Digital Convergence in Intelligent Mobility Systems

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.

• Preface xv1 Arduino-Based Battery-Operated Multi-Purpose Portable Seed-Sowing Machine 1K. Raju, M. Ajay Kumar and Canute Sherwin1.1 Introduction 21.2 Background 41.3 Design Details of Seed-Sowing Machine 81.3.1 Selection of DC Motor 81.3.1.1 Rolling Resistance 81.3.1.2 Grade Resistance 91.3.1.3 Acceleration Force 91.3.1.4 Total Tractive Effort 91.3.1.5 Torque 101.3.1.6 Output Speed 101.3.1.7 Power 101.3.1.8 Battery Capacity Calculation 101.3.1.9 Run Time of the Battery 111.3.1.10 Battery Stand-By Time 111.4 Details of Components of Seed-Sowing Machine 111.4.1 Mechanical Components 111.4.1.1 Hopper 111.4.1.2 Wheel 121.4.1.3 Shaft and Bearing 121.4.1.4 Chain Drive and Sprocket Assembly 121.4.1.5 Tilling Tool 131.4.1.6 Trenching Tool 131.4.1.7 Leveling Tool 131.4.2 Electrical and Electronic Components 141.4.2.1 Battery 141.4.2.2 dc Motor 151.4.2.3 Servo Motor 151.4.2.4 Relay 161.4.2.5 Arduino 161.5 Methodology 161.5.1 Block Diagram of the Proposed Seed-Sowing Machine 161.5.2 CAD Modeling of Seed-Sowing Machine 171.5.3 The Working Principle of the Seed-Sowing Machine 171.6 Results and Discussion 191.7 Scope for Future Work 201.8 Conclusions 20References 212 An Overview of Intelligent Mobility of Agricultural Drones 25Prasad G., Sukumar Dhanapalan, Brandon Bernard Chiripanyanga, Trycene Tadiwanashe Tsabora and Felix MwiyaIntroduction 26Background of the Research 26Technology in Agriculture 29Using Unmanned Aerial Vehicles in Animal Farming 31Design Flow Process 32Management Team, GTM Strategy, and Competitive Landscape 33Design Constraints 34Conclusion 35References 363 Simulation of Proportional-Integral and Derivative (PID) Based Traction and Speed Control System for a Four-Wheel Electric Vehicle Using MATLAB Simulink 39Canute Sherwin, Christina Sundari, Aryan Bakle and Mahijit Dodiya3.1 Introduction 403.2 Literature Review 413.3 Methodology 443.4 Results and Analysis 513.5 Conclusion 55References 564 A Case Study on Electric Vehicles (EV) 59Sumiksha Shetty, Smitha A. B., Manjunatha Badiger and Chandra Singh4.1 Introduction 604.2 Literature Survey 614.3 Government Initiatives 634.3.1 Faster Adoption and Manufacturing of Hybrid and Electric Vehicles (FAME II) Scheme 634.3.2 National Electric Mobility Mission Plan (nemmp) 2020 634.3.3 Charging Infrastructure for Electric Vehicles— Guidelines and Standards of the Ministry of Power 644.3.4 State Government Initiatives 644.3.5 Public Sector Undertakings (PSUs) and Private Sector Collaboration 644.3.6 Smart Cities Mission 654.3.7 National Electric Mobility Infrastructure (NEMI) Guidelines 654.4 Challenges 664.4.1 Capital Intensive Investments 664.4.2 Power Supply and Grid Stability 664.4.3 The Issue of Uniformity in Charging Infrastructure 674.4.4 Space and Land Constraints 684.4.5 Legal and Bureaucratic Obstacles 684.4.6 Technology and Maintenance 694.4.7 Adoption Rate of EVs 704.4.8 Integration with Renewable Energy 704.5 Important Factors 714.6 Infrastructure 724.6.1 Charging Stations 724.6.2 Grid Upgrades 734.6.3 Battery Swapping Stations 744.6.4 Software Systems 744.7 Applications 754.8 Conclusion 76References 765 Accelerating Connections with 5G and Evolution of Vehicle Communication Technology 79Dankan Gowda V., Chippy T., V. Nuthan Prasad, Belsam Jeba Ananth M. and K.D.V. Prasad5.1 Introduction 805.2 Historical Evolution of Vehicle Communication Technology 835.3 Foundations of 5G Technology 855.4 Integration of 5G in Vehicular Networks 875.5 Benefits of 5G in Automotive Communication 905.6 V2X Communication and 5G 925.7 Case Studies 935.8 Challenges and Future Directions 955.9 Conclusion 97References 986 Predicting the Flow with Machine Learning Algorithms for Advanced Traffic Management 101Dankan Gowda V., Rupali Suraskar, Ridhi Rani, K.D.V. Prasad and Ved Srinivas6.1 Introduction 1026.2 Fundamentals of Machine Learning in Traffic Management 1056.3 Applications of ML in Traffic Prediction and Management 1076.4 Case Studies 1106.5 Challenges and Limitations 1126.6 Future Trends and Innovations 1156.7 Conclusion 118References 1207 Secure Routes and Cybersecurity Challenges in Autonomous Mobility Systems 125Dankan Gowda V., Ribhu Abhusan P., V. Nuthan Prasad, K.D.V. Prasad and P. Vishnu Prasanth7.1 Introduction 1267.2 The Landscape of Autonomous Mobility 1297.3 Cybersecurity Challenges 1327.4 Secure Routes: Ensuring Safety in Navigation and Control 1357.5 Defensive Technologies and Strategies 1387.6 Regulatory and Standardization Efforts 1417.7 Ethical and Privacy Considerations 1447.8 Case Studies of Secure Autonomous Mobility Implementations 1477.9 Future Directions and Research Opportunities 1507.10 Conclusion 153References 1558 Green Routes Building the Backbone for Electric Vehicle Charging 159Dankan Gowda V., Sadashiva V. Chakrasali, Ved Srinivas, K.D.V. Prasad and Saptarshi Mukherjee8.1 Introduction 1608.2 Current State of EV Charging Infrastructure 1638.3 Technological Innovations in EV Charging 1668.4 Designing Sustainable Charging Networks 1698.5 Integration with Renewable Energy Sources 1728.6 Economic and Business Models 1768.7 Policy, Regulations, and Standards 1788.8 Public Perception and Adoption 1828.9 Future Directions and Innovations 1858.10 Conclusion 187References 1899 Vehicular Power Line Communication 193Smitha Gayathri D., K.R. Usha Rani and Yasha Jyothi Shirur9.1 Introduction 1949.2 Review and Categorization of Impedance Matching Techniques in Existing Literature 1979.2.1 Impedance Matching: Concept and Classification 1989.2.2 Related Works and Developments 1999.3 Model of Vehicular Power Line Communication 2009.3.1 The Resonance and Absorption Technique for Advanced Impedance Matching 2019.3.1.1 Matching the Impedance to Access Inductive Impedance 2019.3.1.2 System Structure 2049.4 Simulation Results besides Analysis 2089.5 Conclusion 213References 21310 Future Trends in V2X Communication and Interoperability 217Dankan Gowda V., D. Palanikkumar, Satish Dekka, K.D.V. Prasad and Shivoham Singh10.1 Introduction 21810.2 Emerging Technologies in V2X Communication 22110.3 Autonomous Vehicles and V2X Integration 22310.4 Edge Intelligence and Decentralized Communication 22610.5 Interoperability in a Multi-Vendor Ecosystem 22910.6 Cybersecurity in Future V2X Systems 23110.7 Environmental and Sustainability Considerations 23210.8 User Experience and Human-Machine Interaction 23410.9 Conclusion 236References 23711 Toward Smarter Streets: Leveraging Machine Learning, 5G, and V2X Communication for Traffic Insights 241Smitha A. B., Manjunatha Badiger, Sumiksha Shetty, Chinmaya H., Sanketh C. Naik, Sujan R. Arasa, Ajay Deepak Lobo and Shreyas K.11.1 Introduction 24211.2 Literature Survey 24211.3 5G Technology and Its Role in Transportation 24911.4 Vehicular Communication and V2X Standards 25011.4.1 Overview of Vehicular-to-Everything (V2X) Communication Technologies 25011.4.2 V2X Communication Standards and Protocols 25211.4.3 Importance of Interoperability for Seamless Communication between Vehicles and Infrastructure 25411.5 Integration of Machine Learning with 5G and V2X Communication 25511.5.1 Introduction to Machine Learning Algorithms Used in Traffic Prediction 25511.5.2 Overview of Data Sources and Features Used for Training Traffic Prediction Models 25611.5.3 Challenges and Opportunities in Integrating Machine Learning with 5G and V2X Communication 25711.5.4 Potential Applications of Machine Learning in Optimizing Traffic Flow and Management 25811.6 Dynamic Traffic Prediction and Management 25911.6.1 Real-Time Data Utilization for Dynamic Traffic Prediction 25911.6.2 Techniques for Route Optimization and Vehicle Rerouting 26011.6.3 Machine Learning and V2X in Dynamic Traffic Signal Optimization 26011.6.4 Benefits of Adaptive Traffic Signal Control in Improving Traffic Flow and Reducing Congestion 26111.6.5 Safety Applications and Collision Avoidance Systems 26111.7 Future Directions and Challenges 26211.7.1 Emerging Trends and Future Directions in the Integration of Machine Learning, 5G, and V2X Communication 26211.7.2 Addressing Challenges 26311.7.3 Opportunities for Further Research and Development in the Field of Intelligent Transportation Systems 26411.8 Conclusion 264References 26512 Empowering Healthcare through Mobility as a Service: A Comprehensive Review 271Manjunatha Badiger, Thrisha B., Kshithij H. S., Sathwik M. S. and Rakshitha N.12.1 Introduction 27212.2 Mobility as a Service (MaaS) in Healthcare 27412.2.1 Overview of Healthcare Access Challenges 27412.2.2 Enhancing Medical Access with Mobility as a Service 27512.3 Low-Cost Generic Medicine Dispensers 27712.4 Regulatory and Infrastructure Considerations 27912.4.1 Challenges and Solutions 27912.4.2 Strategic Partnerships and Stakeholder Engagement 28012.4.3 Funding and Sustainability Models 28012.4.4 Technology Integration and Digital Connectivity 28112.4.5 User Education, Community Engagement, and Security Measures 28112.5 Assessing Impact: Benefits to Healthcare, Economy, and Society 28212.5.1 Environmental Considerations 28212.5.2 Improved Public Health Outcomes 28312.5.3 Enhanced Data Analytics and Health Insights 28312.6 Future Perspective Empowering Healthcare MAAS to Support Healthcare 28412.6.1 Environmental Considerations 28512.7 Cost Reduction and Efficiency in Healthcare Delivery 287References 28813 An Enhanced Sustainable Mobility as a Service Based on 5G Network for Human-Centric Mobile Network in Smart City 293Senthil G. A., R. Prabha, D. Roopa and S. Sridevi13.1 Introduction 29413.1.1 Objective and Benefits 29513.2 Proposed Enhanced MaaS Framework 29713.2.1 System Architecture 29713.2.2 Service Components 29813.2.3 Human-Centric Design 30013.2.4 Mobility Analysis 30013.3 Sustainability Analysis 30113.3.1 Environmental Impact 30113.3.2 Social Impact 30213.3.3 Economic Impact 30313.4 Challenges and Solutions 30413.4.1 Technological Challenges 30413.4.2 Communication Network and Bandwidth 30513.4.3 Enabling Critical Infrastructures 30613.4.4 Social and Regulatory Challenges 30713.4.5 Quality of Service 30813.5 Conclusion 30913.6 Future Work 310References 31114 Design and Development of Foldable Electric Vehicle 315Akshay S. Bhat, Puneeth H. S., P. Aniketh Solanki, Karthik P., Prajwal K. Kalal and Manoj S.14.1 Introduction 31514.2 Problem Formulation 31714.3 Methodology and Material 31814.3.1 Material Selection Process 31914.3.2 Working 32014.3.3 Electrical Components 32014.4 Static Analysis 32714.5 Results 32814.6 Conclusion 329References 33015 Design and Development of Ultrasonic Assisted Collision Detection and Blind-Spot Reduction 331Puneeth H. S., Akshay S. Bhat, Bhavani A., Lalit V., Sathyarjun A. B. and Vishnu K. J.15.1 Introduction 33215.1.1 Head-Up Display 33315.1.2 Elements That Control IC Engine Vehicles’ Speed 33315.1.2.1 Electronic Control Unit 33315.1.2.2 Sensors Operated by ECU 33415.1.2.3 Air–Fuel Ratio 33415.1.2.4 Air–Fuel Ratio and Engine Performance 33515.1.2.5 Throttle Body 33515.1.3 Components Associated with the Vehicle Speed in EVs 33515.1.3.1 Throttle 33615.1.3.2 Motor 33615.1.3.3 Controller 33615.2 Problem Formulation 33715.2.1 Integration of Head-Up Display 33715.2.2 Vehicle Speed Controller 33715.3 Methodology 33815.3.1 Components Used 33815.3.2 Construction and Working 33815.4 Scope of the Project 34115.4.1 Implementation in IC Engines 34115.4.2 Implementation in Electric Vehicle 34215.4.3 Head-Up Display 34315.5 Results and Discussions 34315.5.1 Results 34315.5.2 Discussions 34315.6 Conclusion 344References 34516 Voting Classifier-Based Machine Learning Technique for the Prediction of the Traffic Flow for the Intelligent Transportation System 347Sandeep Kumar Hegde, Rajalaxmi Hegde and Thangavel Murugan16.1 Introduction 34816.2 Literature Review 35016.3 Methodology 35316.4 Experimental Results 35516.5 Conclusion 360References 36017 Influence of Feature Selection Techniques for Social Media Data Analysis (Text and Image) 363Aruna Bajpai and Yogesh Kumar Gupta17.1 Introduction 36417.2 Literature Review 36417.3 Proposed Work 36917.3.1 Text Feature Analysis 36917.3.2 Image Feature Analysis 37017.4 Results Analysis 37317.5 Conclusions 375Bibliography 376About the Editors 379Index 381