Cyberphysical smart cities infrastructures : optimal operation and intelligent decision making / edited by M. Hadi Amini, Miadreza Shafie-khah.

Format:

Book

Contributor:

Amini, M. Hadi, editor.

Shafie-khah, Miadreza, editor.

Language:

English

Subjects (All):

Smart cities.

Smart structures.

Smart power grids.

Physical Description:

1 online resource (323 pages)

Place of Publication:

Hoboken, New Jersey : John Wiley & Sons, [2022]

Summary:

"This book introduces novel algorithms and solutions to real-world problems under the umbrella of cyberphysical systems. It is organized in two sections: the first covers optimization algorithms for large-scale decision-making and the second covers intelligent decision-making in cyberphysical smart cities. The book takes into account new directions in engineering and science by deploying novel efficient algorithms to enhance near-real-time operation of underlying networks and use of peer-to-peer communication. These include the more in-depth study of special issues on deployment of these algorithms to improve the operation of smart cities. The material is presented in a concise and understandable form, taking into account the requirements for technical texts"-- Provided by publisher.

Contents:

Cover

Title Page

Copyright

Contents

Biography

List of Contributors

Chapter 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

Chapter 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

Chapter 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

Chapter 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

Chapter 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.

Chapter 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

Chapter 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

Chapter 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

Chapter 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)

9.2.5 Testing (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

Chapter 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

10.4.4.2 Application in Distribution Systems

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).

Notes:

Description based on print version record.

Other Format:

Print version: Amini, M. Hadi Cyberphysical Smart Cities Infrastructures

ISBN:

9781119748328

1119748321

9781119748342

1119748348

9781119748311

1119748313

OCLC:

1266196787