Оглавление
Группа авторов. Cyberphysical Smart Cities Infrastructures
Table of Contents
List of Tables
List of Illustrations
Guide
Pages
Cyberphysical Smart Cities Infrastructures. Optimal Operation and Intelligent Decision Making
Biography
List of Contributors
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
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
2.3.1 Data Capturing
2.3.2 Data Analysis
2.3.2.1 Big Data Algorithms and Challenges
2.3.2.2 Machine Learning Process and Challenges
2.3.2.3 Deep Learning Process and Challenges
2.3.2.4 Learning Process and Emerging New Type of Data Problems
2.3.3 Decision‐Making Problems in Smart Cities
2.3.3.1 Traffic Decision‐Making System
2.3.3.2 Safe and Smart Environment
2.4 Conclusion
References
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
3.3.1 Language Grounding
3.3.2 Language Plus Vision
3.3.3 Embodied Visual Recognition
3.3.4 Embodied Question Answering
3.3.5 Interactive Question Answering
3.3.6 Multi‐agent Systems
3.4 Simulators
3.4.1 MINOS
3.4.2 Habitat
3.5 Future of Embodied AI. 3.5.1 Higher Intelligence
3.5.2 Evolution
3.6 Conclusion
References
4 Analysis of Different Regression Techniques for Battery Capacity Prediction
4.1 Introduction
4.2 Data Preparation. 4.2.1 Dataset
4.2.2 Feature Extraction
4.2.3 Noise Addition
4.3 Experiment Design and Machine Learning Algorithms
4.4 Result and Analysis
4.5 Threats to Validity
4.6 Conclusions
References
Note
5 Smart Charging and Operation of Electric Fleet Vehicles in a Smart City
5.1 Smart Charging in Transportation
5.1.1 Available EV Charging Technologies. 5.1.1.1 Inductive Charging
5.1.1.2 Battery Swapping
5.1.1.3 Automatic Robotic Charging Connector
5.1.1.4 Automatic Ground‐Based Docking Connector
5.1.2 Current Regulations on Smart Charging
5.2 Cyber‐Physical Aspects of EV Networks
5.2.1 Sensing and Cooperative Data Collection
5.2.2 Data‐Driven Control and Optimization
5.3 Charging of Electric Fleet Vehicles in Smart Cities
5.3.1 Intelligent Management of Fleets of Electric Vehicles. 5.3.1.1 Charging of EV Fleets
5.3.1.2 Route Mapping with Charging
5.3.2 Electricity Grid Support Services
5.3.2.1 Demand Response
5.3.2.2 Frequency Response
5.3.2.3 Emergency Power
5.3.2.4 Emergency Response
5.4 Data and Cyber Security of EV Networks
5.4.1 Attack Schemes
5.4.1.1 Data Injection
5.4.1.2 Distributed Denial of Service
5.4.1.3 Data and Identity Theft
5.4.1.4 Man‐in‐the‐Middle Attack
5.4.2 Attack Detection Methods
5.4.2.1 Abnormal State Estimation
5.4.2.2 Message Encryption and Authentication
5.4.2.3 Denial‐of‐Service Attacks
5.4.3 Privacy Concerns and Privacy‐Preserving Methods
5.5 EV Smart Charging Strategies
5.5.1 Optimization Approaches
5.5.1.1 Future Scheduling
5.5.1.2 Battery Health Optimization
5.5.1.3 Energy Loss Minimization
5.5.2 Artificial Intelligence Approaches
5.5.2.1 Deep Learning for Smart Charging
5.5.2.2 Predicting Charging Profiles
5.5.3 Coordinated Charging
5.5.3.1 Centralized Optimization
5.5.3.2 Distributed Optimization
5.5.4 Population‐Based Approaches
5.5.4.1 Case Study
5.6 Conclusion
Acknowledgments
References
Note
6 Risk‐Aware Cyber‐Physical Control for Resilient Smart Cities
6.1 Introduction
6.2 System Model
6.2.1 Communication Latency in Smart Grid Systems
6.2.2 Risk Model for Communication Links
6.2.3 History of Communication Links
6.3 Risk‐Aware Quality of Service Routing Using SDN. 6.3.1 Constrained Shortest Path Routing Problem Formulation
6.3.2 SDN Architecture and Implementation
6.3.3 Risk‐Aware Routing Algorithm
6.4 Risk‐Aware Adaptive Control. 6.4.1 Smart Grid Model
6.4.2 Parametric Feedback Linearization Control
6.4.3 Risk‐Aware Routing and Latency‐Adaptive Control Scheme
6.5 Simulation Environment and Numerical Analysis
6.5.1 Avoiding Vulnerable Communication Links While Meeting QoS Constraint
6.5.2 Algorithm Overhead Comparison
6.5.3 Impact of QoS Constraints
6.5.4 Impact on Distributed Control
6.6 Conclusions
References
Notes
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
7.2.1 Data Description
7.2.2 Robust Possibilistic C‐Regression Models
7.2.3 Wind Speed Data Analysis Procedure
7.3 Experiment and Discussion
7.4 Conclusion
References
8 Intelligent Traffic: Formulating an Applied Research Methodology for Computer Vision and Vehicle Detection
8.1 Introduction. 8.1.1 Introduction
8.1.2 Background
8.1.3 Problem Statement. 8.1.3.1 Purpose of Research
8.1.3.2 Research Questions
8.1.3.3 Study Aim and Objectives
8.1.3.4 Significance and Structure of the Research
8.2 Literature Review. 8.2.1 Introduction
8.2.2 Machine Learning, Deep Learning, and Computer Vision. 8.2.2.1 Machine Learning
8.2.2.2 Deep Learning
8.2.2.3 Computer Vision
8.2.3 Object Recognition, Object Detection, and Object Tracking
8.2.3.1 Object Recognition
8.2.3.2 Object Detection
8.2.3.3 Object Tracking
8.2.4 Edge Computing, Fog Computing, and Cloud Computing
8.2.4.1 Edge Computing
8.2.4.2 Fog Computing
8.2.4.3 Cloud Computing
8.2.5 Benefits of Computer Vision‐Driven Traffic Management
8.2.6 Challenges of Computer Vision‐Driven Traffic Management
8.2.6.1 Big Data Issues
8.2.6.2 Privacy Issues
8.2.6.3 Technical Barriers
8.3 Research Methodology
8.3.1 Research Questions and Objectives
8.3.2 Study Design
8.3.2.1 Selection Rationale
8.3.2.2 Potential Challenges
8.3.3 Adapted Study Design Research Approach
8.3.4 Selected Hardware and Software
8.3.4.1 Hardware: The NVIDIA Jetson Nano Developer Kit and Accompanying Items
8.3.5 Hardware Proposed. 8.3.5.1 Software Stack: NVIDIA Jetpack SDK and Accompanying Requirements (All Iterations)
8.3.6 Software Proposed
8.4 Conclusion
References
9 Implementation and Evaluation of Computer Vision Prototype for Vehicle Detection
9.1 Prototype Setup. 9.1.1 Introduction
9.1.2 Environment Setup
9.2 Testing. 9.2.1 Design and Development: The Default Model and the First Iteration
9.2.2 Testing (Multiple Images)
9.2.3 Analysis (Multiple Images)
9.2.4 Testing (MP4 File)
Analysis (MP4 File)
9.2.5 Testing (Livestream Camera)
The Analysis (Livestream Camera)
9.3 Iteration 2: Transfer Learning Model. 9.3.1 Design and Development
9.3.2 Test (Multiple Images)
9.3.3 Analysis (Multiple Images)
9.3.4 Test (MP4 File)
9.3.5 Analysis (MP4 File)
9.3.6 Test (Livestream Camera)
9.3.7 Analysis (Livestream Camera)
9.3.8 Redesign
9.4 Iteration 3: Increased Sample Size and Change of Accuracy Analysis (Images) 9.4.1 Design and Development
9.4.2 Testing
9.4.3 Analysis
9.4.3.1 Confusion Matrices
9.4.3.2 Precision, Recall, and F‐score
9.5 Findings and Discussion
9.5.1 Findings: Vehicle Detection Across Multiple Images
9.5.2 Findings: Vehicle Detection Performance on an MP4 File
9.5.3 Findings: Vehicle Detection on Livestream Camera
9.5.4 Findings: Iteration 3
9.5.5 Addressing the Research Questions
9.5.6 Assessment of Suitability
9.5.7 Future Improvements
9.6 Conclusion
References
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. 10.3.1 IEC 61850 Standards and Classifications
10.3.2 Basics of IEC 61850 Architecture Model
10.3.3 IEC 61850 Class Model
10.3.4 IEC 61850 Logical Interfaces (Functional Hierarchy of IEC 61850)
10.4 IEC 61850 Features
10.4.1 MMS
10.4.2 GOOSE
10.4.3 Sampled Measured Value (SMV) or SV
10.4.4 R‐GOOSE and R‐SV
10.4.4.1 Application in Transmission Systems
Wide Area Control and Monitoring
State Estimation
Fault Location and Protection Design Scheme
10.4.4.2 Application in Distribution Systems. Load Modeling
Harmonic Estimation
Islanding Detection
10.4.5 Web Services
10.5 Relevant Application
10.5.1 Substation Automation System (SAS)
10.5.2 Energy Management System (EMS)
10.5.3 Distribution Management System (DMS)
10.5.3.1 Feeder Balancing and Loss Minimization Distribution
10.5.3.2 Voltage/VAR Optimization (VVO) and Conservation Voltage Reduction
10.5.3.3 Fault Location, Isolation, and Service Restoration
10.5.4 Distribution Automation (DA)
10.5.4.1 Voltage/VAR Control
10.5.4.2 Fault Detection and Isolation
10.5.4.3 Service Restoration Use Case
10.5.5 Distributed Generation and Demand Response Management (Distributed Energy Resource [DER])
10.5.5.1 Storage
10.5.5.2 Solar Panels
10.5.5.3 Wind Farm
10.5.5.4 Virtual Power Plant (VPP)
10.5.6 Advanced Metering Infrastructure (AMI)
10.5.7 Electric Vehicle (EV)
10.6 Advantages of IEC 61850 (Requirements of Smart Grid IEC 61850)
10.6.1 Communications Bandwidth
10.6.2 Interoperability
10.6.3 Cybersecurity
10.6.4 Information Security and Privacy
10.6.5 Security Aspects of IEC 61850 in Smart Grid Applications
10.6.5.1 How Security Can Be Breached
Attack Vectors
Techniques of Attack
10.6.5.2 Effects on the Security
10.6.5.3 Efforts to Address the Security Issues
Classical Approaches
IEC 62351
The Issue of Latency
Encryption and Authentication in Synchrophasor Technology
10.6.6 Reliability of Technology
10.7 Conclusion and Perspective
Acknowledgments
References
11 Electric Vehicles in Smart Cities
11.1 Introduction
11.2 Autonomous Vehicle
11.2.1 Classification of Vehicle Automation
11.2.2 Advantages of CAEVs
11.3 IoT Technology and CAEV
11.4 CAEV Applications and Services
11.4.1 CAEV Charging Management System
11.4.2 CAEV Taxi Service
11.5 Cybersecurity Issues of Internet of EVs
11.5.1 UPCEV Architecture
11.5.2 IoV Communication Technologies
11.5.3 IoEV Vulnerabilities
11.5.3.1 V2S Attacks
11.5.3.2 V2V Attacks
11.5.3.3 V2I Attack
11.5.3.4 V2N Attacks
11.6 IoT‐Based EV State Estimation and Control Under Cyberattacks
11.6.1 State Estimation Problem
11.6.2 Vehicle State Space and IoT Sensing Systems
11.6.3 State Estimation Under Cyberattack
11.7 Effect of EV Charging Behavior on Power System
11.7.1 Behavior of Electric Vehicle Parking Lots as Demand Response Aggregation Agents
11.7.2 Parking Lots in Demand Response Programs
11.7.3 Application of IoT in Demand Response Programs Based on Parking Lots
11.8 Charging Scheme for EVs Using IoT
11.8.1 System Model and IoT Architecture
References
Author Index
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