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

11.3 The Convergence of AI and EVs: Key Enablers for Self-Driving EVs 184

11.3.1 Technical Challenges in the Path Towards Self-Driving EVs 185

11.4 The Impact of Self-Driving EVs on Society and the Environment 186

11.5 The Impact of Self-Driving Vehicles on the Environment 189

11.6 Conclusion 190

References 191

12 Explaining the Distinct Functionalities of Battery Management Systems (BMS) 197
Hawraa Ali Sabah, Shivakrishna Dasi, Jaspreet Kaur, Devendra Singh and Ahmad Radee Alawadi

12.1 Introduction 198

12.2 Battery Management System (BMS) 199

12.3 An Overview of Components and Topologies 202

12.3.1 Software Architecture 203

12.3.2 Functionalities 204

12.4 Battery Models 205

12.4.1 Thermal Modeling 205

12.4.2 Electrical Modeling 207

12.5 Monitoring the Stack 208

12.5.1 Batteries for Grid Storage 209

12.5.2 A Modeling Approach to Lithium-Ion Batteries 209

12.5.3 Advanced Model-Based BMSs 210

12.6 State of Charge Estimation 210

12.6.1 The Need for BMS in Smart Grids and EVs 211

12.6.2 Challenges of BMS and Possible Solutions 211

12.7 Conclusion 211

References 212

13 Detailing How AI Empowers Battery Management Systems 215
Umesh Chandra Garjola, Ashish Singh, Hawraa Ali Sabah, Jaspreet Kaur and Zaid Alsalami

13.1 Introduction 216

13.2 Systems for Managing Batteries 218

13.2.1 Structure of Elements and Arrangements 219

13.2.2 Structure of Battery-Management System 221

13.2.3 System Functions to Manage Batteries 221

13.2.4 Impacts of Battery-Management Systems 222

13.2.5 A Study of How AI Can Be Applied to Smart Grids and Renewable Energy 222

13.3 Traditionally, BMS Has Faced Many Challenges 224

13.4 AI in Business Management Systems 225

13.4.1 Calculation of State of Charge (SoC) and State of Health (SoH) 225

13.4.2 Balancing and Controlling the Temperature of Cells 225

13.4.3 Predicting and Diagnosing Faults 225

13.4.4 Optimizing Energy Efficiency and Extending the Range 226

13.5 BMS Powered by Artificial Intelligence 226

13.5.1 Machine Learning (ML) and Deep Learning (DL) 226

13.5.2 Reinforcement Learning (RL) 226

13.5.3 An Algorithm for Detecting Anomalies 227

13.5.4 Digital Twins 227

13.6 BMS with AI Enhancements: Benefits 227

13.6.1 BMS Integration with AI Offers Numerous Benefits 227

13.6.2 Future Trends and Challenges 227

13.6.3 Future Prospects 228

13.7 Conclusion 228

References 228

Part 3: Infrastructure Optimization in EV 233

14 Insisting for Electric Vehicle (EV) Charging Infrastructure Management Systems 235
Jasgurpreet Singh Chohan, Ashish Singh, Jaspreet Kaur, Ruby Pant and Kassem AL-Attabi

14.1 Introduction 236

14.2 The Need for EV Charging Infrastructure Management Systems 238

14.2.1 User Demand for Convenience 239

14.2.2 Utility and Energy Load Management 239

14.2.3 Integration with Renewable Energy Sources 239

14.3 Overview of the Charging Infrastructure for Electric Vehicles 239

14.3.1 Equipment Specifications for Electric Vehicles 239

14.3.2 Standards for Interoperable EV Charging 241

14.4 Model Overview 242

14.4.1 Vehicle Fleet 243

14.4.2 Deployment of Electric Vehicle Charging Infrastructure 244

14.5 Hotspot-Based EVCS 246

14.6 Key Features of EV Charging Infrastructure Management Systems 246

14.6.1 Smart Charging and Load Balancing 246

14.6.2 Data Collection and Predictive Maintenance 247

14.6.3 Dynamic Pricing and User Management 247

14.6.4 Integration with Mobile Applications 247

14.6.5 Grid Interaction and Energy Storage 247

14.6.6 Scalability and Flexibility 247

14.7 Challenges in Implementing EV Charging Infrastructure Management Systems 248

14.7.1 High Initial Investment Costs 248

14.7.2 Data Security and Privacy 248

14.7.3 Interoperability and Standardization 248

14.7.4 Grid Reliability and Capacity 248

14.8 Future Directions and Innovations in EV Charging Infrastructure Management 248

14.8.1 AI and Machine Learning for Predictive Optimization 249

14.8.2 Blockchain for Secure Transactions 249

14.8.3 Ultra-Fast and Wireless Charging 249

14.9 Conclusion 249

References 250

15 Illuminating the AIs Role in Shaping Up EV Charging Infrastructures 253
Jatinder Kumar, Ashish Singh, Hawraa Ali Sabah, Yashwant Singh Bisht and Laith H. Jasim

15.1 Electro Mobility Charging Systems 254

15.2 Literature Review 256

15.3 Electric Vehicle Charging Infrastructure 257

15.3.1 Infrastructural Types of Charging 258

15.4 Optimizing the Charging Infrastructure Using Artificial Intelligence 259

15.4.1 Predicting Charging Demand with Data Analytics 260

15.4.2 Managing Dynamic Charges with AI 260

15.4.3 Planned Infrastructure Optimization Algorithms 261

15.5 Charging Intelligent Infrastructures 262

15.5.1 The Challenges of Developing EV Charging Infrastructure 262

15.5.2 Predicting Demand and Selecting Sites with AI 262

15.5.3 Managing and Balancing Loads in Real Time 263

15.5.4 The Integration of Renewable Energy Sources with AI 263

15.5.5 Infrastructural Challenges and Considerations in AI-Driven Charging 264

15.5.6 The Future of AI in EV Charging Infrastructure 264

15.6 Conclusion 265

References 265

16 Deciphering Smart Grid Integration and Energy Management 269
Jatinder Kumar, Protyay Dey, Jasgurpreet Singh Chohan, Sanjeev Kumar Shah and Laith Jasim

16.1 Introduction 270

16.1.1 Smart Grid Systems 271

16.1.2 Energy Management System 272

16.1.3 System for Managing Transmission Energy 273

16.2 A Smart Grid EMS Based on Communication Technologies 275

16.2.1 Gprs 275

16.2.2 WiMAX (IEEE 802.16) 276

16.2.3 Bluetooth (IEEE 802.15) 276

16.2.4 Power Line Communication (PLC) 276

16.3 Smart Grids: An Overview 277

16.3.1 An Overview of Smart Grid Components 277

16.3.2 Goals of a Smart Grid 278

16.4 Integrating Smart Grids with Existing Infrastructure 278

16.4.1 Upgrading Infrastructure 278

16.4.2 Synchronizing with Renewable Sources 279

16.4.3 Digitalizing the Grid 279

16.4.4 Cybersecurity Measures 279

16.5 Energy Management in the Smart Grid 279

16.5.1 Demand Response 279

16.5.2 Distributed Energy Resources Management (derm) 280

16.5.3 Energy Storage Solutions 280

16.5.4 Rates and Pricing for Real-Time Usage 280

16.5.5 Electric Vehicle (EV) Integration 280

16.6 Managing Energy and Integrating Smart Grids 280

16.7 Conclusion 281

References 282

17 Decoding the Aspects of Intelligent Traffic Management 287
Preeti Rani, Jatinder Kumar, Sandeep Singh, Protyay Dey and Laith H. Jasim

17.1 Introduction 288

17.2 Related Work 290

17.3 Proposed Methodology 292

17.3.1 Design Objectives 292

17.3.2 Method and Materials 293

17.4 ITS Applications in Various Transport Sectors 295

17.4.1 Transportation Industry 296

17.4.2 Low CE of Urban Transportation 296

17.4.3 Road Traffic Transportation Infrastructure 296

17.5 Result and Discussion 297

17.6 Conclusion 298

References 299

18 Exploring the Impact of Computer Vision in Smart Transportation 301
Umesh Chandra Garjola, Sandeep Singh, Kamaljeet Kaur, Protyay Dey and Laith Hussein

18.1 Introduction 302

18.1.1 Surveillance Systems Along Roadsides: An Overview 302

18.2 Related Work 303

18.2.1 Computer Vision Functions 303

18.3 Proposed Methodology 308

18.3.1 ACF Object Detection System 309

18.3.2 Point Tracker Algorithm 309

18.3.3 Intelligent Transportation Systems: Computer Vision Applications 309

18.3.4 Intelligent Transportation Systems and Machine Learning (ML) 310

18.3.4.1 Machine Learning: The Evolution 311

18.3.4.2 Challenges 313

18.4 Result and Discussion 314

18.5 Conclusion 317

References 317

19 Exposing the Importance of Connected Lighting for Urban Sustainability 323
Zainab. R. Abdulsada, Kamaljeet Kaur, Sapna Singh, Devendra Singh and Mohammed H. Al-Farouni

19.1 Introduction 324

19.2 Sustainability 326

19.3 Transdisciplinary Framework for Urban Lighting Research: Actors, Framework, and Four Steps 327

19.4 Understanding Connected Lighting Systems 330

19.5 Energy Efficiency and Reduced Carbon Emissions 330

19.6 Conclusion 332

References 333

20 Responsible and Green AI for Environment Sustainability 337
Kunal Gagneja, Sapna Singh, Zainab. R. Abdulsada, Shivani Pant and Rami Riad Hussien

20.1 Introduction 338

20.2 AI and the Environment 339

20.2.1 Green-by AI 340

20.2.2 Green-in AI 342

20.3 Principles of Responsible AI 344

20.4 Sustainable AI for Human and Planetary Flourishing 345

20.5 AI for Environmental Sustainability 348

20.5.1 Climate Prediction and Disaster Management 348

20.5.2 Precision Agriculture 348

20.5.3 Wildlife Conservation and Biodiversity 348

20.5.4 Renewable Energy Optimisation 348

20.6 Challenges and Future Directions 349

20.7 Conclusion 349

References 349

21 Integrating AI into Mobility as a Service (MaaS): The Future of Urban Transportation 355
Kunal Gagneja, Zainab. R. Abdulsada, Sapna Singh, Ruby Pant and Ramy Al-Fatlawy

21.1 Introduction 356

21.1.1 The MaaS Concept 356

21.2 Mobility in Rural Areas is a Problem 359

21.3 Transportation Systems and Artificial Intelligence: A Critical Review 360

21.3.1 Artificial Intelligence-Assisted Smart Cities 360

21.3.2 AI Applications Currently in Use 363

21.3.3 Identifying Research Gaps 365

21.3.4 Sustainability Implications of Apps 365

21.3.5 The Impact of Urban Development on the Environment 366

21.4 Encounters 367

21.4.1 Challenges in Knowledge 367

21.5 Expected Early Adopter and Users 368

21.6 Conclusion 371

References 371

Index 375