Digital Convergence in Intelligent Mobility Systems
1st Edition
Published on 16. July 2025
Book
Hardback
416 pages
978-1-394-27524-3 (ISBN)
Description
Digital Convergence in Intelligent Mobility Systems gives a comprehensive understanding of how digital technologies are revolutionizing transportation, equipping you with the insights needed to navigate the future of intelligent mobility systems.
The rapid evolution of digital technologies has transformed the landscape of intelligent mobility systems, ushering in a new era of innovation and convergence. The integration of digital technologies into various aspects of mobility systems, such as autonomous vehicles, smart transportation networks, and advanced traffic management systems, has the potential to revolutionize how we move people and goods.
Digital Convergence in Intelligent Mobility Systems is a comprehensive guide that explores the intersection of digital convergence and intelligent mobility systems. This book aims to provide an in-depth understanding of the state-of-the-art technologies, methodologies, and applications that are reshaping the future of transportation. It will serve as a valuable resource for researchers, engineers, policymakers, and students interested in the field of intelligent mobility.
The rapid evolution of digital technologies has transformed the landscape of intelligent mobility systems, ushering in a new era of innovation and convergence. The integration of digital technologies into various aspects of mobility systems, such as autonomous vehicles, smart transportation networks, and advanced traffic management systems, has the potential to revolutionize how we move people and goods.
Digital Convergence in Intelligent Mobility Systems is a comprehensive guide that explores the intersection of digital convergence and intelligent mobility systems. This book aims to provide an in-depth understanding of the state-of-the-art technologies, methodologies, and applications that are reshaping the future of transportation. It will serve as a valuable resource for researchers, engineers, policymakers, and students interested in the field of intelligent mobility.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
ISBN-13
978-1-394-27524-3 (9781394275243)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Rathishchandra R. Gatti | Chandra Singh
Digital Convergence in Intelligent Mobility Systems
E-Book
07/2025
1st Edition
Wiley
€157.99
Available for download
Rathishchandra R. Gatti | Chandra Singh
Digital Convergence in Intelligent Mobility Systems
E-Book
07/2025
1st Edition
Wiley
€150.99
Available for download
Persons
Rathishchandra R. Gatti, PhD is a professor and Head of the Department of Mechanical and Robotics Engineering at Sahyadri College of Engineering and Management with over 23 years of experience. He has published over seven books, 30 papers in international journals, and 15 patents. His research interests include AI in engineering, machine data analytics, and robotics.
Chandra Singh is an assistant professor in the Department of Electronics and Communications Engineering at the Nitte Mahalinga Adyantaya Memorial Institute of Technology. He has published over eight books, 30 papers in international journals, and five patents. His research interests include optical and wireless communication, machine learning, and cyber physical systems.
Chandra Singh is an assistant professor in the Department of Electronics and Communications Engineering at the Nitte Mahalinga Adyantaya Memorial Institute of Technology. He has published over eight books, 30 papers in international journals, and five patents. His research interests include optical and wireless communication, machine learning, and cyber physical systems.
Content
Preface xv
1 Arduino-Based Battery-Operated Multi-Purpose Portable Seed-Sowing Machine 1
K. Raju, M. Ajay Kumar and Canute Sherwin
1.1 Introduction 2
1.2 Background 4
1.3 Design Details of Seed-Sowing Machine 8
1.3.1 Selection of DC Motor 8
1.3.1.1 Rolling Resistance 8
1.3.1.2 Grade Resistance 9
1.3.1.3 Acceleration Force 9
1.3.1.4 Total Tractive Effort 9
1.3.1.5 Torque 10
1.3.1.6 Output Speed 10
1.3.1.7 Power 10
1.3.1.8 Battery Capacity Calculation 10
1.3.1.9 Run Time of the Battery 11
1.3.1.10 Battery Stand-By Time 11
1.4 Details of Components of Seed-Sowing Machine 11
1.4.1 Mechanical Components 11
1.4.1.1 Hopper 11
1.4.1.2 Wheel 12
1.4.1.3 Shaft and Bearing 12
1.4.1.4 Chain Drive and Sprocket Assembly 12
1.4.1.5 Tilling Tool 13
1.4.1.6 Trenching Tool 13
1.4.1.7 Leveling Tool 13
1.4.2 Electrical and Electronic Components 14
1.4.2.1 Battery 14
1.4.2.2 dc Motor 15
1.4.2.3 Servo Motor 15
1.4.2.4 Relay 16
1.4.2.5 Arduino 16
1.5 Methodology 16
1.5.1 Block Diagram of the Proposed Seed-Sowing Machine 16
1.5.2 CAD Modeling of Seed-Sowing Machine 17
1.5.3 The Working Principle of the Seed-Sowing Machine 17
1.6 Results and Discussion 19
1.7 Scope for Future Work 20
1.8 Conclusions 20
References 21
2 An Overview of Intelligent Mobility of Agricultural Drones 25
Prasad G., Sukumar Dhanapalan, Brandon Bernard Chiripanyanga, Trycene Tadiwanashe Tsabora and Felix Mwiya
Introduction 26
Background of the Research 26
Technology in Agriculture 29
Using Unmanned Aerial Vehicles in Animal Farming 31
Design Flow Process 32
Management Team, GTM Strategy, and Competitive Landscape 33
Design Constraints 34
Conclusion 35
References 36
3 Simulation of Proportional-Integral and Derivative (PID) Based Traction and Speed Control System for a Four-Wheel Electric Vehicle Using MATLAB Simulink 39
Canute Sherwin, Christina Sundari, Aryan Bakle and Mahijit Dodiya
3.1 Introduction 40
3.2 Literature Review 41
3.3 Methodology 44
3.4 Results and Analysis 51
3.5 Conclusion 55
References 56
4 A Case Study on Electric Vehicles (EV) 59
Sumiksha Shetty, Smitha A. B., Manjunatha Badiger and Chandra Singh
4.1 Introduction 60
4.2 Literature Survey 61
4.3 Government Initiatives 63
4.3.1 Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME II) Scheme 63
4.3.2 National Electric Mobility Mission Plan (nemmp) 2020 63
4.3.3 Charging Infrastructure for Electric Vehicles- Guidelines and Standards of the Ministry of Power 64
4.3.4 State Government Initiatives 64
4.3.5 Public Sector Undertakings (PSUs) and Private Sector Collaboration 64
4.3.6 Smart Cities Mission 65
4.3.7 National Electric Mobility Infrastructure (NEMI) Guidelines 65
4.4 Challenges 66
4.4.1 Capital Intensive Investments 66
4.4.2 Power Supply and Grid Stability 66
4.4.3 The Issue of Uniformity in Charging Infrastructure 67
4.4.4 Space and Land Constraints 68
4.4.5 Legal and Bureaucratic Obstacles 68
4.4.6 Technology and Maintenance 69
4.4.7 Adoption Rate of EVs 70
4.4.8 Integration with Renewable Energy 70
4.5 Important Factors 71
4.6 Infrastructure 72
4.6.1 Charging Stations 72
4.6.2 Grid Upgrades 73
4.6.3 Battery Swapping Stations 74
4.6.4 Software Systems 74
4.7 Applications 75
4.8 Conclusion 76
References 76
5 Accelerating Connections with 5G and Evolution of Vehicle Communication Technology 79
Dankan Gowda V., Chippy T., V. Nuthan Prasad, Belsam Jeba Ananth M. and K.D.V. Prasad
5.1 Introduction 80
5.2 Historical Evolution of Vehicle Communication Technology 83
5.3 Foundations of 5G Technology 85
5.4 Integration of 5G in Vehicular Networks 87
5.5 Benefits of 5G in Automotive Communication 90
5.6 V2X Communication and 5G 92
5.7 Case Studies 93
5.8 Challenges and Future Directions 95
5.9 Conclusion 97
References 98
6 Predicting the Flow with Machine Learning Algorithms for Advanced Traffic Management 101
Dankan Gowda V., Rupali Suraskar, Ridhi Rani, K.D.V. Prasad and Ved Srinivas
6.1 Introduction 102
6.2 Fundamentals of Machine Learning in Traffic Management 105
6.3 Applications of ML in Traffic Prediction and Management 107
6.4 Case Studies 110
6.5 Challenges and Limitations 112
6.6 Future Trends and Innovations 115
6.7 Conclusion 118
References 120
7 Secure Routes and Cybersecurity Challenges in Autonomous Mobility Systems 125
Dankan Gowda V., Ribhu Abhusan P., V. Nuthan Prasad, K.D.V. Prasad and P. Vishnu Prasanth
7.1 Introduction 126
7.2 The Landscape of Autonomous Mobility 129
7.3 Cybersecurity Challenges 132
7.4 Secure Routes: Ensuring Safety in Navigation and Control 135
7.5 Defensive Technologies and Strategies 138
7.6 Regulatory and Standardization Efforts 141
7.7 Ethical and Privacy Considerations 144
7.8 Case Studies of Secure Autonomous Mobility Implementations 147
7.9 Future Directions and Research Opportunities 150
7.10 Conclusion 153
References 155
8 Green Routes Building the Backbone for Electric Vehicle Charging 159
Dankan Gowda V., Sadashiva V. Chakrasali, Ved Srinivas, K.D.V. Prasad and Saptarshi Mukherjee
8.1 Introduction 160
8.2 Current State of EV Charging Infrastructure 163
8.3 Technological Innovations in EV Charging 166
8.4 Designing Sustainable Charging Networks 169
8.5 Integration with Renewable Energy Sources 172
8.6 Economic and Business Models 176
8.7 Policy, Regulations, and Standards 178
8.8 Public Perception and Adoption 182
8.9 Future Directions and Innovations 185
8.10 Conclusion 187
References 189
9 Vehicular Power Line Communication 193
Smitha Gayathri D., K.R. Usha Rani and Yasha Jyothi Shirur
9.1 Introduction 194
9.2 Review and Categorization of Impedance Matching Techniques in Existing Literature 197
9.2.1 Impedance Matching: Concept and Classification 198
9.2.2 Related Works and Developments 199
9.3 Model of Vehicular Power Line Communication 200
9.3.1 The Resonance and Absorption Technique for Advanced Impedance Matching 201
9.3.1.1 Matching the Impedance to Access Inductive Impedance 201
9.3.1.2 System Structure 204
9.4 Simulation Results besides Analysis 208
9.5 Conclusion 213
References 213
10 Future Trends in V2X Communication and Interoperability 217
Dankan Gowda V., D. Palanikkumar, Satish Dekka, K.D.V. Prasad and Shivoham Singh
10.1 Introduction 218
10.2 Emerging Technologies in V2X Communication 221
10.3 Autonomous Vehicles and V2X Integration 223
10.4 Edge Intelligence and Decentralized Communication 226
10.5 Interoperability in a Multi-Vendor Ecosystem 229
10.6 Cybersecurity in Future V2X Systems 231
10.7 Environmental and Sustainability Considerations 232
10.8 User Experience and Human-Machine Interaction 234
10.9 Conclusion 236
References 237
11 Toward Smarter Streets: Leveraging Machine Learning, 5G, and V2X Communication for Traffic Insights 241
Smitha A. B., Manjunatha Badiger, Sumiksha Shetty, Chinmaya H., Sanketh C. Naik, Sujan R. Arasa, Ajay Deepak Lobo and Shreyas K.
11.1 Introduction 242
11.2 Literature Survey 242
11.3 5G Technology and Its Role in Transportation 249
11.4 Vehicular Communication and V2X Standards 250
11.4.1 Overview of Vehicular-to-Everything (V2X) Communication Technologies 250
11.4.2 V2X Communication Standards and Protocols 252
11.4.3 Importance of Interoperability for Seamless Communication between Vehicles and Infrastructure 254
11.5 Integration of Machine Learning with 5G and V2X Communication 255
11.5.1 Introduction to Machine Learning Algorithms Used in Traffic Prediction 255
11.5.2 Overview of Data Sources and Features Used for Training Traffic Prediction Models 256
11.5.3 Challenges and Opportunities in Integrating Machine Learning with 5G and V2X Communication 257
11.5.4 Potential Applications of Machine Learning in Optimizing Traffic Flow and Management 258
11.6 Dynamic Traffic Prediction and Management 259
11.6.1 Real-Time Data Utilization for Dynamic Traffic Prediction 259
11.6.2 Techniques for Route Optimization and Vehicle Rerouting 260
11.6.3 Machine Learning and V2X in Dynamic Traffic Signal Optimization 260
11.6.4 Benefits of Adaptive Traffic Signal Control in Improving Traffic Flow and Reducing Congestion 261
11.6.5 Safety Applications and Collision Avoidance Systems 261
11.7 Future Directions and Challenges 262
11.7.1 Emerging Trends and Future Directions in the Integration of Machine Learning, 5G, and V2X Communication 262
11.7.2 Addressing Challenges 263
11.7.3 Opportunities for Further Research and Development in the Field of Intelligent Transportation Systems 264
11.8 Conclusion 264
References 265
12 Empowering Healthcare through Mobility as a Service: A Comprehensive Review 271
Manjunatha Badiger, Thrisha B., Kshithij H. S., Sathwik M. S. and Rakshitha N.
12.1 Introduction 272
12.2 Mobility as a Service (MaaS) in Healthcare 274
12.2.1 Overview of Healthcare Access Challenges 274
12.2.2 Enhancing Medical Access with Mobility as a Service 275
12.3 Low-Cost Generic Medicine Dispensers 277
12.4 Regulatory and Infrastructure Considerations 279
12.4.1 Challenges and Solutions 279
12.4.2 Strategic Partnerships and Stakeholder Engagement 280
12.4.3 Funding and Sustainability Models 280
12.4.4 Technology Integration and Digital Connectivity 281
12.4.5 User Education, Community Engagement, and Security Measures 281
12.5 Assessing Impact: Benefits to Healthcare, Economy, and Society 282
12.5.1 Environmental Considerations 282
12.5.2 Improved Public Health Outcomes 283
12.5.3 Enhanced Data Analytics and Health Insights 283
12.6 Future Perspective Empowering Healthcare MAAS to Support Healthcare 284
12.6.1 Environmental Considerations 285
12.7 Cost Reduction and Efficiency in Healthcare Delivery 287
References 288
13 An Enhanced Sustainable Mobility as a Service Based on 5G Network for Human-Centric Mobile Network in Smart City 293
Senthil G. A., R. Prabha, D. Roopa and S. Sridevi
13.1 Introduction 294
13.1.1 Objective and Benefits 295
13.2 Proposed Enhanced MaaS Framework 297
13.2.1 System Architecture 297
13.2.2 Service Components 298
13.2.3 Human-Centric Design 300
13.2.4 Mobility Analysis 300
13.3 Sustainability Analysis 301
13.3.1 Environmental Impact 301
13.3.2 Social Impact 302
13.3.3 Economic Impact 303
13.4 Challenges and Solutions 304
13.4.1 Technological Challenges 304
13.4.2 Communication Network and Bandwidth 305
13.4.3 Enabling Critical Infrastructures 306
13.4.4 Social and Regulatory Challenges 307
13.4.5 Quality of Service 308
13.5 Conclusion 309
13.6 Future Work 310
References 311
14 Design and Development of Foldable Electric Vehicle 315
Akshay S. Bhat, Puneeth H. S., P. Aniketh Solanki, Karthik P., Prajwal K. Kalal and Manoj S.
14.1 Introduction 315
14.2 Problem Formulation 317
14.3 Methodology and Material 318
14.3.1 Material Selection Process 319
14.3.2 Working 320
14.3.3 Electrical Components 320
14.4 Static Analysis 327
14.5 Results 328
14.6 Conclusion 329
References 330
15 Design and Development of Ultrasonic Assisted Collision Detection and Blind-Spot Reduction 331
Puneeth H. S., Akshay S. Bhat, Bhavani A., Lalit V., Sathyarjun A. B. and Vishnu K. J.
15.1 Introduction 332
15.1.1 Head-Up Display 333
15.1.2 Elements That Control IC Engine Vehicles' Speed 333
15.1.2.1 Electronic Control Unit 333
15.1.2.2 Sensors Operated by ECU 334
15.1.2.3 Air-Fuel Ratio 334
15.1.2.4 Air-Fuel Ratio and Engine Performance 335
15.1.2.5 Throttle Body 335
15.1.3 Components Associated with the Vehicle Speed in EVs 335
15.1.3.1 Throttle 336
15.1.3.2 Motor 336
15.1.3.3 Controller 336
15.2 Problem Formulation 337
15.2.1 Integration of Head-Up Display 337
15.2.2 Vehicle Speed Controller 337
15.3 Methodology 338
15.3.1 Components Used 338
15.3.2 Construction and Working 338
15.4 Scope of the Project 341
15.4.1 Implementation in IC Engines 341
15.4.2 Implementation in Electric Vehicle 342
15.4.3 Head-Up Display 343
15.5 Results and Discussions 343
15.5.1 Results 343
15.5.2 Discussions 343
15.6 Conclusion 344
References 345
16 Voting Classifier-Based Machine Learning Technique for the Prediction of the Traffic Flow for the Intelligent Transportation System 347
Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan
16.1 Introduction 348
16.2 Literature Review 350
16.3 Methodology 353
16.4 Experimental Results 355
16.5 Conclusion 360
References 360
17 Influence of Feature Selection Techniques for Social Media Data Analysis (Text and Image) 363
Aruna Bajpai and Yogesh Kumar Gupta
17.1 Introduction 364
17.2 Literature Review 364
17.3 Proposed Work 369
17.3.1 Text Feature Analysis 369
17.3.2 Image Feature Analysis 370
17.4 Results Analysis 373
17.5 Conclusions 375
Bibliography 376
About the Editors 379
Index 381
1 Arduino-Based Battery-Operated Multi-Purpose Portable Seed-Sowing Machine 1
K. Raju, M. Ajay Kumar and Canute Sherwin
1.1 Introduction 2
1.2 Background 4
1.3 Design Details of Seed-Sowing Machine 8
1.3.1 Selection of DC Motor 8
1.3.1.1 Rolling Resistance 8
1.3.1.2 Grade Resistance 9
1.3.1.3 Acceleration Force 9
1.3.1.4 Total Tractive Effort 9
1.3.1.5 Torque 10
1.3.1.6 Output Speed 10
1.3.1.7 Power 10
1.3.1.8 Battery Capacity Calculation 10
1.3.1.9 Run Time of the Battery 11
1.3.1.10 Battery Stand-By Time 11
1.4 Details of Components of Seed-Sowing Machine 11
1.4.1 Mechanical Components 11
1.4.1.1 Hopper 11
1.4.1.2 Wheel 12
1.4.1.3 Shaft and Bearing 12
1.4.1.4 Chain Drive and Sprocket Assembly 12
1.4.1.5 Tilling Tool 13
1.4.1.6 Trenching Tool 13
1.4.1.7 Leveling Tool 13
1.4.2 Electrical and Electronic Components 14
1.4.2.1 Battery 14
1.4.2.2 dc Motor 15
1.4.2.3 Servo Motor 15
1.4.2.4 Relay 16
1.4.2.5 Arduino 16
1.5 Methodology 16
1.5.1 Block Diagram of the Proposed Seed-Sowing Machine 16
1.5.2 CAD Modeling of Seed-Sowing Machine 17
1.5.3 The Working Principle of the Seed-Sowing Machine 17
1.6 Results and Discussion 19
1.7 Scope for Future Work 20
1.8 Conclusions 20
References 21
2 An Overview of Intelligent Mobility of Agricultural Drones 25
Prasad G., Sukumar Dhanapalan, Brandon Bernard Chiripanyanga, Trycene Tadiwanashe Tsabora and Felix Mwiya
Introduction 26
Background of the Research 26
Technology in Agriculture 29
Using Unmanned Aerial Vehicles in Animal Farming 31
Design Flow Process 32
Management Team, GTM Strategy, and Competitive Landscape 33
Design Constraints 34
Conclusion 35
References 36
3 Simulation of Proportional-Integral and Derivative (PID) Based Traction and Speed Control System for a Four-Wheel Electric Vehicle Using MATLAB Simulink 39
Canute Sherwin, Christina Sundari, Aryan Bakle and Mahijit Dodiya
3.1 Introduction 40
3.2 Literature Review 41
3.3 Methodology 44
3.4 Results and Analysis 51
3.5 Conclusion 55
References 56
4 A Case Study on Electric Vehicles (EV) 59
Sumiksha Shetty, Smitha A. B., Manjunatha Badiger and Chandra Singh
4.1 Introduction 60
4.2 Literature Survey 61
4.3 Government Initiatives 63
4.3.1 Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME II) Scheme 63
4.3.2 National Electric Mobility Mission Plan (nemmp) 2020 63
4.3.3 Charging Infrastructure for Electric Vehicles- Guidelines and Standards of the Ministry of Power 64
4.3.4 State Government Initiatives 64
4.3.5 Public Sector Undertakings (PSUs) and Private Sector Collaboration 64
4.3.6 Smart Cities Mission 65
4.3.7 National Electric Mobility Infrastructure (NEMI) Guidelines 65
4.4 Challenges 66
4.4.1 Capital Intensive Investments 66
4.4.2 Power Supply and Grid Stability 66
4.4.3 The Issue of Uniformity in Charging Infrastructure 67
4.4.4 Space and Land Constraints 68
4.4.5 Legal and Bureaucratic Obstacles 68
4.4.6 Technology and Maintenance 69
4.4.7 Adoption Rate of EVs 70
4.4.8 Integration with Renewable Energy 70
4.5 Important Factors 71
4.6 Infrastructure 72
4.6.1 Charging Stations 72
4.6.2 Grid Upgrades 73
4.6.3 Battery Swapping Stations 74
4.6.4 Software Systems 74
4.7 Applications 75
4.8 Conclusion 76
References 76
5 Accelerating Connections with 5G and Evolution of Vehicle Communication Technology 79
Dankan Gowda V., Chippy T., V. Nuthan Prasad, Belsam Jeba Ananth M. and K.D.V. Prasad
5.1 Introduction 80
5.2 Historical Evolution of Vehicle Communication Technology 83
5.3 Foundations of 5G Technology 85
5.4 Integration of 5G in Vehicular Networks 87
5.5 Benefits of 5G in Automotive Communication 90
5.6 V2X Communication and 5G 92
5.7 Case Studies 93
5.8 Challenges and Future Directions 95
5.9 Conclusion 97
References 98
6 Predicting the Flow with Machine Learning Algorithms for Advanced Traffic Management 101
Dankan Gowda V., Rupali Suraskar, Ridhi Rani, K.D.V. Prasad and Ved Srinivas
6.1 Introduction 102
6.2 Fundamentals of Machine Learning in Traffic Management 105
6.3 Applications of ML in Traffic Prediction and Management 107
6.4 Case Studies 110
6.5 Challenges and Limitations 112
6.6 Future Trends and Innovations 115
6.7 Conclusion 118
References 120
7 Secure Routes and Cybersecurity Challenges in Autonomous Mobility Systems 125
Dankan Gowda V., Ribhu Abhusan P., V. Nuthan Prasad, K.D.V. Prasad and P. Vishnu Prasanth
7.1 Introduction 126
7.2 The Landscape of Autonomous Mobility 129
7.3 Cybersecurity Challenges 132
7.4 Secure Routes: Ensuring Safety in Navigation and Control 135
7.5 Defensive Technologies and Strategies 138
7.6 Regulatory and Standardization Efforts 141
7.7 Ethical and Privacy Considerations 144
7.8 Case Studies of Secure Autonomous Mobility Implementations 147
7.9 Future Directions and Research Opportunities 150
7.10 Conclusion 153
References 155
8 Green Routes Building the Backbone for Electric Vehicle Charging 159
Dankan Gowda V., Sadashiva V. Chakrasali, Ved Srinivas, K.D.V. Prasad and Saptarshi Mukherjee
8.1 Introduction 160
8.2 Current State of EV Charging Infrastructure 163
8.3 Technological Innovations in EV Charging 166
8.4 Designing Sustainable Charging Networks 169
8.5 Integration with Renewable Energy Sources 172
8.6 Economic and Business Models 176
8.7 Policy, Regulations, and Standards 178
8.8 Public Perception and Adoption 182
8.9 Future Directions and Innovations 185
8.10 Conclusion 187
References 189
9 Vehicular Power Line Communication 193
Smitha Gayathri D., K.R. Usha Rani and Yasha Jyothi Shirur
9.1 Introduction 194
9.2 Review and Categorization of Impedance Matching Techniques in Existing Literature 197
9.2.1 Impedance Matching: Concept and Classification 198
9.2.2 Related Works and Developments 199
9.3 Model of Vehicular Power Line Communication 200
9.3.1 The Resonance and Absorption Technique for Advanced Impedance Matching 201
9.3.1.1 Matching the Impedance to Access Inductive Impedance 201
9.3.1.2 System Structure 204
9.4 Simulation Results besides Analysis 208
9.5 Conclusion 213
References 213
10 Future Trends in V2X Communication and Interoperability 217
Dankan Gowda V., D. Palanikkumar, Satish Dekka, K.D.V. Prasad and Shivoham Singh
10.1 Introduction 218
10.2 Emerging Technologies in V2X Communication 221
10.3 Autonomous Vehicles and V2X Integration 223
10.4 Edge Intelligence and Decentralized Communication 226
10.5 Interoperability in a Multi-Vendor Ecosystem 229
10.6 Cybersecurity in Future V2X Systems 231
10.7 Environmental and Sustainability Considerations 232
10.8 User Experience and Human-Machine Interaction 234
10.9 Conclusion 236
References 237
11 Toward Smarter Streets: Leveraging Machine Learning, 5G, and V2X Communication for Traffic Insights 241
Smitha A. B., Manjunatha Badiger, Sumiksha Shetty, Chinmaya H., Sanketh C. Naik, Sujan R. Arasa, Ajay Deepak Lobo and Shreyas K.
11.1 Introduction 242
11.2 Literature Survey 242
11.3 5G Technology and Its Role in Transportation 249
11.4 Vehicular Communication and V2X Standards 250
11.4.1 Overview of Vehicular-to-Everything (V2X) Communication Technologies 250
11.4.2 V2X Communication Standards and Protocols 252
11.4.3 Importance of Interoperability for Seamless Communication between Vehicles and Infrastructure 254
11.5 Integration of Machine Learning with 5G and V2X Communication 255
11.5.1 Introduction to Machine Learning Algorithms Used in Traffic Prediction 255
11.5.2 Overview of Data Sources and Features Used for Training Traffic Prediction Models 256
11.5.3 Challenges and Opportunities in Integrating Machine Learning with 5G and V2X Communication 257
11.5.4 Potential Applications of Machine Learning in Optimizing Traffic Flow and Management 258
11.6 Dynamic Traffic Prediction and Management 259
11.6.1 Real-Time Data Utilization for Dynamic Traffic Prediction 259
11.6.2 Techniques for Route Optimization and Vehicle Rerouting 260
11.6.3 Machine Learning and V2X in Dynamic Traffic Signal Optimization 260
11.6.4 Benefits of Adaptive Traffic Signal Control in Improving Traffic Flow and Reducing Congestion 261
11.6.5 Safety Applications and Collision Avoidance Systems 261
11.7 Future Directions and Challenges 262
11.7.1 Emerging Trends and Future Directions in the Integration of Machine Learning, 5G, and V2X Communication 262
11.7.2 Addressing Challenges 263
11.7.3 Opportunities for Further Research and Development in the Field of Intelligent Transportation Systems 264
11.8 Conclusion 264
References 265
12 Empowering Healthcare through Mobility as a Service: A Comprehensive Review 271
Manjunatha Badiger, Thrisha B., Kshithij H. S., Sathwik M. S. and Rakshitha N.
12.1 Introduction 272
12.2 Mobility as a Service (MaaS) in Healthcare 274
12.2.1 Overview of Healthcare Access Challenges 274
12.2.2 Enhancing Medical Access with Mobility as a Service 275
12.3 Low-Cost Generic Medicine Dispensers 277
12.4 Regulatory and Infrastructure Considerations 279
12.4.1 Challenges and Solutions 279
12.4.2 Strategic Partnerships and Stakeholder Engagement 280
12.4.3 Funding and Sustainability Models 280
12.4.4 Technology Integration and Digital Connectivity 281
12.4.5 User Education, Community Engagement, and Security Measures 281
12.5 Assessing Impact: Benefits to Healthcare, Economy, and Society 282
12.5.1 Environmental Considerations 282
12.5.2 Improved Public Health Outcomes 283
12.5.3 Enhanced Data Analytics and Health Insights 283
12.6 Future Perspective Empowering Healthcare MAAS to Support Healthcare 284
12.6.1 Environmental Considerations 285
12.7 Cost Reduction and Efficiency in Healthcare Delivery 287
References 288
13 An Enhanced Sustainable Mobility as a Service Based on 5G Network for Human-Centric Mobile Network in Smart City 293
Senthil G. A., R. Prabha, D. Roopa and S. Sridevi
13.1 Introduction 294
13.1.1 Objective and Benefits 295
13.2 Proposed Enhanced MaaS Framework 297
13.2.1 System Architecture 297
13.2.2 Service Components 298
13.2.3 Human-Centric Design 300
13.2.4 Mobility Analysis 300
13.3 Sustainability Analysis 301
13.3.1 Environmental Impact 301
13.3.2 Social Impact 302
13.3.3 Economic Impact 303
13.4 Challenges and Solutions 304
13.4.1 Technological Challenges 304
13.4.2 Communication Network and Bandwidth 305
13.4.3 Enabling Critical Infrastructures 306
13.4.4 Social and Regulatory Challenges 307
13.4.5 Quality of Service 308
13.5 Conclusion 309
13.6 Future Work 310
References 311
14 Design and Development of Foldable Electric Vehicle 315
Akshay S. Bhat, Puneeth H. S., P. Aniketh Solanki, Karthik P., Prajwal K. Kalal and Manoj S.
14.1 Introduction 315
14.2 Problem Formulation 317
14.3 Methodology and Material 318
14.3.1 Material Selection Process 319
14.3.2 Working 320
14.3.3 Electrical Components 320
14.4 Static Analysis 327
14.5 Results 328
14.6 Conclusion 329
References 330
15 Design and Development of Ultrasonic Assisted Collision Detection and Blind-Spot Reduction 331
Puneeth H. S., Akshay S. Bhat, Bhavani A., Lalit V., Sathyarjun A. B. and Vishnu K. J.
15.1 Introduction 332
15.1.1 Head-Up Display 333
15.1.2 Elements That Control IC Engine Vehicles' Speed 333
15.1.2.1 Electronic Control Unit 333
15.1.2.2 Sensors Operated by ECU 334
15.1.2.3 Air-Fuel Ratio 334
15.1.2.4 Air-Fuel Ratio and Engine Performance 335
15.1.2.5 Throttle Body 335
15.1.3 Components Associated with the Vehicle Speed in EVs 335
15.1.3.1 Throttle 336
15.1.3.2 Motor 336
15.1.3.3 Controller 336
15.2 Problem Formulation 337
15.2.1 Integration of Head-Up Display 337
15.2.2 Vehicle Speed Controller 337
15.3 Methodology 338
15.3.1 Components Used 338
15.3.2 Construction and Working 338
15.4 Scope of the Project 341
15.4.1 Implementation in IC Engines 341
15.4.2 Implementation in Electric Vehicle 342
15.4.3 Head-Up Display 343
15.5 Results and Discussions 343
15.5.1 Results 343
15.5.2 Discussions 343
15.6 Conclusion 344
References 345
16 Voting Classifier-Based Machine Learning Technique for the Prediction of the Traffic Flow for the Intelligent Transportation System 347
Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan
16.1 Introduction 348
16.2 Literature Review 350
16.3 Methodology 353
16.4 Experimental Results 355
16.5 Conclusion 360
References 360
17 Influence of Feature Selection Techniques for Social Media Data Analysis (Text and Image) 363
Aruna Bajpai and Yogesh Kumar Gupta
17.1 Introduction 364
17.2 Literature Review 364
17.3 Proposed Work 369
17.3.1 Text Feature Analysis 369
17.3.2 Image Feature Analysis 370
17.4 Results Analysis 373
17.5 Conclusions 375
Bibliography 376
About the Editors 379
Index 381