Preface xix Part 1: Artificial Intelligence in Solving Urban Planning and Designing Challenges 1 1 Illustrating the Sustainability, Challenges, and Concerns of Urban Mobility and Smart Cities 3 Nilesh Bhosle, Amandeep Kaur, Raman Kumar, Yashwant Singh Bisht and Laith H. Alzubaidi 1.1 Introduction 4 1.1.1 Characteristics of a Smart City 5 1.2 Smart City 6 1.2.1 An Overview of Smart Cities 6 1.

2.2 Role of Digitalisation in Smart Cities 6 1.2.3 Infrastructural Impacts of Digitalisation in Smart Cities 9 1.3 Smart Mobility in Smart Cities 10 1.4 Analysis of Security Threats 13 1.4.1 Mobility Trends in Smart Cities in the Future 14 1.

5 Issues and Opportunities Related to Smart Cities 15 1.5.1 Challenges for Smart Cities 15 1.5.2 Trends and Opportunities for the Future 17 1.6 Conclusions 17 References 18 2 Accentuating Climate Change Adaptation and Vulnerability (CCAV) Challenges 23 Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Sanjeev Kumar Shah and Mohemmed Hussien 2.1 Introduction 24 2.1.

1 Adapting to Climate Change Vulnerabilities 25 2.2 Related Work 26 2.2.1 Spatial Violence 26 2.2.2 Response to Climate Change 27 2.3 Key Challenges in Climate Change Adaptation and Vulnerability (CCAV) 28 2.3.

1 Technical Challenges 28 2.3.2 Financial Constraints 28 2.3.3 Social and Cultural Barriers 28 2.3.4 Institutional and Governance Challenges 29 2.3.

5 Multi-Level Governance (MLG) of Climate Change 29 2.4 Case Studies Highlighting Vulnerability and Adaptation Challenges 30 2.4.1 Small Island Developing States (SIDS) 30 2.4.2 Rural Farming Communities in Sub-Saharan Africa 30 2.4.3 Urban Slums in South Asia 31 2.

5 Strategic Frameworks for Addressing CCAV Challenges 31 2.5.1 Through Community-Based Approaches 31 2.5.2 Mobilising Climate Finance and Reducing Funding Barriers 31 2.5.3 Strengthening Institutional Capacity and Governance Frameworks 32 2.5.

4 Innovating and Leveraging Technology 32 2.5.5 Insufficient Funding and Resources 32 2.5.6 Data Gaps and Uncertainty 32 2.5.7 Insufficient Localised Solutions 33 2.5.

8 Institutional and Policy Challenges 33 2.5.9 Social and Economic Inequities 33 2.5.10 Awareness and Engagement of the Public Lacking 33 2.5.11 Using Fossil Fuels as a Source of Energy 33 2.5.

12 Limitations 34 2.5.13 Maintaining a Balance Between Short-Term and Long-Term Needs 34 2.5.14 Adaptation Challenges Based on Ecosystems 34 2.5.15 Efforts to Monitor and Evaluate Adaptation 34 2.5.

16 Global Coordination and Climate Justice 34 2.6 Conclusion 35 References 35 3 Delineating the Solution Approaches for Sustainable Urban Mobility 39 Adil Abbas Alwan, Amandeep Kaur, Nilesh Bhosle, Rajesh Singh and Mohammed Al-Farouni 3.1 Introduction 40 3.2 Related Work 42 3.3 Materials and Methods 44 3.3.1 Travel Demand Generation 45 3.3.

2 Traffic Simulation Process 46 3.4 Results Analysis and Discussion 47 3.4.1 Amsterdam 47 3.4.2 Helsinki 49 3.5 Conclusion 51 References 51 4 About the Growing Power of Artificial Intelligence (AI) and Blockchain for Fleet Management and Sustainable Societies 55 Jayant Jagtap, Raman Kumar, Kunal Gagneja, Anita Gehlot and M. Muhsen Hassan 4.

1 Introduction 56 4.1.1 Artificial Intelligence and Blockchain 57 4.1.2 Sustainable Smart City Society 59 4.2 Literature Survey and Contribution 60 4.2.1 Privacy and Security Concerns 60 4.

3 Blockchain to Support Smart Cities'' Operations 62 4.4 Blockchain Benefits 63 4.5 Types of Blockchain Networks 64 4.6 Blockchain Suitability 65 4.7 Conclusion 66 References 67 5 Testifying the Criticality of the Internet of Things (IoT), 5G and AI: A Perfect Combination for Battery Management 71 Preeti Rani, Raman Kumar, Amrita Singh, Jayant Jagtap and Muntather Almusawi 5.1 Introduction 72 5.1.1 Energy Management Strategy Description 74 5.

2 Literature Review 74 5.2.1 Managing an EV Battery Pack 75 5.2.2 IoT in Battery Management 75 5.2.3 Wireless BMS Incentive Program 75 5.2.

4 5G as a Catalyst for Rapid Data Transmission 76 5.2.5 AI and Predictive Analytics in Battery Optimization 76 5.2.6 Synergy of IoT, 5G, and AI in Battery Management 76 5.3 The Internet of Things (IoT) in Battery Management 77 5.3.1 Real-Time Monitoring and Predictive Maintenance 77 5.

3.2 Data Collection and Data-Driven Insights 77 5.4 5G Connectivity: Enabling High-Speed, Low-Latency Data Exchange 77 5.4.1 Enhancing Real-Time Decision Making 78 5.4.2 Scalability of IoT Networks 78 5.5 Artificial Intelligence (AI): The Brain Behind Smart Battery Management 78 5.

6 BMS''s Goals and Challenges 81 5.6.1 Optimal Charging 82 5.6.2 Fast Characterization 83 5.7 Conclusion 83 References 84 6 Using Local Knowledge and Sustainable Transport for Greener Mobility 89 Jayant Jagtap, Amrita Singh, Sandeep Singh, Shivani Pant and Haider Mohammed Abbas 6.1 Introduction 90 6.2 Related Work 92 6.

3 Greening Mobility Necessities 94 6.3.1 Green Transport Standards 94 6.4 Principles of the Sustainable Mobility Paradigm 97 6.5 Conclusion 100 References 100 Part 2: Green Revolution in IoV 105 7 Expounding the Importance of Explainable AI for Greener Transportations 107 Abhilasha Jadhav, Amrita Singh, Adil Abbas Alwan, Ruby Pant and Haider Alabdeli 7.1 Introduction 108 7.2 Related Work 110 7.2.

1 Why Explainable AI is Needed? 111 7.2.2 Evaluation of Explainable-AI (XAI) Frameworks and Results 113 7.3 The Need for Explainable AI in Transportation 115 7.4 AI''s Potential for Transforming Smart Cities and its Limitations 116 7.5 Explainable AI Supports Greener Transportation 117 7.5.1 Optimizing Traffic Flow and Reducing Emissions 117 7.

5.2 Managing and Reducing Fleet Emissions 117 7.5.3 Enhancing Predictive Maintenance 118 7.5.4 Supporting Autonomous Vehicles and Green Routing 118 7.5.5 Facilitating Transparent Data Sharing 118 7.

6 Benefits of Explainable AI in Greener Transportation 118 7.7 Challenges of Implementing Explainable AI in Greener Transportation 119 7.8 Conclusion 120 References 120 8 Demystifying the Aspects of Edge Computing and Edge AI for Real-Time Insights 127 Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Ruby Pant, Nidhi Singh and Hassan M. Al-Jawahry 8.1 Introduction 128 8.1.1 Importance of Real-Time Processing in AI 129 8.

1.2 A Paradigm for Edge Computing 131 8.1.3 Mobile Edge Computing (MEC) 132 8.1.3.1 Understanding Edge Computing 132 8.1.

3.2 The Architecture of Edge Computing 132 8.1.4 Advantages of Edge Computing 133 8.2 Edge AI 134 8.2.1 Decision-Making in Real-Time: Why it''s Important 135 8.2.

2 Purpose and Scope of the Paper 135 8.3 Application of Edge AI in a Variety of Industries 137 8.3.1 Manufacturing 137 8.4 Edge AI Challenges and Limitations 138 8.4.1 Challenges in Technology 138 8.5 Future Directions and Trends 140 8.

5.1 Federated Learning on the Edge 140 8.5.2 5G and Edge Synergy 140 8.5.3 TinyML for Edge AI 140 8.5.4 Integration with Blockchain for Security 140 8.

6 Conclusion 140 References 141 9 Elucidating the Strategic Significance of Smart Grids Towards Sustainable Cities 145 Abhilasha Jadhav, Heena Madan, Mohammed Y. Al-khuzaie, Sanjeev Kumar Shah and Mohammed I. Habelalmateen 9.1 Introduction 146 9.2 Related Work 149 9.3 Smart Grids as a Catalyst for Sustainability in Urban Environments 154 9.4 Smart Grid Technologies: Enabling Real-Time Decision Making 155 9.5 Challenges in Implementing Smart Grids for Sustainable Cities 156 9.

6 Case Studies: Smart Grid Implementation in Sustainable Cities 156 9.7 Conclusion 157 References 157 10 Describing the Needs for Connected Electric Vehicles for Better Air Quality 161 Shivakrishna Dasi, Jasgurpreet Singh Chohan, Saroj Kumar Gupta, Rajesh Singh and Myasar Mundher Adnan 10.1 Introduction 162 10.2 Related Work 164 10.2.1 Battery Electric Vehicles 165 10.3 Performance Aspects of CAEVs 166 10.3.

1 Autonomous Vehicles 167 10.3.2 Connected Vehicles 168 10.3.3 Electric Vehicles 169 10.4 The Impact of Air Quality on Environmental Justice (EJ) 170 10.4.1 Data Collection and Setup of Air Quality Modeling Systems 170 10.

5 CAV Taxonomy Based on Performance 170 10.5.1 Connected and Autonomous Electric Vehicles (CAEVs) 171 10.5.2 The Quality of Experience Framework for CAEVs 172 10.6 Conclusion 173 References 173 11 Distilling the Convergence of AI and EVs Towards Self-Driving EVs 177 Shivakrishna Dasi, Heena Madan, Mohammed Y. Al-khuzaie, Anita Gehlot and Ramy Riad Al-Fatlawy 11.1 Introduction 178 11.

1.1 The State of Electric Vehicles (EVs) Today 181 11.2 Related Work 181 11.2.1 AI as the Backbone of Self-Driving Technology 182 11.2.2 Machine Learning and Computer Vision 182 11.2.

3 Deep Reinforcement Learning 182.