Emerging Electrical and Computer Technologies for Smart Cities : Modelling, Solution Techniques and Applications.

Author
Mahela, Om Prakash [Browse]
Format
Book
Language
English
Εdition
1st ed.
Published/​Created
  • Milton : Taylor & Francis Group, 2024.
  • ©2024.
Description
1 online resource (387 pages)

Details

Summary note
This text discusses smart grid technologies including home energy management systems, demand management systems, source-side management systems and communication technologies for power supply management, and supervisory control and data acquisition.
Source of description
Description based on publisher supplied metadata and other sources.
Contents
  • Cover
  • Half Title
  • Title
  • Copyright
  • Table of Contents
  • About the Editors
  • Contributors
  • Section 1 General Overview
  • 1 Introduction
  • 1.1 The Smart City Concept
  • 1.2 Characteristics of Smart Cities
  • 1.3 Evaluation Index of Smart City Development
  • 1.4 The Construction and Inspiration of Smart Cities around the World
  • 1.4.1 Smart City Construction Worldwide
  • 1.5 Conclusion
  • References
  • 2 Overview of Recent Smart City Practices
  • 2.1 Introduction
  • 2.2 Smart City: Connotation and Concept
  • 2.2.1 Smart City 1.0
  • 2.2.2 Smart City 2.0
  • 2.2.3 Smart City 3.0
  • 2.3 Conclusion
  • 2.4 Acknowledgment
  • Section 2 Strategic Development of Cybersecurity for Smart Cities
  • 3 Cybersecurity for Smart Cities
  • 3.1 Introduction
  • 3.2 Cybersecurity
  • 3.3 Smart City Sensors
  • 3.3.1 Applications of IoT Sensors in Smart Cities
  • 3.4 Smart Cities and the Cloud
  • 3.5 Cybersecurity in Smart Cities
  • 3.5.1 Smart Power Networks
  • 3.5.2 Smart Buildings
  • 3.5.3 Smart Transportation
  • 3.5.4 Smart Healthcare Systems
  • 3.5.5 Smart Communication Systems
  • 3.6 Cybersecurity Challenges in Smart Cities
  • 3.7 Conclusion
  • 3.8 Acknowledgment
  • 4 Cybersecurity Issues and Solutions for Smart Power Networks
  • 4.1 Introduction
  • 4.2 Cybersecurity Issues in the Power Sector
  • 4.3 Increasing Cyber-Physical Systems
  • 4.4 Present-Day Cybersecurity Standards in the Electricity Industry
  • 4.5 Elevated Significance of Cybersecurity in the Electricity Industry Relative to Other Sectors
  • 4.5.1 Fundamental Critical Infrastructure
  • 4.5.2 Public Safety Risks
  • 4.5.3 Large-Scale, Interconnected Systems
  • 4.5.4 Real-Time Operational Requirements
  • 4.5.5 Transition to Smart Grids and IoT
  • 4.5.6 Legacy Systems and Interoperability Challenges
  • 4.5.7 Regulatory Compliance.
  • 4.6 Cyberthreat-Resilient Power Systems
  • 4.7 Three-Dimensional Model for Organization Cybersecurity Maturity
  • 4.7.1 Hunting
  • 4.7.2 Detection
  • 4.7.3 Response
  • 4.8 Conclusion
  • Section 3 Artificial Intelligence Applications for Smart Cities
  • 5 Recent Trends in Artificial Intelligence Applications for Smart Cities: A Review
  • 5.1 Introduction
  • 5.2 Background Study on Smart Cities and AI
  • 5.3 AI Adoption in Smart Cities
  • 5.3.1 Transportation
  • 5.3.2 Energy Management
  • 5.3.3 Healthcare
  • 5.3.4 Environmental Monitoring and Waste Management
  • 5.3.5 Smart Irrigation and Agriculture
  • 5.4 Ten Countries Implementing AI for Their City Development
  • 5.5 Challenges and Future Scope
  • 5.6 Conclusion
  • 6 VANETs for Smart Cities: Opportunities and Upcoming Research Directions
  • 6.1 Introduction
  • 6.1.1 Application
  • 6.1.2 Architecture
  • 6.1.3 Characteristics and Issues
  • 6.2 Artificial Intelligence in VANETs
  • 6.3 Autonomous Driving
  • 6.4 Game-Theory-Based Applications in VANETs
  • 6.5 Security and Privacy Preservation
  • 6.5.1 A Trust-Management-Based IoV Forest Fire Scheme
  • 6.5.2 An FL-Based Privacy-Preserving Protocol for a Vehicular Fog Environment
  • 6.5.3 The Role of Blockchain in VANETs
  • 6.5.4 Vehicular Compatibility Routing Scheme in the IoV Using ML
  • 6.6 The Role of ML and DL Techniques in VANETs
  • 6.7 Future Directions
  • 6.8 Conclusion
  • 7 Urban Smart Parking Systems: A Taxonomic Approach
  • 7.1 Introduction
  • 7.1.1 Modules of Smart Parking Systems
  • 7.2 Literature Survey
  • 7.3 SPS-Enabling Components
  • 7.4 Types of Parking Lots
  • 7.4.1 Indoor Parking
  • 7.4.2 Outdoor Parking
  • 7.5 Challenges in the Current Systems
  • 7.6 Conclusion
  • 8 Machine Learning Approaches in Financial Management of Smart Cities
  • 8.1 Introduction.
  • 8.2 Categorisation of Data Mining Techniques
  • 8.2.1 Classification
  • 8.2.2 Clustering
  • 8.2.3 Prediction
  • 8.2.4 Outlier Detection
  • 8.2.5 Regression
  • 8.2.6 Visualisation
  • 8.3 Financial Fraud
  • 8.3.1 Financial Statements
  • 8.3.2 Financial Statement Fraud Detection
  • 8.4 Deep Learning Techniques for Financial Accounting Fraud Detection
  • 8.4.1 Convolutional Neural Networks
  • 8.4.2 Long Short-Term Memory Networks
  • 8.4.3 Recurrent Neural Networks
  • 8.4.4 Generative Adversarial Networks
  • 8.4.5 Radial Basis Function Networks
  • 8.4.6 Multilayer Perceptron
  • 8.4.7 Self-Organising Maps
  • 8.4.8 Deep Belief Networks
  • 8.4.9 Restricted Boltzmann Machines
  • 8.4.10 Autoencoders
  • 8.5 Analysis of Existing Techniques
  • 8.6 Conclusion and Future Scope
  • Section 4 Internet of Things for Smart Cities
  • 9 Traffic Control Using IoT Technologies and LoRaWAN for Smart Cities
  • 9.1 Introduction
  • 9.2 Related Works
  • 9.2.1 Our Contributions
  • 9.3 Technical Overview of LoRaWAN
  • 9.3.1 Internet of Things
  • 9.3.2 Role of IoT Technologies
  • 9.3.3 LoRa Overview
  • 9.3.4 Low-Power Wide Area Network
  • 9.3.5 LoRaWAN Architecture
  • 9.4 Addressing Traffic Management Challenges in Smart Cities with LoRaWAN
  • 9.5 The Challenges with LoRaWAN Technology
  • 9.6 System Model and Proposed Solution
  • 9.6.1 LoED
  • 9.6.2 Gateway Details
  • 9.6.3 Comparative Analysis of Gateway Performance for Case Studies 1 and 2 of a Smart Parking System
  • 9.7 Numerical Results and Discussion
  • 9.8 Conclusions and Future Research Directions
  • 10 Using the Internet of Medical Things to Self-Monitor Vital Parameters
  • 10.1 Introduction
  • 10.1.1 Key Vital Parameters
  • 10.1.2 Wearable Devices for Vital Parameter Monitoring
  • 10.2 Sensors and Techniques to Monitor Key Vital Parameters for Self-Monitoring.
  • 10.3 Data Collection, Analysis, and Interpretation
  • 10.4 Machine Learning and AI in Self-Monitoring
  • 10.5 Data Security and Privacy Challenges in IoMT
  • 10.6 Future Directions and Emerging Trends
  • 10.7 Conclusion
  • Section 5 Smart Grid Technologies and Renewable Energy
  • 11 Renewable Energy Technologies in Smart Cities
  • 11.1 Introduction
  • 11.2 The Concept of Smart Cities
  • 11.3 Energy Management in Smart Cities
  • 11.4 Renewable Energy Technologies
  • 11.4.1 Solar Energy Technologies
  • 11.4.2 Wind Energy Technologies
  • 11.4.3 Geothermal Energy Technologies
  • 11.4.4 Miscellaneous Energy Technologies
  • 11.5 Comparative Study
  • 11.6 Conclusion
  • 11.7 Acknowledgment
  • 12 Optimal Placement of Distributed Energy Generators Using Multiobjective Harmony Search Algorithm for Loss Reduction in Microgrid for Smart Cities
  • 12.1 Introduction
  • 12.1.1 Research Contributions
  • 12.1.2 Organization of Chapter
  • 12.2 The Proposed IEEE 33-Bus Test System
  • 12.3 The Proposed MOHSA
  • 12.3.1 Design of Objective Functions
  • 12.3.2 Constraints
  • 12.3.3 Multiobjective Harmony Search Algorithm
  • 12.4 Performance Indicators
  • 12.4.1 TAPL Reduction
  • 12.4.2 TRPL Reduction
  • 12.4.3 TVD Reduction
  • 12.5 Simulation Results
  • 12.5.1 Microgrid with No Distributed Energy Generators
  • 12.5.2 One DG Unit
  • 12.5.3 Two DG Units
  • 12.5.4 Three DG Units
  • 12.5.5 Four DG Units
  • 12.5.6 Five DG Units
  • 12.5.7 Analysis of Performance Indicators
  • 12.6 Cost-Benefit Analysis and Computation of Payback Period
  • 12.6.1 Loss Savings and Size of DG Plants
  • 12.6.2 Cost-Benefit Analysis
  • 12.6.3 Performance Comparative Study
  • 12.7 Conclusion
  • 12.8 Acknowledgment
  • 13 Integrated Vehicle-to-Grid Control of a Smart City Power Network Microgrid
  • 13.1 Introduction.
  • 13.2 Block Diagram of a Proposed Smart City Microgrid
  • 13.2.1 Wind Power Plant
  • 13.2.2 Solar Power Plant
  • 13.2.3 Diesel Generator
  • 13.2.4 Vehicle-to-Grid Technology
  • 13.2.5 Load
  • 13.2.6 Details of Transformers
  • 13.2.7 Proposed EV Charging Philosophy
  • 13.3 Detailed Description of Design and Implementation of Smart City MG and V2G Technology Control
  • 13.4 Simulation
  • 13.5 Simulation Results and Discussion
  • 13.5.1 Scenario 1
  • 13.5.2 Scenario 2
  • 13.5.3 Scenario 3
  • 13.6 Performance Comparative Study
  • 13.7 Conclusion
  • 13.8 Acknowledgment
  • Section 6 Transmission and Distribution of Electrical Power in Smart Cities
  • 14 Comprehensive Overview of Utility Network Technologies for Smart Cities
  • 14.1 Introduction
  • 14.2 Smart City Microgrid
  • 14.2.1 Microgrid Control Algorithms
  • 14.3 Generation Technology for Smart City Networks
  • 14.3.1 Wind Power Plant
  • 14.3.2 Solar PV Power Plant
  • 14.3.3 Diesel Generator
  • 14.4 Vehicle-to-Grid Technology in Smart City Networks
  • 14.4.1 PQ Detection Technology
  • 14.4.2 PQ Mitigation Technology
  • 14.5 Protection Technology in Smart City Networks
  • 14.6 Smart Metering Technology in Smart City Networks
  • 14.7 Conclusion
  • 14.8 Acknowledgment
  • 15 Smart Power Quality Monitoring System for Smart Cities
  • 15.1 Introduction
  • 15.2 Generation of PQ Disturbances
  • 15.2.1 Voltage Signal without PQ Event
  • 15.2.2 Sag
  • 15.2.3 Swell with Voltage Signal
  • 15.2.4 Momentary Interruption with Voltage Signal
  • 15.2.5 Harmonics with Voltage Signal
  • 15.2.6 Flicker with Voltage Signal
  • 15.2.7 OT with Voltage Signal
  • 15.2.8 IT with Voltage Signal
  • 15.2.9 Notches with Voltage Signal
  • 15.2.10 Spikes with Voltage Signal
  • 15.3 Wavelet Singular Entropy and Stationary Wavelet Transform
  • 15.3.1 Stationary Wavelet Transform
  • 15.3.2 Shannon Entropy.
  • 15.3.3 Singular Value Decomposition.
ISBN
  • 1-003-48693-2
  • 1-04-004081-0
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