Pethuru Raj, PhD is the Chief Architect in the Edge AI division of Reliance Jio Platforms Ltd. He has published more than thirty research papers in peer-reviewed journals, authored and edited forty-two books, and contributed fifty-four book chapters. He focuses on emerging technologies such as the Internet of Things, artificial intelligence model optimization, big and streaming data analytics, and blockchain. Sudesh Yadav, PhD is an Assistant Professor in the Govt. College, Ateli, Distt-Mahendergarh, Haryana, India. She has published and reviewed many research papers in refereed international journals and conferences. Her areas of interest include artificial intelligence, IoT, digital image processing, soft computing and pattern recognition, and natural language processing. Manas Kumar Mishra, PhD is a Professor at the IMS Engineering College, Ghaziabad, Uttar Pradesh, India. He has published more than 80 book chapters and research articles in international journals of repute. His research interests include distributed systems, mobile computing, artificial intelligence, and wireless sensor networks. Satya Prakash Yadav, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India with more than 17 years of experience. He has published four books, two patents, and many research papers in international journals. His research focuses on image processing, information retrieval, digital image processing, feature extraction, information retrieval, C++, C#, and Java. Victor Hugo C. de Albuquerque, PhD is a Professor and Senior Researcher in the Department of Teleinformatics Engineering at the Federal University of Ceará, Brazil. He is a member of the Brazilian Society of Biomedical Engineering. He has experience in biomedical science and engineering, with a focus on applied computing, intelligent systems, visualization and interaction, artificial intelligence, and image processing and analysis.

• Preface xixPart 1: Artificial Intelligence in Solving Urban Planning and Designing Challenges 11 Illustrating the Sustainability, Challenges, and Concerns of Urban Mobility and Smart Cities 3Nilesh Bhosle, Amandeep Kaur, Raman Kumar, Yashwant Singh Bisht and Laith H. Alzubaidi1.1 Introduction 41.1.1 Characteristics of a Smart City 51.2 Smart City 61.2.1 An Overview of Smart Cities 61.2.2 Role of Digitalisation in Smart Cities 61.2.3 Infrastructural Impacts of Digitalisation in Smart Cities 91.3 Smart Mobility in Smart Cities 101.4 Analysis of Security Threats 131.4.1 Mobility Trends in Smart Cities in the Future 141.5 Issues and Opportunities Related to Smart Cities 151.5.1 Challenges for Smart Cities 151.5.2 Trends and Opportunities for the Future 171.6 Conclusions 17References 182 Accentuating Climate Change Adaptation and Vulnerability (CCAV) Challenges 23Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Sanjeev Kumar Shah and Mohemmed Hussien2.1 Introduction 242.1.1 Adapting to Climate Change Vulnerabilities 252.2 Related Work 262.2.1 Spatial Violence 262.2.2 Response to Climate Change 272.3 Key Challenges in Climate Change Adaptation and Vulnerability (CCAV) 282.3.1 Technical Challenges 282.3.2 Financial Constraints 282.3.3 Social and Cultural Barriers 282.3.4 Institutional and Governance Challenges 292.3.5 Multi-Level Governance (MLG) of Climate Change 292.4 Case Studies Highlighting Vulnerability and Adaptation Challenges 302.4.1 Small Island Developing States (SIDS) 302.4.2 Rural Farming Communities in Sub-Saharan Africa 302.4.3 Urban Slums in South Asia 312.5 Strategic Frameworks for Addressing CCAV Challenges 312.5.1 Through Community-Based Approaches 312.5.2 Mobilising Climate Finance and Reducing Funding Barriers 312.5.3 Strengthening Institutional Capacity and Governance Frameworks 322.5.4 Innovating and Leveraging Technology 322.5.5 Insufficient Funding and Resources 322.5.6 Data Gaps and Uncertainty 322.5.7 Insufficient Localised Solutions 332.5.8 Institutional and Policy Challenges 332.5.9 Social and Economic Inequities 332.5.10 Awareness and Engagement of the Public Lacking 332.5.11 Using Fossil Fuels as a Source of Energy 332.5.12 Limitations 342.5.13 Maintaining a Balance Between Short-Term and Long-Term Needs 342.5.14 Adaptation Challenges Based on Ecosystems 342.5.15 Efforts to Monitor and Evaluate Adaptation 342.5.16 Global Coordination and Climate Justice 342.6 Conclusion 35References 353 Delineating the Solution Approaches for Sustainable Urban Mobility 39Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Rajesh Singh and Mohammed Al-Farouni3.1 Introduction 403.2 Related Work 423.3 Materials and Methods 443.3.1 Travel Demand Generation 453.3.2 Traffic Simulation Process 463.4 Results Analysis and Discussion 473.4.1 Amsterdam 473.4.2 Helsinki 493.5 Conclusion 51References 514 About the Growing Power of Artificial Intelligence (AI) and Blockchain for Fleet Management and Sustainable Societies 55Jayant Jagtap, Raman Kumar, Kunal Gagneja, Anita Gehlot and M. Muhsen Hassan4.1 Introduction 564.1.1 Artificial Intelligence and Blockchain 574.1.2 Sustainable Smart City Society 594.2 Literature Survey and Contribution 604.2.1 Privacy and Security Concerns 604.3 Blockchain to Support Smart Cities’ Operations 624.4 Blockchain Benefits 634.5 Types of Blockchain Networks 644.6 Blockchain Suitability 654.7 Conclusion 66References 675 Testifying the Criticality of the Internet of Things (IoT), 5G and AI: A Perfect Combination for Battery Management 71Preeti Rani, Raman Kumar, Amrita Singh, Jayant Jagtap and Muntather Almusawi5.1 Introduction 725.1.1 Energy Management Strategy Description 745.2 Literature Review 745.2.1 Managing an EV Battery Pack 755.2.2 IoT in Battery Management 755.2.3 Wireless BMS Incentive Program 755.2.4 5G as a Catalyst for Rapid Data Transmission 765.2.5 AI and Predictive Analytics in Battery Optimization 765.2.6 Synergy of IoT, 5G, and AI in Battery Management 765.3 The Internet of Things (IoT) in Battery Management 775.3.1 Real-Time Monitoring and Predictive Maintenance 775.3.2 Data Collection and Data-Driven Insights 775.4 5G Connectivity: Enabling High-Speed, Low-Latency Data Exchange 775.4.1 Enhancing Real-Time Decision Making 785.4.2 Scalability of IoT Networks 785.5 Artificial Intelligence (AI): The Brain Behind Smart Battery Management 785.6 BMS’s Goals and Challenges 815.6.1 Optimal Charging 825.6.2 Fast Characterization 835.7 Conclusion 83References 846 Using Local Knowledge and Sustainable Transport for Greener Mobility 89Jayant Jagtap, Amrita Singh, Sandeep Singh, Shivani Pant and Haider Mohammed Abbas6.1 Introduction 906.2 Related Work 926.3 Greening Mobility Necessities 946.3.1 Green Transport Standards 946.4 Principles of the Sustainable Mobility Paradigm 976.5 Conclusion 100References 100Part 2: Green Revolution in IoV 1057 Expounding the Importance of Explainable AI for Greener Transportations 107Abhilasha Jadhav, Amrita Singh, Adil Abbas Alwan, Ruby Pant and Haider Alabdeli7.1 Introduction 1087.2 Related Work 1107.2.1 Why Explainable AI is Needed? 1117.2.2 Evaluation of Explainable-AI (XAI) Frameworks and Results 1137.3 The Need for Explainable AI in Transportation 1157.4 AI’s Potential for Transforming Smart Cities and its Limitations 1167.5 Explainable AI Supports Greener Transportation 1177.5.1 Optimizing Traffic Flow and Reducing Emissions 1177.5.2 Managing and Reducing Fleet Emissions 1177.5.3 Enhancing Predictive Maintenance 1187.5.4 Supporting Autonomous Vehicles and Green Routing 1187.5.5 Facilitating Transparent Data Sharing 1187.6 Benefits of Explainable AI in Greener Transportation 1187.7 Challenges of Implementing Explainable AI in Greener Transportation 1197.8 Conclusion 120References 1208 Demystifying the Aspects of Edge Computing and Edge AI for Real-Time Insights 127Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Ruby Pant, Nidhi Singh and Hassan M. Al-Jawahry8.1 Introduction 1288.1.1 Importance of Real-Time Processing in AI 1298.1.2 A Paradigm for Edge Computing 1318.1.3 Mobile Edge Computing (MEC) 1328.1.3.1 Understanding Edge Computing 1328.1.3.2 The Architecture of Edge Computing 1328.1.4 Advantages of Edge Computing 1338.2 Edge AI 1348.2.1 Decision-Making in Real-Time: Why it’s Important 1358.2.2 Purpose and Scope of the Paper 1358.3 Application of Edge AI in a Variety of Industries 1378.3.1 Manufacturing 1378.4 Edge AI Challenges and Limitations 1388.4.1 Challenges in Technology 1388.5 Future Directions and Trends 1408.5.1 Federated Learning on the Edge 1408.5.2 5G and Edge Synergy 1408.5.3 TinyML for Edge AI 1408.5.4 Integration with Blockchain for Security 1408.6 Conclusion 140References 1419 Elucidating the Strategic Significance of Smart Grids Towards Sustainable Cities 145Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Sanjeev Kumar Shah and Mohammed I. Habelalmateen9.1 Introduction 1469.2 Related Work 1499.3 Smart Grids as a Catalyst for Sustainability in Urban Environments 1549.4 Smart Grid Technologies: Enabling Real-Time Decision Making 1559.5 Challenges in Implementing Smart Grids for Sustainable Cities 1569.6 Case Studies: Smart Grid Implementation in Sustainable Cities 1569.7 Conclusion 157References 15710 Describing the Needs for Connected Electric Vehicles for Better Air Quality 161Shivakrishna Dasi, Jasgurpreet Singh Chohan, Saroj Kumar Gupta, Rajesh Singh and Myasar Mundher Adnan10.1 Introduction 16210.2 Related Work 16410.2.1 Battery Electric Vehicles 16510.3 Performance Aspects of CAEVs 16610.3.1 Autonomous Vehicles 16710.3.2 Connected Vehicles 16810.3.3 Electric Vehicles 16910.4 The Impact of Air Quality on Environmental Justice (EJ) 17010.4.1 Data Collection and Setup of Air Quality Modeling Systems 17010.5 CAV Taxonomy Based on Performance 17010.5.1 Connected and Autonomous Electric Vehicles (CAEVs) 17110.5.2 The Quality of Experience Framework for CAEVs 17210.6 Conclusion 173References 17311 Distilling the Convergence of AI and EVs Towards Self-Driving EVs 177Shivakrishna Dasi, Heena Madan, Mohammed Y. Al-khuzaie, Anita Gehlot and Ramy Riad Al-Fatlawy11.1 Introduction 17811.1.1 The State of Electric Vehicles (EVs) Today 18111.2 Related Work 18111.2.1 AI as the Backbone of Self-Driving Technology 18211.2.2 Machine Learning and Computer Vision 18211.2.3 Deep Reinforcement Learning 18211.3 The Convergence of AI and EVs: Key Enablers for Self-Driving EVs 18411.3.1 Technical Challenges in the Path Towards Self-Driving EVs 18511.4 The Impact of Self-Driving EVs on Society and the Environment 18611.5 The Impact of Self-Driving Vehicles on the Environment 18911.6 Conclusion 190References 19112 Explaining the Distinct Functionalities of Battery Management Systems (BMS) 197Hawraa Ali Sabah, Shivakrishna Dasi, Jaspreet Kaur, Devendra Singh and Ahmad Radee Alawadi12.1 Introduction 19812.2 Battery Management System (BMS) 19912.3 An Overview of Components and Topologies 20212.3.1 Software Architecture 20312.3.2 Functionalities 20412.4 Battery Models 20512.4.1 Thermal Modeling 20512.4.2 Electrical Modeling 20712.5 Monitoring the Stack 20812.5.1 Batteries for Grid Storage 20912.5.2 A Modeling Approach to Lithium-Ion Batteries 20912.5.3 Advanced Model-Based BMSs 21012.6 State of Charge Estimation 21012.6.1 The Need for BMS in Smart Grids and EVs 21112.6.2 Challenges of BMS and Possible Solutions 21112.7 Conclusion 211References 21213 Detailing How AI Empowers Battery Management Systems 215Umesh Chandra Garjola, Ashish Singh, Hawraa Ali Sabah, Jaspreet Kaur and Zaid Alsalami13.1 Introduction 21613.2 Systems for Managing Batteries 21813.2.1 Structure of Elements and Arrangements 21913.2.2 Structure of Battery-Management System 22113.2.3 System Functions to Manage Batteries 22113.2.4 Impacts of Battery-Management Systems 22213.2.5 A Study of How AI Can Be Applied to Smart Grids and Renewable Energy 22213.3 Traditionally, BMS Has Faced Many Challenges 22413.4 AI in Business Management Systems 22513.4.1 Calculation of State of Charge (SoC) and State of Health (SoH) 22513.4.2 Balancing and Controlling the Temperature of Cells 22513.4.3 Predicting and Diagnosing Faults 22513.4.4 Optimizing Energy Efficiency and Extending the Range 22613.5 BMS Powered by Artificial Intelligence 22613.5.1 Machine Learning (ML) and Deep Learning (DL) 22613.5.2 Reinforcement Learning (RL) 22613.5.3 An Algorithm for Detecting Anomalies 22713.5.4 Digital Twins 22713.6 BMS with AI Enhancements: Benefits 22713.6.1 BMS Integration with AI Offers Numerous Benefits 22713.6.2 Future Trends and Challenges 22713.6.3 Future Prospects 22813.7 Conclusion 228References 228Part 3: Infrastructure Optimization in EV 23314 Insisting for Electric Vehicle (EV) Charging Infrastructure Management Systems 235Jasgurpreet Singh Chohan, Ashish Singh, Jaspreet Kaur, Ruby Pant and Kassem AL-Attabi14.1 Introduction 23614.2 The Need for EV Charging Infrastructure Management Systems 23814.2.1 User Demand for Convenience 23914.2.2 Utility and Energy Load Management 23914.2.3 Integration with Renewable Energy Sources 23914.3 Overview of the Charging Infrastructure for Electric Vehicles 23914.3.1 Equipment Specifications for Electric Vehicles 23914.3.2 Standards for Interoperable EV Charging 24114.4 Model Overview 24214.4.1 Vehicle Fleet 24314.4.2 Deployment of Electric Vehicle Charging Infrastructure 24414.5 Hotspot-Based EVCS 24614.6 Key Features of EV Charging Infrastructure Management Systems 24614.6.1 Smart Charging and Load Balancing 24614.6.2 Data Collection and Predictive Maintenance 24714.6.3 Dynamic Pricing and User Management 24714.6.4 Integration with Mobile Applications 24714.6.5 Grid Interaction and Energy Storage 24714.6.6 Scalability and Flexibility 24714.7 Challenges in Implementing EV Charging Infrastructure Management Systems 24814.7.1 High Initial Investment Costs 24814.7.2 Data Security and Privacy 24814.7.3 Interoperability and Standardization 24814.7.4 Grid Reliability and Capacity 24814.8 Future Directions and Innovations in EV Charging Infrastructure Management 24814.8.1 AI and Machine Learning for Predictive Optimization 24914.8.2 Blockchain for Secure Transactions 24914.8.3 Ultra-Fast and Wireless Charging 24914.9 Conclusion 249References 25015 Illuminating the AIs Role in Shaping Up EV Charging Infrastructures 253Jatinder Kumar, Ashish Singh, Hawraa Ali Sabah, Yashwant Singh Bisht and Laith H. Jasim15.1 Electro Mobility Charging Systems 25415.2 Literature Review 25615.3 Electric Vehicle Charging Infrastructure 25715.3.1 Infrastructural Types of Charging 25815.4 Optimizing the Charging Infrastructure Using Artificial Intelligence 25915.4.1 Predicting Charging Demand with Data Analytics 26015.4.2 Managing Dynamic Charges with AI 26015.4.3 Planned Infrastructure Optimization Algorithms 26115.5 Charging Intelligent Infrastructures 26215.5.1 The Challenges of Developing EV Charging Infrastructure 26215.5.2 Predicting Demand and Selecting Sites with AI 26215.5.3 Managing and Balancing Loads in Real Time 26315.5.4 The Integration of Renewable Energy Sources with AI 26315.5.5 Infrastructural Challenges and Considerations in AI-Driven Charging 26415.5.6 The Future of AI in EV Charging Infrastructure 26415.6 Conclusion 265References 26516 Deciphering Smart Grid Integration and Energy Management 269Jatinder Kumar, Protyay Dey, Jasgurpreet Singh Chohan, Sanjeev Kumar Shah and Laith Jasim16.1 Introduction 27016.1.1 Smart Grid Systems 27116.1.2 Energy Management System 27216.1.3 System for Managing Transmission Energy 27316.2 A Smart Grid EMS Based on Communication Technologies 27516.2.1 Gprs 27516.2.2 WiMAX (IEEE 802.16) 27616.2.3 Bluetooth (IEEE 802.15) 27616.2.4 Power Line Communication (PLC) 27616.3 Smart Grids: An Overview 27716.3.1 An Overview of Smart Grid Components 27716.3.2 Goals of a Smart Grid 27816.4 Integrating Smart Grids with Existing Infrastructure 27816.4.1 Upgrading Infrastructure 27816.4.2 Synchronizing with Renewable Sources 27916.4.3 Digitalizing the Grid 27916.4.4 Cybersecurity Measures 27916.5 Energy Management in the Smart Grid 27916.5.1 Demand Response 27916.5.2 Distributed Energy Resources Management (derm) 28016.5.3 Energy Storage Solutions 28016.5.4 Rates and Pricing for Real-Time Usage 28016.5.5 Electric Vehicle (EV) Integration 28016.6 Managing Energy and Integrating Smart Grids 28016.7 Conclusion 281References 28217 Decoding the Aspects of Intelligent Traffic Management 287Preeti Rani, Jatinder Kumar, Sandeep Singh, Protyay Dey and Laith H. Jasim17.1 Introduction 28817.2 Related Work 29017.3 Proposed Methodology 29217.3.1 Design Objectives 29217.3.2 Method and Materials 29317.4 ITS Applications in Various Transport Sectors 29517.4.1 Transportation Industry 29617.4.2 Low CE of Urban Transportation 29617.4.3 Road Traffic Transportation Infrastructure 29617.5 Result and Discussion 29717.6 Conclusion 298References 29918 Exploring the Impact of Computer Vision in Smart Transportation 301Umesh Chandra Garjola, Sandeep Singh, Kamaljeet Kaur, Protyay Dey and Laith Hussein18.1 Introduction 30218.1.1 Surveillance Systems Along Roadsides: An Overview 30218.2 Related Work 30318.2.1 Computer Vision Functions 30318.3 Proposed Methodology 30818.3.1 ACF Object Detection System 30918.3.2 Point Tracker Algorithm 30918.3.3 Intelligent Transportation Systems: Computer Vision Applications 30918.3.4 Intelligent Transportation Systems and Machine Learning (ML) 31018.3.4.1 Machine Learning: The Evolution 31118.3.4.2 Challenges 31318.4 Result and Discussion 31418.5 Conclusion 317References 31719 Exposing the Importance of Connected Lighting for Urban Sustainability 323Zainab. R. Abdulsada, Kamaljeet Kaur, Sapna Singh, Devendra Singh and Mohammed H. Al-Farouni19.1 Introduction 32419.2 Sustainability 32619.3 Transdisciplinary Framework for Urban Lighting Research: Actors, Framework, and Four Steps 32719.4 Understanding Connected Lighting Systems 33019.5 Energy Efficiency and Reduced Carbon Emissions 33019.6 Conclusion 332References 33320 Responsible and Green AI for Environment Sustainability 337Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant and Rami Riad Hussien20.1 Introduction 33820.2 AI and the Environment 33920.2.1 Green-by AI 34020.2.2 Green-in AI 34220.3 Principles of Responsible AI 34420.4 Sustainable AI for Human and Planetary Flourishing 34520.5 AI for Environmental Sustainability 34820.5.1 Climate Prediction and Disaster Management 34820.5.2 Precision Agriculture 34820.5.3 Wildlife Conservation and Biodiversity 34820.5.4 Renewable Energy Optimisation 34820.6 Challenges and Future Directions 34920.7 Conclusion 349References 34921 Integrating AI into Mobility as a Service (MaaS): The Future of Urban Transportation 355Kunal Gagneja, Zainab. R. Abdulsada, Sapna Singh, Ruby Pant and Ramy Al-Fatlawy21.1 Introduction 35621.1.1 The MaaS Concept 35621.2 Mobility in Rural Areas is a Problem 35921.3 Transportation Systems and Artificial Intelligence: A Critical Review 36021.3.1 Artificial Intelligence-Assisted Smart Cities 36021.3.2 AI Applications Currently in Use 36321.3.3 Identifying Research Gaps 36521.3.4 Sustainability Implications of Apps 36521.3.5 The Impact of Urban Development on the Environment 36621.4 Encounters 36721.4.1 Challenges in Knowledge 36721.5 Expected Early Adopter and Users 36821.6 Conclusion 371References 371Index 375

Pethuru Raj, PhD is the Chief Architect in the Edge AI division of Reliance Jio Platforms Ltd. He has published more than thirty research papers in peer-reviewed journals, authored and edited forty-two books, and contributed fifty-four book chapters. He focuses on emerging technologies such as the Internet of Things, artificial intelligence model optimization, big and streaming data analytics, and blockchain. Sudesh Yadav, PhD is an Assistant Professor in the Govt. College, Ateli, Distt-Mahendergarh, Haryana, India. She has published and reviewed many research papers in refereed international journals and conferences. Her areas of interest include artificial intelligence, IoT, digital image processing, soft computing and pattern recognition, and natural language processing. Manas Kumar Mishra, PhD is a Professor at the IMS Engineering College, Ghaziabad, Uttar Pradesh, India. He has published more than 80 book chapters and research articles in international journals of repute. His research interests include distributed systems, mobile computing, artificial intelligence, and wireless sensor networks. Satya Prakash Yadav, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India with more than 17 years of experience. He has published four books, two patents, and many research papers in international journals. His research focuses on image processing, information retrieval, digital image processing, feature extraction, information retrieval, C++, C#, and Java. Victor Hugo C. de Albuquerque, PhD is a Professor and Senior Researcher in the Department of Teleinformatics Engineering at the Federal University of Ceará, Brazil. He is a member of the Brazilian Society of Biomedical Engineering. He has experience in biomedical science and engineering, with a focus on applied computing, intelligent systems, visualization and interaction, artificial intelligence, and image processing and analysis.

• Preface xixPart 1: Artificial Intelligence in Solving Urban Planning and Designing Challenges 11 Illustrating the Sustainability, Challenges, and Concerns of Urban Mobility and Smart Cities 3Nilesh Bhosle, Amandeep Kaur, Raman Kumar, Yashwant Singh Bisht and Laith H. Alzubaidi1.1 Introduction 41.1.1 Characteristics of a Smart City 51.2 Smart City 61.2.1 An Overview of Smart Cities 61.2.2 Role of Digitalisation in Smart Cities 61.2.3 Infrastructural Impacts of Digitalisation in Smart Cities 91.3 Smart Mobility in Smart Cities 101.4 Analysis of Security Threats 131.4.1 Mobility Trends in Smart Cities in the Future 141.5 Issues and Opportunities Related to Smart Cities 151.5.1 Challenges for Smart Cities 151.5.2 Trends and Opportunities for the Future 171.6 Conclusions 17References 182 Accentuating Climate Change Adaptation and Vulnerability (CCAV) Challenges 23Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Sanjeev Kumar Shah and Mohemmed Hussien2.1 Introduction 242.1.1 Adapting to Climate Change Vulnerabilities 252.2 Related Work 262.2.1 Spatial Violence 262.2.2 Response to Climate Change 272.3 Key Challenges in Climate Change Adaptation and Vulnerability (CCAV) 282.3.1 Technical Challenges 282.3.2 Financial Constraints 282.3.3 Social and Cultural Barriers 282.3.4 Institutional and Governance Challenges 292.3.5 Multi-Level Governance (MLG) of Climate Change 292.4 Case Studies Highlighting Vulnerability and Adaptation Challenges 302.4.1 Small Island Developing States (SIDS) 302.4.2 Rural Farming Communities in Sub-Saharan Africa 302.4.3 Urban Slums in South Asia 312.5 Strategic Frameworks for Addressing CCAV Challenges 312.5.1 Through Community-Based Approaches 312.5.2 Mobilising Climate Finance and Reducing Funding Barriers 312.5.3 Strengthening Institutional Capacity and Governance Frameworks 322.5.4 Innovating and Leveraging Technology 322.5.5 Insufficient Funding and Resources 322.5.6 Data Gaps and Uncertainty 322.5.7 Insufficient Localised Solutions 332.5.8 Institutional and Policy Challenges 332.5.9 Social and Economic Inequities 332.5.10 Awareness and Engagement of the Public Lacking 332.5.11 Using Fossil Fuels as a Source of Energy 332.5.12 Limitations 342.5.13 Maintaining a Balance Between Short-Term and Long-Term Needs 342.5.14 Adaptation Challenges Based on Ecosystems 342.5.15 Efforts to Monitor and Evaluate Adaptation 342.5.16 Global Coordination and Climate Justice 342.6 Conclusion 35References 353 Delineating the Solution Approaches for Sustainable Urban Mobility 39Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Rajesh Singh and Mohammed Al-Farouni3.1 Introduction 403.2 Related Work 423.3 Materials and Methods 443.3.1 Travel Demand Generation 453.3.2 Traffic Simulation Process 463.4 Results Analysis and Discussion 473.4.1 Amsterdam 473.4.2 Helsinki 493.5 Conclusion 51References 514 About the Growing Power of Artificial Intelligence (AI) and Blockchain for Fleet Management and Sustainable Societies 55Jayant Jagtap, Raman Kumar, Kunal Gagneja, Anita Gehlot and M. Muhsen Hassan4.1 Introduction 564.1.1 Artificial Intelligence and Blockchain 574.1.2 Sustainable Smart City Society 594.2 Literature Survey and Contribution 604.2.1 Privacy and Security Concerns 604.3 Blockchain to Support Smart Cities’ Operations 624.4 Blockchain Benefits 634.5 Types of Blockchain Networks 644.6 Blockchain Suitability 654.7 Conclusion 66References 675 Testifying the Criticality of the Internet of Things (IoT), 5G and AI: A Perfect Combination for Battery Management 71Preeti Rani, Raman Kumar, Amrita Singh, Jayant Jagtap and Muntather Almusawi5.1 Introduction 725.1.1 Energy Management Strategy Description 745.2 Literature Review 745.2.1 Managing an EV Battery Pack 755.2.2 IoT in Battery Management 755.2.3 Wireless BMS Incentive Program 755.2.4 5G as a Catalyst for Rapid Data Transmission 765.2.5 AI and Predictive Analytics in Battery Optimization 765.2.6 Synergy of IoT, 5G, and AI in Battery Management 765.3 The Internet of Things (IoT) in Battery Management 775.3.1 Real-Time Monitoring and Predictive Maintenance 775.3.2 Data Collection and Data-Driven Insights 775.4 5G Connectivity: Enabling High-Speed, Low-Latency Data Exchange 775.4.1 Enhancing Real-Time Decision Making 785.4.2 Scalability of IoT Networks 785.5 Artificial Intelligence (AI): The Brain Behind Smart Battery Management 785.6 BMS’s Goals and Challenges 815.6.1 Optimal Charging 825.6.2 Fast Characterization 835.7 Conclusion 83References 846 Using Local Knowledge and Sustainable Transport for Greener Mobility 89Jayant Jagtap, Amrita Singh, Sandeep Singh, Shivani Pant and Haider Mohammed Abbas6.1 Introduction 906.2 Related Work 926.3 Greening Mobility Necessities 946.3.1 Green Transport Standards 946.4 Principles of the Sustainable Mobility Paradigm 976.5 Conclusion 100References 100Part 2: Green Revolution in IoV 1057 Expounding the Importance of Explainable AI for Greener Transportations 107Abhilasha Jadhav, Amrita Singh, Adil Abbas Alwan, Ruby Pant and Haider Alabdeli7.1 Introduction 1087.2 Related Work 1107.2.1 Why Explainable AI is Needed? 1117.2.2 Evaluation of Explainable-AI (XAI) Frameworks and Results 1137.3 The Need for Explainable AI in Transportation 1157.4 AI’s Potential for Transforming Smart Cities and its Limitations 1167.5 Explainable AI Supports Greener Transportation 1177.5.1 Optimizing Traffic Flow and Reducing Emissions 1177.5.2 Managing and Reducing Fleet Emissions 1177.5.3 Enhancing Predictive Maintenance 1187.5.4 Supporting Autonomous Vehicles and Green Routing 1187.5.5 Facilitating Transparent Data Sharing 1187.6 Benefits of Explainable AI in Greener Transportation 1187.7 Challenges of Implementing Explainable AI in Greener Transportation 1197.8 Conclusion 120References 1208 Demystifying the Aspects of Edge Computing and Edge AI for Real-Time Insights 127Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Ruby Pant, Nidhi Singh and Hassan M. Al-Jawahry8.1 Introduction 1288.1.1 Importance of Real-Time Processing in AI 1298.1.2 A Paradigm for Edge Computing 1318.1.3 Mobile Edge Computing (MEC) 1328.1.3.1 Understanding Edge Computing 1328.1.3.2 The Architecture of Edge Computing 1328.1.4 Advantages of Edge Computing 1338.2 Edge AI 1348.2.1 Decision-Making in Real-Time: Why it’s Important 1358.2.2 Purpose and Scope of the Paper 1358.3 Application of Edge AI in a Variety of Industries 1378.3.1 Manufacturing 1378.4 Edge AI Challenges and Limitations 1388.4.1 Challenges in Technology 1388.5 Future Directions and Trends 1408.5.1 Federated Learning on the Edge 1408.5.2 5G and Edge Synergy 1408.5.3 TinyML for Edge AI 1408.5.4 Integration with Blockchain for Security 1408.6 Conclusion 140References 1419 Elucidating the Strategic Significance of Smart Grids Towards Sustainable Cities 145Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Sanjeev Kumar Shah and Mohammed I. Habelalmateen9.1 Introduction 1469.2 Related Work 1499.3 Smart Grids as a Catalyst for Sustainability in Urban Environments 1549.4 Smart Grid Technologies: Enabling Real-Time Decision Making 1559.5 Challenges in Implementing Smart Grids for Sustainable Cities 1569.6 Case Studies: Smart Grid Implementation in Sustainable Cities 1569.7 Conclusion 157References 15710 Describing the Needs for Connected Electric Vehicles for Better Air Quality 161Shivakrishna Dasi, Jasgurpreet Singh Chohan, Saroj Kumar Gupta, Rajesh Singh and Myasar Mundher Adnan10.1 Introduction 16210.2 Related Work 16410.2.1 Battery Electric Vehicles 16510.3 Performance Aspects of CAEVs 16610.3.1 Autonomous Vehicles 16710.3.2 Connected Vehicles 16810.3.3 Electric Vehicles 16910.4 The Impact of Air Quality on Environmental Justice (EJ) 17010.4.1 Data Collection and Setup of Air Quality Modeling Systems 17010.5 CAV Taxonomy Based on Performance 17010.5.1 Connected and Autonomous Electric Vehicles (CAEVs) 17110.5.2 The Quality of Experience Framework for CAEVs 17210.6 Conclusion 173References 17311 Distilling the Convergence of AI and EVs Towards Self-Driving EVs 177Shivakrishna Dasi, Heena Madan, Mohammed Y. Al-khuzaie, Anita Gehlot and Ramy Riad Al-Fatlawy11.1 Introduction 17811.1.1 The State of Electric Vehicles (EVs) Today 18111.2 Related Work 18111.2.1 AI as the Backbone of Self-Driving Technology 18211.2.2 Machine Learning and Computer Vision 18211.2.3 Deep Reinforcement Learning 18211.3 The Convergence of AI and EVs: Key Enablers for Self-Driving EVs 18411.3.1 Technical Challenges in the Path Towards Self-Driving EVs 18511.4 The Impact of Self-Driving EVs on Society and the Environment 18611.5 The Impact of Self-Driving Vehicles on the Environment 18911.6 Conclusion 190References 19112 Explaining the Distinct Functionalities of Battery Management Systems (BMS) 197Hawraa Ali Sabah, Shivakrishna Dasi, Jaspreet Kaur, Devendra Singh and Ahmad Radee Alawadi12.1 Introduction 19812.2 Battery Management System (BMS) 19912.3 An Overview of Components and Topologies 20212.3.1 Software Architecture 20312.3.2 Functionalities 20412.4 Battery Models 20512.4.1 Thermal Modeling 20512.4.2 Electrical Modeling 20712.5 Monitoring the Stack 20812.5.1 Batteries for Grid Storage 20912.5.2 A Modeling Approach to Lithium-Ion Batteries 20912.5.3 Advanced Model-Based BMSs 21012.6 State of Charge Estimation 21012.6.1 The Need for BMS in Smart Grids and EVs 21112.6.2 Challenges of BMS and Possible Solutions 21112.7 Conclusion 211References 21213 Detailing How AI Empowers Battery Management Systems 215Umesh Chandra Garjola, Ashish Singh, Hawraa Ali Sabah, Jaspreet Kaur and Zaid Alsalami13.1 Introduction 21613.2 Systems for Managing Batteries 21813.2.1 Structure of Elements and Arrangements 21913.2.2 Structure of Battery-Management System 22113.2.3 System Functions to Manage Batteries 22113.2.4 Impacts of Battery-Management Systems 22213.2.5 A Study of How AI Can Be Applied to Smart Grids and Renewable Energy 22213.3 Traditionally, BMS Has Faced Many Challenges 22413.4 AI in Business Management Systems 22513.4.1 Calculation of State of Charge (SoC) and State of Health (SoH) 22513.4.2 Balancing and Controlling the Temperature of Cells 22513.4.3 Predicting and Diagnosing Faults 22513.4.4 Optimizing Energy Efficiency and Extending the Range 22613.5 BMS Powered by Artificial Intelligence 22613.5.1 Machine Learning (ML) and Deep Learning (DL) 22613.5.2 Reinforcement Learning (RL) 22613.5.3 An Algorithm for Detecting Anomalies 22713.5.4 Digital Twins 22713.6 BMS with AI Enhancements: Benefits 22713.6.1 BMS Integration with AI Offers Numerous Benefits 22713.6.2 Future Trends and Challenges 22713.6.3 Future Prospects 22813.7 Conclusion 228References 228Part 3: Infrastructure Optimization in EV 23314 Insisting for Electric Vehicle (EV) Charging Infrastructure Management Systems 235Jasgurpreet Singh Chohan, Ashish Singh, Jaspreet Kaur, Ruby Pant and Kassem AL-Attabi14.1 Introduction 23614.2 The Need for EV Charging Infrastructure Management Systems 23814.2.1 User Demand for Convenience 23914.2.2 Utility and Energy Load Management 23914.2.3 Integration with Renewable Energy Sources 23914.3 Overview of the Charging Infrastructure for Electric Vehicles 23914.3.1 Equipment Specifications for Electric Vehicles 23914.3.2 Standards for Interoperable EV Charging 24114.4 Model Overview 24214.4.1 Vehicle Fleet 24314.4.2 Deployment of Electric Vehicle Charging Infrastructure 24414.5 Hotspot-Based EVCS 24614.6 Key Features of EV Charging Infrastructure Management Systems 24614.6.1 Smart Charging and Load Balancing 24614.6.2 Data Collection and Predictive Maintenance 24714.6.3 Dynamic Pricing and User Management 24714.6.4 Integration with Mobile Applications 24714.6.5 Grid Interaction and Energy Storage 24714.6.6 Scalability and Flexibility 24714.7 Challenges in Implementing EV Charging Infrastructure Management Systems 24814.7.1 High Initial Investment Costs 24814.7.2 Data Security and Privacy 24814.7.3 Interoperability and Standardization 24814.7.4 Grid Reliability and Capacity 24814.8 Future Directions and Innovations in EV Charging Infrastructure Management 24814.8.1 AI and Machine Learning for Predictive Optimization 24914.8.2 Blockchain for Secure Transactions 24914.8.3 Ultra-Fast and Wireless Charging 24914.9 Conclusion 249References 25015 Illuminating the AIs Role in Shaping Up EV Charging Infrastructures 253Jatinder Kumar, Ashish Singh, Hawraa Ali Sabah, Yashwant Singh Bisht and Laith H. Jasim15.1 Electro Mobility Charging Systems 25415.2 Literature Review 25615.3 Electric Vehicle Charging Infrastructure 25715.3.1 Infrastructural Types of Charging 25815.4 Optimizing the Charging Infrastructure Using Artificial Intelligence 25915.4.1 Predicting Charging Demand with Data Analytics 26015.4.2 Managing Dynamic Charges with AI 26015.4.3 Planned Infrastructure Optimization Algorithms 26115.5 Charging Intelligent Infrastructures 26215.5.1 The Challenges of Developing EV Charging Infrastructure 26215.5.2 Predicting Demand and Selecting Sites with AI 26215.5.3 Managing and Balancing Loads in Real Time 26315.5.4 The Integration of Renewable Energy Sources with AI 26315.5.5 Infrastructural Challenges and Considerations in AI-Driven Charging 26415.5.6 The Future of AI in EV Charging Infrastructure 26415.6 Conclusion 265References 26516 Deciphering Smart Grid Integration and Energy Management 269Jatinder Kumar, Protyay Dey, Jasgurpreet Singh Chohan, Sanjeev Kumar Shah and Laith Jasim16.1 Introduction 27016.1.1 Smart Grid Systems 27116.1.2 Energy Management System 27216.1.3 System for Managing Transmission Energy 27316.2 A Smart Grid EMS Based on Communication Technologies 27516.2.1 Gprs 27516.2.2 WiMAX (IEEE 802.16) 27616.2.3 Bluetooth (IEEE 802.15) 27616.2.4 Power Line Communication (PLC) 27616.3 Smart Grids: An Overview 27716.3.1 An Overview of Smart Grid Components 27716.3.2 Goals of a Smart Grid 27816.4 Integrating Smart Grids with Existing Infrastructure 27816.4.1 Upgrading Infrastructure 27816.4.2 Synchronizing with Renewable Sources 27916.4.3 Digitalizing the Grid 27916.4.4 Cybersecurity Measures 27916.5 Energy Management in the Smart Grid 27916.5.1 Demand Response 27916.5.2 Distributed Energy Resources Management (derm) 28016.5.3 Energy Storage Solutions 28016.5.4 Rates and Pricing for Real-Time Usage 28016.5.5 Electric Vehicle (EV) Integration 28016.6 Managing Energy and Integrating Smart Grids 28016.7 Conclusion 281References 28217 Decoding the Aspects of Intelligent Traffic Management 287Preeti Rani, Jatinder Kumar, Sandeep Singh, Protyay Dey and Laith H. Jasim17.1 Introduction 28817.2 Related Work 29017.3 Proposed Methodology 29217.3.1 Design Objectives 29217.3.2 Method and Materials 29317.4 ITS Applications in Various Transport Sectors 29517.4.1 Transportation Industry 29617.4.2 Low CE of Urban Transportation 29617.4.3 Road Traffic Transportation Infrastructure 29617.5 Result and Discussion 29717.6 Conclusion 298References 29918 Exploring the Impact of Computer Vision in Smart Transportation 301Umesh Chandra Garjola, Sandeep Singh, Kamaljeet Kaur, Protyay Dey and Laith Hussein18.1 Introduction 30218.1.1 Surveillance Systems Along Roadsides: An Overview 30218.2 Related Work 30318.2.1 Computer Vision Functions 30318.3 Proposed Methodology 30818.3.1 ACF Object Detection System 30918.3.2 Point Tracker Algorithm 30918.3.3 Intelligent Transportation Systems: Computer Vision Applications 30918.3.4 Intelligent Transportation Systems and Machine Learning (ML) 31018.3.4.1 Machine Learning: The Evolution 31118.3.4.2 Challenges 31318.4 Result and Discussion 31418.5 Conclusion 317References 31719 Exposing the Importance of Connected Lighting for Urban Sustainability 323Zainab. R. Abdulsada, Kamaljeet Kaur, Sapna Singh, Devendra Singh and Mohammed H. Al-Farouni19.1 Introduction 32419.2 Sustainability 32619.3 Transdisciplinary Framework for Urban Lighting Research: Actors, Framework, and Four Steps 32719.4 Understanding Connected Lighting Systems 33019.5 Energy Efficiency and Reduced Carbon Emissions 33019.6 Conclusion 332References 33320 Responsible and Green AI for Environment Sustainability 337Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant and Rami Riad Hussien20.1 Introduction 33820.2 AI and the Environment 33920.2.1 Green-by AI 34020.2.2 Green-in AI 34220.3 Principles of Responsible AI 34420.4 Sustainable AI for Human and Planetary Flourishing 34520.5 AI for Environmental Sustainability 34820.5.1 Climate Prediction and Disaster Management 34820.5.2 Precision Agriculture 34820.5.3 Wildlife Conservation and Biodiversity 34820.5.4 Renewable Energy Optimisation 34820.6 Challenges and Future Directions 34920.7 Conclusion 349References 34921 Integrating AI into Mobility as a Service (MaaS): The Future of Urban Transportation 355Kunal Gagneja, Zainab. R. Abdulsada, Sapna Singh, Ruby Pant and Ramy Al-Fatlawy21.1 Introduction 35621.1.1 The MaaS Concept 35621.2 Mobility in Rural Areas is a Problem 35921.3 Transportation Systems and Artificial Intelligence: A Critical Review 36021.3.1 Artificial Intelligence-Assisted Smart Cities 36021.3.2 AI Applications Currently in Use 36321.3.3 Identifying Research Gaps 36521.3.4 Sustainability Implications of Apps 36521.3.5 The Impact of Urban Development on the Environment 36621.4 Encounters 36721.4.1 Challenges in Knowledge 36721.5 Expected Early Adopter and Users 36821.6 Conclusion 371References 371Index 375

Pethuru Raj, PhD is the Chief Architect in the Edge AI division of Reliance Jio Platforms Ltd. He has published more than thirty research papers in peer-reviewed journals, authored and edited forty-two books, and contributed fifty-four book chapters. He focuses on emerging technologies such as the Internet of Things, artificial intelligence model optimization, big and streaming data analytics, and blockchain. Sudesh Yadav, PhD is an Assistant Professor in the Govt. College, Ateli, Distt-Mahendergarh, Haryana, India. She has published and reviewed many research papers in refereed international journals and conferences. Her areas of interest include artificial intelligence, IoT, digital image processing, soft computing and pattern recognition, and natural language processing. Manas Kumar Mishra, PhD is a Professor at the IMS Engineering College, Ghaziabad, Uttar Pradesh, India. He has published more than 80 book chapters and research articles in international journals of repute. His research interests include distributed systems, mobile computing, artificial intelligence, and wireless sensor networks. Satya Prakash Yadav, PhD is an Associate Professor in the Department of Computer Science and Engineering at the Madan Mohan Malaviya University of Technology, Gorakhpur, U.P., India with more than 17 years of experience. He has published four books, two patents, and many research papers in international journals. His research focuses on image processing, information retrieval, digital image processing, feature extraction, information retrieval, C++, C#, and Java. Victor Hugo C. de Albuquerque, PhD is a Professor and Senior Researcher in the Department of Teleinformatics Engineering at the Federal University of Ceará, Brazil. He is a member of the Brazilian Society of Biomedical Engineering. He has experience in biomedical science and engineering, with a focus on applied computing, intelligent systems, visualization and interaction, artificial intelligence, and image processing and analysis.

• Preface xixPart 1: Artificial Intelligence in Solving Urban Planning and Designing Challenges 11 Illustrating the Sustainability, Challenges, and Concerns of Urban Mobility and Smart Cities 3Nilesh Bhosle, Amandeep Kaur, Raman Kumar, Yashwant Singh Bisht and Laith H. Alzubaidi1.1 Introduction 41.1.1 Characteristics of a Smart City 51.2 Smart City 61.2.1 An Overview of Smart Cities 61.2.2 Role of Digitalisation in Smart Cities 61.2.3 Infrastructural Impacts of Digitalisation in Smart Cities 91.3 Smart Mobility in Smart Cities 101.4 Analysis of Security Threats 131.4.1 Mobility Trends in Smart Cities in the Future 141.5 Issues and Opportunities Related to Smart Cities 151.5.1 Challenges for Smart Cities 151.5.2 Trends and Opportunities for the Future 171.6 Conclusions 17References 182 Accentuating Climate Change Adaptation and Vulnerability (CCAV) Challenges 23Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Sanjeev Kumar Shah and Mohemmed Hussien2.1 Introduction 242.1.1 Adapting to Climate Change Vulnerabilities 252.2 Related Work 262.2.1 Spatial Violence 262.2.2 Response to Climate Change 272.3 Key Challenges in Climate Change Adaptation and Vulnerability (CCAV) 282.3.1 Technical Challenges 282.3.2 Financial Constraints 282.3.3 Social and Cultural Barriers 282.3.4 Institutional and Governance Challenges 292.3.5 Multi-Level Governance (MLG) of Climate Change 292.4 Case Studies Highlighting Vulnerability and Adaptation Challenges 302.4.1 Small Island Developing States (SIDS) 302.4.2 Rural Farming Communities in Sub-Saharan Africa 302.4.3 Urban Slums in South Asia 312.5 Strategic Frameworks for Addressing CCAV Challenges 312.5.1 Through Community-Based Approaches 312.5.2 Mobilising Climate Finance and Reducing Funding Barriers 312.5.3 Strengthening Institutional Capacity and Governance Frameworks 322.5.4 Innovating and Leveraging Technology 322.5.5 Insufficient Funding and Resources 322.5.6 Data Gaps and Uncertainty 322.5.7 Insufficient Localised Solutions 332.5.8 Institutional and Policy Challenges 332.5.9 Social and Economic Inequities 332.5.10 Awareness and Engagement of the Public Lacking 332.5.11 Using Fossil Fuels as a Source of Energy 332.5.12 Limitations 342.5.13 Maintaining a Balance Between Short-Term and Long-Term Needs 342.5.14 Adaptation Challenges Based on Ecosystems 342.5.15 Efforts to Monitor and Evaluate Adaptation 342.5.16 Global Coordination and Climate Justice 342.6 Conclusion 35References 353 Delineating the Solution Approaches for Sustainable Urban Mobility 39Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Rajesh Singh and Mohammed Al-Farouni3.1 Introduction 403.2 Related Work 423.3 Materials and Methods 443.3.1 Travel Demand Generation 453.3.2 Traffic Simulation Process 463.4 Results Analysis and Discussion 473.4.1 Amsterdam 473.4.2 Helsinki 493.5 Conclusion 51References 514 About the Growing Power of Artificial Intelligence (AI) and Blockchain for Fleet Management and Sustainable Societies 55Jayant Jagtap, Raman Kumar, Kunal Gagneja, Anita Gehlot and M. Muhsen Hassan4.1 Introduction 564.1.1 Artificial Intelligence and Blockchain 574.1.2 Sustainable Smart City Society 594.2 Literature Survey and Contribution 604.2.1 Privacy and Security Concerns 604.3 Blockchain to Support Smart Cities’ Operations 624.4 Blockchain Benefits 634.5 Types of Blockchain Networks 644.6 Blockchain Suitability 654.7 Conclusion 66References 675 Testifying the Criticality of the Internet of Things (IoT), 5G and AI: A Perfect Combination for Battery Management 71Preeti Rani, Raman Kumar, Amrita Singh, Jayant Jagtap and Muntather Almusawi5.1 Introduction 725.1.1 Energy Management Strategy Description 745.2 Literature Review 745.2.1 Managing an EV Battery Pack 755.2.2 IoT in Battery Management 755.2.3 Wireless BMS Incentive Program 755.2.4 5G as a Catalyst for Rapid Data Transmission 765.2.5 AI and Predictive Analytics in Battery Optimization 765.2.6 Synergy of IoT, 5G, and AI in Battery Management 765.3 The Internet of Things (IoT) in Battery Management 775.3.1 Real-Time Monitoring and Predictive Maintenance 775.3.2 Data Collection and Data-Driven Insights 775.4 5G Connectivity: Enabling High-Speed, Low-Latency Data Exchange 775.4.1 Enhancing Real-Time Decision Making 785.4.2 Scalability of IoT Networks 785.5 Artificial Intelligence (AI): The Brain Behind Smart Battery Management 785.6 BMS’s Goals and Challenges 815.6.1 Optimal Charging 825.6.2 Fast Characterization 835.7 Conclusion 83References 846 Using Local Knowledge and Sustainable Transport for Greener Mobility 89Jayant Jagtap, Amrita Singh, Sandeep Singh, Shivani Pant and Haider Mohammed Abbas6.1 Introduction 906.2 Related Work 926.3 Greening Mobility Necessities 946.3.1 Green Transport Standards 946.4 Principles of the Sustainable Mobility Paradigm 976.5 Conclusion 100References 100Part 2: Green Revolution in IoV 1057 Expounding the Importance of Explainable AI for Greener Transportations 107Abhilasha Jadhav, Amrita Singh, Adil Abbas Alwan, Ruby Pant and Haider Alabdeli7.1 Introduction 1087.2 Related Work 1107.2.1 Why Explainable AI is Needed? 1117.2.2 Evaluation of Explainable-AI (XAI) Frameworks and Results 1137.3 The Need for Explainable AI in Transportation 1157.4 AI’s Potential for Transforming Smart Cities and its Limitations 1167.5 Explainable AI Supports Greener Transportation 1177.5.1 Optimizing Traffic Flow and Reducing Emissions 1177.5.2 Managing and Reducing Fleet Emissions 1177.5.3 Enhancing Predictive Maintenance 1187.5.4 Supporting Autonomous Vehicles and Green Routing 1187.5.5 Facilitating Transparent Data Sharing 1187.6 Benefits of Explainable AI in Greener Transportation 1187.7 Challenges of Implementing Explainable AI in Greener Transportation 1197.8 Conclusion 120References 1208 Demystifying the Aspects of Edge Computing and Edge AI for Real-Time Insights 127Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Ruby Pant, Nidhi Singh and Hassan M. Al-Jawahry8.1 Introduction 1288.1.1 Importance of Real-Time Processing in AI 1298.1.2 A Paradigm for Edge Computing 1318.1.3 Mobile Edge Computing (MEC) 1328.1.3.1 Understanding Edge Computing 1328.1.3.2 The Architecture of Edge Computing 1328.1.4 Advantages of Edge Computing 1338.2 Edge AI 1348.2.1 Decision-Making in Real-Time: Why it’s Important 1358.2.2 Purpose and Scope of the Paper 1358.3 Application of Edge AI in a Variety of Industries 1378.3.1 Manufacturing 1378.4 Edge AI Challenges and Limitations 1388.4.1 Challenges in Technology 1388.5 Future Directions and Trends 1408.5.1 Federated Learning on the Edge 1408.5.2 5G and Edge Synergy 1408.5.3 TinyML for Edge AI 1408.5.4 Integration with Blockchain for Security 1408.6 Conclusion 140References 1419 Elucidating the Strategic Significance of Smart Grids Towards Sustainable Cities 145Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Sanjeev Kumar Shah and Mohammed I. Habelalmateen9.1 Introduction 1469.2 Related Work 1499.3 Smart Grids as a Catalyst for Sustainability in Urban Environments 1549.4 Smart Grid Technologies: Enabling Real-Time Decision Making 1559.5 Challenges in Implementing Smart Grids for Sustainable Cities 1569.6 Case Studies: Smart Grid Implementation in Sustainable Cities 1569.7 Conclusion 157References 15710 Describing the Needs for Connected Electric Vehicles for Better Air Quality 161Shivakrishna Dasi, Jasgurpreet Singh Chohan, Saroj Kumar Gupta, Rajesh Singh and Myasar Mundher Adnan10.1 Introduction 16210.2 Related Work 16410.2.1 Battery Electric Vehicles 16510.3 Performance Aspects of CAEVs 16610.3.1 Autonomous Vehicles 16710.3.2 Connected Vehicles 16810.3.3 Electric Vehicles 16910.4 The Impact of Air Quality on Environmental Justice (EJ) 17010.4.1 Data Collection and Setup of Air Quality Modeling Systems 17010.5 CAV Taxonomy Based on Performance 17010.5.1 Connected and Autonomous Electric Vehicles (CAEVs) 17110.5.2 The Quality of Experience Framework for CAEVs 17210.6 Conclusion 173References 17311 Distilling the Convergence of AI and EVs Towards Self-Driving EVs 177Shivakrishna Dasi, Heena Madan, Mohammed Y. Al-khuzaie, Anita Gehlot and Ramy Riad Al-Fatlawy11.1 Introduction 17811.1.1 The State of Electric Vehicles (EVs) Today 18111.2 Related Work 18111.2.1 AI as the Backbone of Self-Driving Technology 18211.2.2 Machine Learning and Computer Vision 18211.2.3 Deep Reinforcement Learning 18211.3 The Convergence of AI and EVs: Key Enablers for Self-Driving EVs 18411.3.1 Technical Challenges in the Path Towards Self-Driving EVs 18511.4 The Impact of Self-Driving EVs on Society and the Environment 18611.5 The Impact of Self-Driving Vehicles on the Environment 18911.6 Conclusion 190References 19112 Explaining the Distinct Functionalities of Battery Management Systems (BMS) 197Hawraa Ali Sabah, Shivakrishna Dasi, Jaspreet Kaur, Devendra Singh and Ahmad Radee Alawadi12.1 Introduction 19812.2 Battery Management System (BMS) 19912.3 An Overview of Components and Topologies 20212.3.1 Software Architecture 20312.3.2 Functionalities 20412.4 Battery Models 20512.4.1 Thermal Modeling 20512.4.2 Electrical Modeling 20712.5 Monitoring the Stack 20812.5.1 Batteries for Grid Storage 20912.5.2 A Modeling Approach to Lithium-Ion Batteries 20912.5.3 Advanced Model-Based BMSs 21012.6 State of Charge Estimation 21012.6.1 The Need for BMS in Smart Grids and EVs 21112.6.2 Challenges of BMS and Possible Solutions 21112.7 Conclusion 211References 21213 Detailing How AI Empowers Battery Management Systems 215Umesh Chandra Garjola, Ashish Singh, Hawraa Ali Sabah, Jaspreet Kaur and Zaid Alsalami13.1 Introduction 21613.2 Systems for Managing Batteries 21813.2.1 Structure of Elements and Arrangements 21913.2.2 Structure of Battery-Management System 22113.2.3 System Functions to Manage Batteries 22113.2.4 Impacts of Battery-Management Systems 22213.2.5 A Study of How AI Can Be Applied to Smart Grids and Renewable Energy 22213.3 Traditionally, BMS Has Faced Many Challenges 22413.4 AI in Business Management Systems 22513.4.1 Calculation of State of Charge (SoC) and State of Health (SoH) 22513.4.2 Balancing and Controlling the Temperature of Cells 22513.4.3 Predicting and Diagnosing Faults 22513.4.4 Optimizing Energy Efficiency and Extending the Range 22613.5 BMS Powered by Artificial Intelligence 22613.5.1 Machine Learning (ML) and Deep Learning (DL) 22613.5.2 Reinforcement Learning (RL) 22613.5.3 An Algorithm for Detecting Anomalies 22713.5.4 Digital Twins 22713.6 BMS with AI Enhancements: Benefits 22713.6.1 BMS Integration with AI Offers Numerous Benefits 22713.6.2 Future Trends and Challenges 22713.6.3 Future Prospects 22813.7 Conclusion 228References 228Part 3: Infrastructure Optimization in EV 23314 Insisting for Electric Vehicle (EV) Charging Infrastructure Management Systems 235Jasgurpreet Singh Chohan, Ashish Singh, Jaspreet Kaur, Ruby Pant and Kassem AL-Attabi14.1 Introduction 23614.2 The Need for EV Charging Infrastructure Management Systems 23814.2.1 User Demand for Convenience 23914.2.2 Utility and Energy Load Management 23914.2.3 Integration with Renewable Energy Sources 23914.3 Overview of the Charging Infrastructure for Electric Vehicles 23914.3.1 Equipment Specifications for Electric Vehicles 23914.3.2 Standards for Interoperable EV Charging 24114.4 Model Overview 24214.4.1 Vehicle Fleet 24314.4.2 Deployment of Electric Vehicle Charging Infrastructure 24414.5 Hotspot-Based EVCS 24614.6 Key Features of EV Charging Infrastructure Management Systems 24614.6.1 Smart Charging and Load Balancing 24614.6.2 Data Collection and Predictive Maintenance 24714.6.3 Dynamic Pricing and User Management 24714.6.4 Integration with Mobile Applications 24714.6.5 Grid Interaction and Energy Storage 24714.6.6 Scalability and Flexibility 24714.7 Challenges in Implementing EV Charging Infrastructure Management Systems 24814.7.1 High Initial Investment Costs 24814.7.2 Data Security and Privacy 24814.7.3 Interoperability and Standardization 24814.7.4 Grid Reliability and Capacity 24814.8 Future Directions and Innovations in EV Charging Infrastructure Management 24814.8.1 AI and Machine Learning for Predictive Optimization 24914.8.2 Blockchain for Secure Transactions 24914.8.3 Ultra-Fast and Wireless Charging 24914.9 Conclusion 249References 25015 Illuminating the AIs Role in Shaping Up EV Charging Infrastructures 253Jatinder Kumar, Ashish Singh, Hawraa Ali Sabah, Yashwant Singh Bisht and Laith H. Jasim15.1 Electro Mobility Charging Systems 25415.2 Literature Review 25615.3 Electric Vehicle Charging Infrastructure 25715.3.1 Infrastructural Types of Charging 25815.4 Optimizing the Charging Infrastructure Using Artificial Intelligence 25915.4.1 Predicting Charging Demand with Data Analytics 26015.4.2 Managing Dynamic Charges with AI 26015.4.3 Planned Infrastructure Optimization Algorithms 26115.5 Charging Intelligent Infrastructures 26215.5.1 The Challenges of Developing EV Charging Infrastructure 26215.5.2 Predicting Demand and Selecting Sites with AI 26215.5.3 Managing and Balancing Loads in Real Time 26315.5.4 The Integration of Renewable Energy Sources with AI 26315.5.5 Infrastructural Challenges and Considerations in AI-Driven Charging 26415.5.6 The Future of AI in EV Charging Infrastructure 26415.6 Conclusion 265References 26516 Deciphering Smart Grid Integration and Energy Management 269Jatinder Kumar, Protyay Dey, Jasgurpreet Singh Chohan, Sanjeev Kumar Shah and Laith Jasim16.1 Introduction 27016.1.1 Smart Grid Systems 27116.1.2 Energy Management System 27216.1.3 System for Managing Transmission Energy 27316.2 A Smart Grid EMS Based on Communication Technologies 27516.2.1 Gprs 27516.2.2 WiMAX (IEEE 802.16) 27616.2.3 Bluetooth (IEEE 802.15) 27616.2.4 Power Line Communication (PLC) 27616.3 Smart Grids: An Overview 27716.3.1 An Overview of Smart Grid Components 27716.3.2 Goals of a Smart Grid 27816.4 Integrating Smart Grids with Existing Infrastructure 27816.4.1 Upgrading Infrastructure 27816.4.2 Synchronizing with Renewable Sources 27916.4.3 Digitalizing the Grid 27916.4.4 Cybersecurity Measures 27916.5 Energy Management in the Smart Grid 27916.5.1 Demand Response 27916.5.2 Distributed Energy Resources Management (derm) 28016.5.3 Energy Storage Solutions 28016.5.4 Rates and Pricing for Real-Time Usage 28016.5.5 Electric Vehicle (EV) Integration 28016.6 Managing Energy and Integrating Smart Grids 28016.7 Conclusion 281References 28217 Decoding the Aspects of Intelligent Traffic Management 287Preeti Rani, Jatinder Kumar, Sandeep Singh, Protyay Dey and Laith H. Jasim17.1 Introduction 28817.2 Related Work 29017.3 Proposed Methodology 29217.3.1 Design Objectives 29217.3.2 Method and Materials 29317.4 ITS Applications in Various Transport Sectors 29517.4.1 Transportation Industry 29617.4.2 Low CE of Urban Transportation 29617.4.3 Road Traffic Transportation Infrastructure 29617.5 Result and Discussion 29717.6 Conclusion 298References 29918 Exploring the Impact of Computer Vision in Smart Transportation 301Umesh Chandra Garjola, Sandeep Singh, Kamaljeet Kaur, Protyay Dey and Laith Hussein18.1 Introduction 30218.1.1 Surveillance Systems Along Roadsides: An Overview 30218.2 Related Work 30318.2.1 Computer Vision Functions 30318.3 Proposed Methodology 30818.3.1 ACF Object Detection System 30918.3.2 Point Tracker Algorithm 30918.3.3 Intelligent Transportation Systems: Computer Vision Applications 30918.3.4 Intelligent Transportation Systems and Machine Learning (ML) 31018.3.4.1 Machine Learning: The Evolution 31118.3.4.2 Challenges 31318.4 Result and Discussion 31418.5 Conclusion 317References 31719 Exposing the Importance of Connected Lighting for Urban Sustainability 323Zainab. R. Abdulsada, Kamaljeet Kaur, Sapna Singh, Devendra Singh and Mohammed H. Al-Farouni19.1 Introduction 32419.2 Sustainability 32619.3 Transdisciplinary Framework for Urban Lighting Research: Actors, Framework, and Four Steps 32719.4 Understanding Connected Lighting Systems 33019.5 Energy Efficiency and Reduced Carbon Emissions 33019.6 Conclusion 332References 33320 Responsible and Green AI for Environment Sustainability 337Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant and Rami Riad Hussien20.1 Introduction 33820.2 AI and the Environment 33920.2.1 Green-by AI 34020.2.2 Green-in AI 34220.3 Principles of Responsible AI 34420.4 Sustainable AI for Human and Planetary Flourishing 34520.5 AI for Environmental Sustainability 34820.5.1 Climate Prediction and Disaster Management 34820.5.2 Precision Agriculture 34820.5.3 Wildlife Conservation and Biodiversity 34820.5.4 Renewable Energy Optimisation 34820.6 Challenges and Future Directions 34920.7 Conclusion 349References 34921 Integrating AI into Mobility as a Service (MaaS): The Future of Urban Transportation 355Kunal Gagneja, Zainab. R. Abdulsada, Sapna Singh, Ruby Pant and Ramy Al-Fatlawy21.1 Introduction 35621.1.1 The MaaS Concept 35621.2 Mobility in Rural Areas is a Problem 35921.3 Transportation Systems and Artificial Intelligence: A Critical Review 36021.3.1 Artificial Intelligence-Assisted Smart Cities 36021.3.2 AI Applications Currently in Use 36321.3.3 Identifying Research Gaps 36521.3.4 Sustainability Implications of Apps 36521.3.5 The Impact of Urban Development on the Environment 36621.4 Encounters 36721.4.1 Challenges in Knowledge 36721.5 Expected Early Adopter and Users 36821.6 Conclusion 371References 371Index 375