Cyberphysical Smart Cities Infrastructures

1 Cover

2 Title Page Cyberphysical Smart Cities Infrastructures Optimal Operation and Intelligent Decision Making Edited by M. Hadi Amini Florida International University Miami, Florida Miadreza Shafie‐khah University of Vaasa Finland

3 Copyright

4 Biography

5 List of Contributors

6 1 Artificial Intelligence and Cybersecurity: Tale of Healthcare Applications 1.1 Introduction 1.2 A Brief History of AI 1.3 AI in Healthcare 1.4 Morality and Ethical Association of AI in Healthcare 1.5 Cybersecurity, AI, and Healthcare 1.6 Future of AI and Healthcare 1.7 Conclusion References

7 2 Data Analytics for Smart Cities: Challenges and Promises 2.1 Introduction 2.2 Role of Machine Learning in Smart Cities 2.3 Smart Cities Data Analytics Framework 2.4 Conclusion References

8 3 Embodied AI‐Driven Operation of Smart Cities: A Concise Review 3.1 Introduction 3.2 Rise of the Embodied AI 3.3 Breakdown of Embodied AI 3.4 Simulators 3.5 Future of Embodied AI 3.6 Conclusion References

9 4 Analysis of Different Regression Techniques for Battery Capacity Prediction 4.1 Introduction 4.2 Data Preparation 4.3 Experiment Design and Machine Learning Algorithms 4.4 Result and Analysis 4.5 Threats to Validity 4.6 Conclusions References Note

10 5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City 5.1 Smart Charging in Transportation 5.2 Cyber‐Physical Aspects of EV Networks 5.3 Charging of Electric Fleet Vehicles in Smart Cities 5.4 Data and Cyber Security of EV Networks 5.5 EV Smart Charging Strategies 5.6 Conclusion Acknowledgments References Note

11 6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities 6.1 Introduction 6.2 System Model 6.3 Risk‐Aware Quality of Service Routing Using SDN 6.4 Risk‐Aware Adaptive Control 6.5 Simulation Environment and Numerical Analysis 6.6 Conclusions References Notes

12 7 Wind Speed Prediction Using a Robust Possibilistic C‐Regression Model Method: A Case Study of Tunisia 7.1 Introduction 7.2 Data Collection and Method 7.3 Experiment and Discussion 7.4 Conclusion References

13 8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection 8.1 Introduction 8.2 Literature Review 8.3 Research Methodology 8.4 Conclusion References

14 9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection 9.1 Prototype Setup 9.2 Testing 9.3 Iteration 2: Transfer Learning Model 9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) 9.5 Findings and Discussion 9.6 Conclusion References

15 10 A Review on Applications of the Standard Series IEC 61850 in Smart Grid Applications 10.1 Introduction 10.2 Overview of IEC 61850 Standards 10.3 IEC 61850 Protocols and Substandards 10.4 IEC 61850 Features 10.5 Relevant Application 10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850) 10.7 Conclusion and Perspective Acknowledgments References

16 11 Electric Vehicles in Smart Cities 11.1 Introduction 11.2 Autonomous Vehicle 11.3 IoT Technology and CAEV 11.4 CAEV Applications and Services 11.5 Cybersecurity Issues of Internet of EVs 11.6 IoT‐Based EV State Estimation and Control Under Cyberattacks 11.7 Effect of EV Charging Behavior on Power System 11.8 Charging Scheme for EVs Using IoT References

17 Author Index

18 End User License Agreement

1 Chapter 4Table 4.1 Correlation coefficients for batteryB0006.Table 4.2 Results for battery B0005.Table 4.3 Results for battery B0006.Table 4.4 Results for battery B0007.Table 4.5 Results for battery B0018.Table 4.6 Results for combined batteries.

2 Chapter 6Table 6.1 Processing time comparison.

3 Chapter 7Table 7.1 Performance indexes.Table 7.2 Performance indexes of the three local sites.Table 7.3 Prediction performance indexes of the three local sites.

4 Chapter 8Table 8.1 The adaptation of the DBR methodology for this research.Table 8.2 Applying phase 1 of the DBR methodology to the prototype.Table 8.3 Applying phase 2 of the DBR methodology to the prototype.Table 8.4 Applying phase 3 of the DBR methodology to the prototype.Table 8.5 Applying phase 4 of the DBR methodology to the prototype.Table 8.6 The hardware components obtained and utilized for the design and d...Table 8.7 Software obtained and utilized for the development and design of t...

5 Chapter 9Table 9.1 Datapoints obtained alongside the average and average using the fo...Table 9.2 Comparison of the prototype and current approaches.Table 9.3 Datapoints obtained alongside the average and average using the fo...Table 9.4 Datapoints obtained alongside the average and average using the fo...Table 9.5 List of command options for training the SSD MobileNet model [48].Table 9.6 Datapoints obtained alongside the average and average using the fo...Table 9.7 Datapoints obtained alongside the average and average using the fo...Table 9.8 Datapoints obtained alongside the average and average using the fo...Table 9.9 Confusion matrix for the output of the default model.Table 9.10 Confusion matrix for the output of the transfer learning model.

6 Chapter 10Table 10.1 IEC 61850 substandard.Table 10.2 IEC61850 UML model.Table 10.3 Indemnification of “logical nodes” (LNs).Table 10.4 Type of message with distinct communication times.Table 10.5 Basic functions of an SAS.Table 10.6 Roles and descriptions for some logical nodes in PV.Table 10.7 Definition and description of IEC 61400‐25 protocol.Table 10.8 Wind farm systems logical nodes.Table 10.9 Components of AMI system.Table 10.10 EV classes and other DER models.

7 Chapter 11Table 11.1 Automation levels for vehicles introduced by SAE.Table 11.2 Parameters of differential equations.

1 Chapter 1 Figure 1.1 Intersection of AI, healthcare, and cybersecurity.

2 Chapter 2 Figure 2.1 Smart cities main features. Figure 2.2 Smart cities data analytics framework.

3 Chapter 3 Figure 3.1 Embodied AI in smart cities.

4 Chapter 4Figure 4.1 Battery capacity for different batteries with the usage of the ba...Figure 4.2 Average voltage for each discharging cycle of battery B0006 with ...Figure 4.3 Average current for each discharging cycle of battery B0006 with ...Figure 4.4 Average power for each discharging cycle of battery B0006 with us...Figure 4.5 Time required by the battery B0006 to reach the threshold voltage...Figure 4.6 Results for battery B0005 displaying relative comparison between ...Figure 4.7 Results for battery B0006 displaying relative comparison between ...Figure 4.8 Results for battery B0007 displaying relative comparison between ...Figure 4.9 Results for battery B0018 displaying relative comparison between ...Figure 4.10 Results for combined batteries displaying relative comparison be...

5 Chapter 5Figure 5.1 Electric car stock in main markets.Figure 5.2 Number of charging devices in the United Kingdom.Figure 5.3 Man‐in‐the‐middle attack.Figure 5.4 Simulated versus measured EV charge curve [74]. (a) Simulated CCC...Figure 5.5 Mean energy consumption of ...Figure 5.6 Density distribution ....Figure 5.7 Battery level of EVs ....Figure 5.8 Evolution of electricity price as a function of time for a day.

6 Chapter 6Figure 6.1 (a) A depiction of the communication network topology for part of...Figure 6.2 Cross‐layer risk‐aware cyber‐physical system.Figure 6.3 Pseudocode of the LARAC algorithm.Figure 6.4 Implementation of the SDN controller in the VLAA algorithm.Figure 6.5 SDN architecture.Figure 6.6 Implementation of the SDN switches in the VLAA algorithm.Figure 6.7 Test power system: single line diagram and the accompanying commu...Figure 6.8 Packets transmitted on high‐risk links.Figure 6.9 Impact of communication delay and bandwidth on performance.Figure 6.10 Leakage and processing time versus maximum allowable network del...Figure 6.11 Cyber attack impact on average latency.