistan

Copyright

This edition first published 2019

© 2019 John Wiley & Sons Ltd

All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.

The right of Hussein T. Mouftah, Melike Erol‐Kantarci and Mubashir Husain Rehmani to be identified as the authors of the editorial material in this work has been asserted in accordance with law.

Registered Offices

John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA

John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

Editorial Office

The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, UK

For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.

Wiley also publishes its books in a variety of electronic formats and by print‐on‐demand. Some content that appears in standard print versions of this book may not be available in other formats.

Limit of Liability/Disclaimer of Warranty

MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This work's use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLAB® software.

While the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

Library of Congress Cataloging‐in‐Publication Data

Names: Mouftah, Hussein T., editor. | Erol-Kantarci, Melike, editor. |

Rehmani, Mubashir Husain, 1983- editor.

Title: Transportation and power grid in smart cities : communication networks and services / edited by Hussein T. Mouftah, Melike Erol-Kantarci, Mubashir Husain Rehmani.

Description: Hoboken, NJ : John Wiley & Son, 2019. | Includes bibliographical references and index. | Description based on print version record and CIP data provided by publisher; resource not viewed.

Identifiers: LCCN 2018012509 (print) | LCCN 2018028974 (ebook) | ISBN 9781119360094 (pdf) | ISBN 9781119360117 (epub) | ISBN 9781119360087 (cloth : alk. paper)

Subjects: LCSH: Smart power grids-Communication systems. | Urban transportation.

Classification: LCC TK3105 (ebook) | LCC TK3105 .T73 2018 (print) | DDC 388.3/12-dc23

LC record available at https://lccn.loc.gov/2018012509

Cover image: © chinaface/iStockphoto; © oonal/iStockphoto; © ansonmiao/iStockphoto

Cover design by Wiley

List of Contributors

Taufik Abrao

Department of Electrical Engineering

Londrina State University (DEEL‐UEL)

Brazil

Ibrahim Abualhaol

Carleton University

Ottawa

Canada

José A. Aguado

University of Málaga

Spain

Ozgur B. Akan

Koc University

Turkey

Kemal Akkaya

Florida International University

Miami, FL

USA

Fadi Al‐Turjman

Antalya Bilim University

Antalya

Turkey

Muhammmad Amjad

COMSATS Institute of Information Technology

Wah Cantt

Pakistan

Muhammad Rizwan Asghar

Department of Computer Science

The University of Auckland

New Zealand

Ravil Bikmetov

University of North Carolina at Charlotte

NC

USA

Shengxia Cai

Zhou Enlai School of Government

Nankai University

China

Hakki C. Cankaya

Fujitsu Network Communications

Richardson, TX

USA

Oktay Cetinkaya

Next‐generation and Wireless Communications Laboratory

Koc University

Turkey

Periklis Chatzimisios

Department of Informatics

Thessaloniki

Greece

Lei Chu

Research Center for Big Data Engineering and Technology

State Energy Smart Grid Research and Development Center, Shanghai, China

and

Department of Electrical Engineering

Shanghai Jiaotong University

Shanghai, China

Melike Erol‐Kantarci

School of Electrical Engineering and Computer Science

University of Ottawa

Canada

Mahmoud Gad

Cognitive Labs Inc

Ottawa

Canada

Hervé Ganem

Gemalto

Paris, France

Fabrizio Granelli

Department of Information Engineering and Computer Science

University of Trento

Italy

Halil Gulacar

Department of Computer Engineering

Istanbul Technical University

Turkey

Sanket Gupte

Department of Computer Science and Electrical Engineering

University of Maryland Baltimore County

Baltimore

Guillaume Habault

IMT Atlantique ‐ IRISA

France

Syed Hashim Raza Bukhari

COMSATS Institute of Information Technology

Wah Cantt

Pakistan

and

COMSATS Institute of Information Technology

Attock

Pakistan

Xing He

Research Center for Big Data Engineering and Technology

State Energy Smart Grid Research and Development Center, Shanghai, China

and

Department of Electrical Engineering

Shanghai Jiaotong University

Shanghai, China

Ekram Hossain

Department of Electrical and Computer Engineering

University of Manitoba

Canada

Md Maruf Hossain

Department of Natural and Applied Sciences (Engineering Technology Program)

University of Wisconsin‐Green Bay

USA

Muhammad Ismail

Texas A&M University at Qatar

Doha, Qatar

Ljupco Jorguseski

TNO

The Hague

Netherlands

Khurram Kazi

Draper Laboratory

Cambridge MA

USA

Francois Lemercier

Itron and IMT Atlantique ‐ IRISA

France

Zenan Ling

Research Center for Big Data Engineering and Technology

State Energy Smart Grid Research and Development Center, Shanghai, China

and

Department of Electrical Engineering

Shanghai Jiaotong University

Shanghai, China

Haichun Liu

Research Center for Big Data Engineering and Technology

State Energy Smart Grid Research and Development Center, Shanghai, China

and

Department of Electrical Engineering

Shanghai Jiaotong University

Shanghai, China

Qi Liu

Key Laboratory of Smart Grid of Ministry of Education

Tianjin University

China

Mohammad Upal Mahfuz

University of Wisconsin‐Green Bay

Wisconsin

USA

Mohamed Mahmoud

Tennessee Technological University

TN, USA

Patrick Maille

IMT Atlantique ‐ IRISA

France

Prodromos‐Vasileios Mekikis

Department of Informatics and Telecommunication

University of Athens

Athens

Greece

Nicolas Montavont

IMT Atlantique ‐ IRISA

France

Hussein T. Mouftah

School of Electrical Engineering and Computer Science (EECS)

University of Ottawa

Canada

Seyedamirabbas Mousavian

School of Business

Clarkson University

Potsdam

NY

USA

Ahmed O. Nasif

Department of Engineering Technology

University of Wisconsin‐Oshkosh

Wisconsin

USA

Jimmy Jessen Nielsen

Aalborg University

Denmark

Sema F. Oktug

Computer Engineering Department

Istanbul Technical University

Turkey

Leonardo D. Oliveira

Department of Telecommunications and Control Engineering

University of Sao Paulo

Brazil

Mustafa Ozger

Next‐generation and Wireless Communications Laboratory

Koc University

Turkey

Georgios Z. Papadopoulos

IMT Atlantique – IRISA

France

Marbin Pazos-Revilla

Tennessee Technological University

TN, USA

Konstantinos Plataniotis

Department of Electrical and Computer Engineering

University of Toronto

Canada

Petar Popovski

Aalborg University

Denmark

Khalid Qaraqe

Texas A&M University at Qatar

Doha, Qatar

Robert Qiu

Research Center for Big Data Engineering and Technology

State Energy Smart Grid Research and Development Center, Shanghai, China

and

Department of Electrical Engineering

Shanghai Jiaotong University

Shanghai, China

and

Tennessee Technological University

Cookeville TN

USA

Md. Abdur Rahman

Department of Electrical and Electronic Engineering

American International University‐Bangladesh

Bangladesh

Mubashir Husain Rehmani

COMSATS Institute of Information Technology

Wah Cantt

Pakistan

Dhaou Said

School of Electrical Engineering and Computer Science (EECS)

University of Ottawa

Ottawa

Canada

Erchin Serpedin

Texas A&M University

College Station

USA

Ahmed Sherif

Tennessee Technological University Cookeville

TN, USA

Sajid Siraj

University of Leeds

United Kingdom

and

COMSATS Institute of Information Technology

Wah Cantt

Pakistan

Petros Spachos

School of Engineering

University of Guelph

Canada

Alicia Triviño‐Cabrera

University of Málaga

Spain

Tariq Umer

COMSATS Institute of Information Technology

Wah Cantt

Pakistan

Muhammad Usman

Department of Information Engineering and Computer Science

University of Trento

Italy

Binod Vaidya

School of Electrical Engineering and Computer Science (EECS)

University of Ottawa

Canada

John Vardakas

Iquadrat Informatica

Barcelona

Spain

Christos Verikoukis

Telecommunications Technological Centre of Catalonia (CTTC/CERCA)

Barcelona

Spain

Shouxiang Wang

Key Laboratory of Smart Grid of Ministry of Education

Tianjin University

China

Lei Wu

Electrical and Computer Engineering Department

Clarkson University

Potsdam

NY

USA

M. Yasin Akhtar Raja

Center for Optoelectronics and Optical Communication

University of North Carolina at Charlotte

USA

Yusuf Yaslan

Computer Engineering Department

Istanbul Technical University

Turkey

Mohamed Younis

Department of Computer Science and Electrical Engineering

University of Maryland Baltimore County

USA

Ioannis Zenginis

Iquadrat Informatica

Barcelona

Spain

Haibin Zhang

TNO

The Hague

The Netherlands

Ziming Zhu

Toshiba Research Europe Ltd

Bristol

United Kingdom

Preface

In recent years, there has been an increasing trend in population moving toward urban regions and large cities. It is envisioned that the future cities around the world will be smart cities. Plenty of efforts have been made to improve the quality of inhabitants of smart cities by integrating different technologies in their day‐to‐day lifestyle. These improvements include advancement in public facilities such as water systems, transportation systems, and the electricity system. In smart cities, information and communication technologies (ICT) will play a vital role for providing services in urban environments. These services include real‐time monitoring and control through wireless sensor and actuator networks. Smart grids (SGs), intelligent transportation systems (ITS), the Internet of Things (IoT), electric vehicles (EVs), and wireless sensor networks (WSNs) will be the building blocks of future smart cities. “Smart grid” refers to the modernization of the traditional power grid by incorporating two‐way digital communication support at the generation, transmission, and distribution levels. “Intelligent transportation system” refers to making the vehicular traffic smarter by reducing congestion, optimizing fuel consumption, choosing shorter routes, and improving safety, as well as allowing self‐driving cars by using communication and sensing technologies. The “Internet of Things” refers to a worldwide network of interconnected objects uniquely addressable, based on standard communication protocols and allows people and things to be connected anytime, anyplace, with anything and anyone, ideally using any path/network and any service. The IoT can be very useful for resource management in the context of smart cities. Wireless sensor networks are composed of sensor nodes capable of performing sensing. The application of WSNs ranges from environmental monitoring to forest fire detection and from power system applications to disaster, security, emergency applications in urban environments. Electric vehicles aim to reduce vehicle emissions and can also be envisaged as mobile power stations, which can introduce the consumer‐generated energy to the main electrical grid. All these technologies will somehow help to build a smart city.

This book provides detailed insights on communication networks and services for transportation and power grid for the future smart cities. The book aims to be a complementary reference for the smart city governors, utility operators, telecom operators, communications engineers, power engineers, electric vehicle service providers, university professors, researchers, and students who would like to grasp the advances in smart cities, smart grid, and intelligent transportation. This book accommodates 23 book chapters authored by world‐renowned experts, all presenting their views on transportation and power grid in smart cities with a focus on communication networks and services. The chapters are organized in five parts.

Part I: Communication Technologies for Smart Cities focuses on the latest advancements in smart grid communications including cognitive radio based solutions, device‐to‐device communications, and 5G. Part I consists of five chapters.

Chapter 1 “Energy‐Harvesting Cognitive Radios in Smart Cities,” authored by Mustafa Ozger, Oktay Cetinkaya and Ozgur B. Akan, discusses the potential use cases of energy harvesting cognitive radios in smart cities along with research challenges that need to be addressed. Cognitive radio is a revolutionary technology that allows for opportunistic use of the unused spectrum frequencies to increase the communication capabilities and improve the overall system performance. On the other hand, energy harvesting brings a new perspective to the operation of cognitive radio, and their use in smart cities bare many opportunities that can lead to remarkable advances.

Chapter 2 “LTE‐D2D Communication for Power Distribution Grid: Resource Allocation for Time‐Critical Applications,” authored by Leonardo Dagui de Oliveira, Taufik Abrao, and Ekram Hossain, focuses on device‐to‐device communications and LTE integration for applications in time‐critical smart grid infrastructure. The authors propose a full duplex LTE‐D2D scheduler to improve the capacity of LTE networks to enhance their performance in smart city applications.

Chapter 3 “5G and Cellular Networks in the Smart Grid,” authored by Jimmy Jessen Nielsen, Ljupco Jorguseski, Haibin Zhang, Hervé Ganem, Ziming Zhu, and Petar Popovski, describes and analyzes the most relevant wireless cellular communication technologies for supporting the smart grid. Under the umbrella of 3GPP, the authors have looked specifically at releases up to and including Release 13, as well as considering the non‐3GPP technologies such as IEEE 802.11ah, Sigfox, and LoRa. The authors provide a solid tutorial on cellular networks and their use in smart grid and smart cities.

Chapter 4 “Machine‐to‐Machine Communications in the Smart City—a Smart Grid Perspective,” authored by Ravil Bikmetov, M. Yasin Akhtar Raja, and Khurram Kazi, presents intelligent Machine‐to‐Machine Communication techniques that can be used in smart cities. The chapter discusses optimization algorithms and machine learning techniques for efficient management of energy services in smart cities.

Chapter 5 “5G and D2D Communications at the Service of Smart Cities,” authored by Muhammad Usman, Muhammad Rizwan Asghar and Fabrizio Granelli, provides an excellent survey on 5G and Device‐to‐Device (D2D) communications in the context of smart cities. The chapter presents smart city scenarios, their communication requirements, and the potential impact on the life of citizens as well as discussing the impact of big data on smart cities with potential security and privacy concerns.

Part II: Emerging Communication Networks for Smart Cities consists of five chapters that focus on emerging networks for smart cities.

Chapter 6 “Software Defined Networking and Virtualization for Smart Grid,” authored by Hakki C. Cankaya, gives a comprehensive review of the state of the art in smart grid and SDN. The chapter then discusses the use cases for SDN for smart grid as well as several smart city scenarios.

Chapter 7 “GHetNet: A Framework Validating Green Mobile Femtocells in Smart‐Grids,” authored by Fadi Al‐Turjman, focuses on energy‐efficiency aspects of communications systems and presents energy‐based analysis of femtocells in ultra‐large scale (ULS) applications such as the smart grid.

Chapter 8 “Communication Architectures and Technologies for Advanced Smart Grid Services,” authored by Francois Lemercier, Guillaume Habault, Georgios Z. Papadopoulos, Patrick Maille, Periklis Chatzimisios, and Nicolas Montavont, presents communication architectures and technologies employed in the smart grid and the requirements for next‐generation smart grid systems. The chapter compares existing routing families in the constrained‐based smart grid environment.

Chapter 9 “Wireless Sensor Networks in Smart Cities: Applications of Channel Bonding to Meet Data Communication Requirements,” authored by Syed Hashim Raza Bukhari, Sajid Siraj, and Mubashir Husain Rehmani, motivates the use of WSN‐based solutions in smart cities. The authors have introduced a channel‐bonding technique for cognitive radios that can meet the requirements of high‐bandwidth applications in smart cities. The chapter concludes with interesting future directions that pinpoint the open issues in this very active area of research.

Chapter 10 “A Prediction Module for Smart City IoT Platforms,” authored by Sema F. Oktug, Yusuf Yaslan, and Halil Gulacar, brings the IoT perspective to the smart city discussion of our book. The authors emphasize the significance of prediction and present a tool that is used for prediction of traffic jams in populated smart cities.

Part III: Renewable Energy Resources and Microgrid in Smart Cities covers integration of renewable energy sources and the use of microgrids in smart cities. This part consists of four chapters.

Chapter 11 “Integration of Renewable Energy Resources in the Smart Grid: Opportunities and Challenges,” authored by Mohammad Upal Mahfuz, Ahmed O. Nasif, Md Maruf Hossain, and Md Abdur Rahman, presents the opportunities and the corresponding challenges of integrating renewable energy resources in the smart grid. This chapter extensively discusses the impact of renewable energy on sustainable smart grid and sustainable cities.

Chapter 12 “Environmental Monitoring for Smart Buildings,” authored by Petros Spachos and Konstantinos Plataniotis, focuses on smart buildings, which are an important component of the smart city. Their chapter introduces a wireless sensor network system that monitors the quality of the air in smart buildings. A framework is proposed for real‐time remote monitoring of the carbon dioxide in a complex indoor environment showing an excellent real‐world implementation of a smart building.

Chapter 13 “Cooperative Energy Management in Microgrids,” authored by Ioannis Zenginis, John Vardakas, Prodromos‐Vasileios Mekikis, and Christos Verikoukis, presents a cooperative energy management model for buildings that can exchange the energy produced by their PV panels or stored at their energy storage systems (ESSs), in a smart way so that the excess energy of buildings with energy surplus is consumed by buildings of the same microgrid with energy deficit. Energy management of buildings is certainly an important part of smart cities, and this chapter has a special focus on buildings and energy management.

Chapter 14 “Optimal Planning and Performance Assessment of Multi‐Microgrid Systems in Future Smart Cities,” authored by Shouxiang Wang, Lei Wu, Qi Liu, and Shengxia Cai, introduces optimal planning for multi‐microgrids (MG). A microgrid is a small‐scale power system containing distributed generation, loads, ESSs, and a control system. MGs have high flexibility so that they can be connected to the distribution network or work in an isolated mode when grid faults occur in the distribution network. Therefore, they are anticipated to have a significant role in smart cities. This chapter provides valuable results on resilient planning of multi‐microgrids.

Part IV: Smart Cities, Intelligent Transportation System and Electric Vehicles includes four chapters focusing on electric vehicles and intelligent transportation solutions that are a part of smart cities.

Chapter 15 “Wireless Charging for Electric Vehicles in the Smart Cities: Technology Review and Impact,” authored by Alicia Triviño‐Cabrera and José A. Aguado, provides an extensive review on wireless power transfer applied to the charging of electric vehicles and studies the scheduling algorithms that control the timing of the charge process in a group of EVs. The authors provide useful insights and present open issues in this exciting field of research.

Chapter 16 “Channel Access Modelling for EV Charging/Discharging Service through Vehicular ad hoc Networks (VANETs) Communications,” authored by Dhaou Said and Hussein T. Mouftah, first presents the scheduling problem for electric vehicle (EV) charging/discharging and then introduces a specific case study of the channel access modelling for the EV charging service based on the IEEE802.11p/DSRC protocol.

Chapter 17 “Intelligent Parking Management in Smart Cities,” authored by Sanket Gupte and Mohamed Younis, focuses on a popular smart city application, namely intelligent parking. The authors provide a taxonomy of parking systems, they survey existing solutions, and highlight the most important aspects, advantages, and shortcomings, and then they categorize regular parking systems based on the sensing infrastructure, communication infrastructure, storage infrastructure, application infrastructure, and user interfacing.

Chapter 18