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Internet of Vehicles

Edited by S. Neelakrishnan, Niresh Jayarajan, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj, and Archana Naganathan
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394275083  |  Hardcover  |  
516 pages
Price: $225 USD
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One Line Description
Position yourself at the forefront of the transportation revolution with this guide to mastering computational intelligence that serves as the essential linchpin for the safe, sustainable, and hyper-connected Internet of Vehicular Things.

Audience
Academics, automobile engineers, IT professionals, and researchers working in the areas of autonomous vehicles, artificial intelligence, machine learning, wireless sensor networks, and the Internet of Things.

Description
The rapid adoption of the Internet of Things has transformed the way we interact with our environment, ushering in an era of unprecedented connectivity and data sharing. One of the most dynamic and influential manifestations of this technological revolution is the Internet of Vehicular Things, a paradigm that connects the digital and physical worlds through the integration of intelligent vehicles, smart infrastructure, and advanced communication networks. In this fast-evolving landscape, computational intelligence emerges as the linchpin that enables IoVT to realize its full potential. This book addresses the fascinating intersection of vehicle technology and computational intelligence. It explores the transformative power of algorithms, machine learning, artificial intelligence, and data analytics shaping the future of transportation. As vehicles become smarter, safer, and more efficient, the opportunities for innovation and optimization are limitless. The IoVT ecosystem encompasses a wide range of applications, from autonomous vehicles and traffic management systems to driver assistance technologies and predictive maintenance. By harnessing the collective intelligence of vehicles and infrastructures, IoVT promises to revolutionize not only our daily commute, but the broader transportation landscape, paving the way for sustainable, efficient, and safe mobility solutions. This book is a comprehensive guide for researchers, engineers, practitioners, and policymakers looking to navigate the complex terrain of IoVT using computational intelligence. It provides a multidisciplinary perspective and draws on recent advances in computer science, data science, electrical engineering, and transportation science to facilitate a deep understanding of the key concepts, challenges, and opportunities associated with IoVT and computational intelligence.

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Author / Editor Details
S. Neelakrishnan, PhD is a Professor and the Head of the Department of Automobile Engineering at the PSG College of Technology. He has been instrumental in the development of vocational programs for the automotive domain and set up 15 specialized state of the art laboratories in the automotive and textile domain.

Niresh Jayarajan, PhD is an Assistant Professor in the Department of Automobile Engineering at the PSG College of Technology and a member of the Society of Automotive Engineers of India. His research interests include electric and hybrid vehicles, vehicle acoustics, and energy management systems

Archana Naganathan, PhD is an Assistant Professor in the Department of Electrical and Electronics Engineering at the PSG College of Technology and a member of the Institute of Engineers of India. Her research interests cover the areas of power electronics, optimisation techniques, and hybrid electric vehicles.

Malathy Sathyamoorthy, PhD is an Assistant Professor in the Department of Information Technology, at the KPR Institute of Engineering and Technology. She has published more than 25 research papers in international journals, 22 papers in international conferences, eight book chapters, one book, and two patents. Her research interests include wireless sensor networks, networking, security, and machine learning.

Rajesh Kumar Dhanaraj, PhD is a Professor at Symbiosis International University. He has contributed more than 100 articles to international journals and conferences, authored and edited more than 50 books and holds 21 patents. His research interests encompass machine learning, cyber-physical systems, and wireless sensor networks. 

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Table of Contents
Preface
1. Computational Intelligence and Internet of Vehicle Things: An Introduction
Akshya. J., Sundarrajan M., Mani Deepak Choudhry, Kirubhakaran M. and Reeba Rose L.
1.1 Introduction to Computational Intelligence in Internet of Vehicle Things
1.1.1 Emergence of Intelligent Vehicle Networks
1.1.2 Role of Computational Intelligence in Connected Mobility
1.1.3 Advantages and Motivations for CI-IoVT Integration
1.2 Evolution of Vehicle Communication and IoVT Frameworks
1.2.1 From Traditional VANETs to Intelligent IoVT Systems
1.2.2 Key Components and Protocols in IoVT
1.2.3 Trends in Smart Vehicle Ecosystem Development
1.3 System Architecture for CI-Enabled IoVT Networks
1.3.1 Edge, Fog, and Cloud Integration Layers
1.3.2 Real-Time Data Processing and Predictive Analytics in IoVT
1.4 Security, Privacy, and Trust Models in CI-Based IoVT
1.4.1 Threat Landscape and Vulnerabilities in Intelligent Vehicle Networks
1.4.2 Computational Intelligence for Anomaly Detection and Cybersecurity
1.5 Performance Metrics and Benchmarking of CI-Driven IoVT Systems
1.5.1 Defining Key Performance Indicators (KPIs) for IoVT Networks
1.5.2 Benchmarking Learning Accuracy, Latency, and Energy Efficiency
1.5.3 Case Studies on Real-World CI-IoVT Deployments
1.6 Challenges and Future Trends in CI-Enabled IoVT
1.6.1 Scalability and Interoperability Issues in Heterogeneous Networks
1.6.2 Emerging Trends: 6G Integration, Federated Learning, and Quantum Enhancements
1.7 Conclusion and Research Directions
1.7.1 Summary of Key Contributions of Computational Intelligence in IoVT
1.7.2 Vision for Next-Generation Autonomous Vehicle Networks
References
2. Internet of Things in Vehicular Technologies: Past, Present, and Future
Divya Joshi, Hemlata Gangwar, Md. Wazih and Pradeep Kumar Sharma
2.1 Introduction
2.1.1 Definition of Internet of Things in Vehicular Technologies
2.1.2 Significance and Relevance in the Automotive Industry
2.1.3 Brief Overview of the Evolution of IoT in Vehicular Technologies
2.2 The Past: Mechanical Vehicles to Early Electronic Systems
2.2.1 Evolution of Automotive Technology from Mechanical to Electronic
2.2.2 Emergence of Basic Electronic Control Units (ECUs)
2.2.3 Challenges Faced in the Early Stages
2.3 Current State of IoT in Vehicular Technologies
2.3.1 Overview of the Current Landscape
2.3.2 V2X (Vehicle-to-Everything) Communication and Its Impact on Road Safety
2.3.3 Advanced Driver Assistance Systems and IoT Integration
2.3.4 Challenges and Limitations Faced in the Present Scenario
2.4 Current Framework and Design Issues of IoT
2.4.1 Layers of IoT
2.4.2 Protocols Used in IoT
2.4.3 Design Patterns Related to IoT
2.5 IoT-Enabled Applications in Vehicular Technologies
2.5.1 Connected Vehicles and Their Impact
2.5.2 V2V (Vehicle-to-Vehicle) Communications
2.5.3 V2I (Vehicle-to-Infrastructure) Communications
2.5.4 V2P (Vehicle-to-Pedestrian) Communications
2.5.5 V2N (Vehicle-to-Network) Communication
2.6 Security and Privacy Concerns
2.6.1 Addressing Security Challenges in IoT for Vehicular Technologies
2.6.2 Privacy Issues and Potential Solutions
2.6.3 Regulatory Frameworks and Standards
2.7 Emerging Technologies and Trends
2.7.1 Edge Intelligence in Vehicular IoT
2.7.1.1 Edge Computing Platform for Autonomous Vehicles
2.7.1.2 Challenges in Autonomous Vehicles (AVs)
2.7.2 Evolution of Wireless Communication: 5G Networks
2.7.2.1 Challenges in 5G V2X Communications
2.7.3 Decentralized Ledger Technology: Blockchain
2.7.3.1 Challenges in Blockchain Applications for AVs
2.8 Future Research Directions
2.8.1 Emergence of High-Definition (HD) Maps with Big Data and HPC
2.8.2 Risk Assessment
2.8.3 Enhanced Simulation Tested with AR/VR
2.8.4 Green Energy Solutions
2.8.5 Improvement of Quality of Service (QoS)
2.8.6 Smart Contracts
2.9 Case Study
2.9.1 Case Study of Successful Implementations
2.10 Conclusion
Bibliography
3. IoT-Based Network Architectures and Communication Protocols for UAV Communications
Anusha N., Malini S. and P. Suresh
3.1 Introduction
3.1.1 Components of IoT Architecture for UAVs
3.1.2 Instance of the UAV’s Architecture
3.2 Classification of UAVs
3.2.1 UAVs Based on Autonomy
3.2.2 UAVs Based on Communication Architectures
3.3 IoT Architectures
3.3.1 Three-Layer Model
3.3.2 Four-Layer Model
3.3.3 Five-Layer Model
3.3.4 Six-Layer Model
3.3.5 Seven-Layer Model
3.4 IoT Communication Protocols for UAV
3.5 Conclusion
Bibliography
4. Mobility as a Service (MaaS): A Paradigm Shift in Intelligent Transportation
P. Keerthika, P. Suresh, R. Manjula Devi and S. Savitha
4.1 Introduction
4.2 Working of Intelligent Transport Systems
4.3 Mobility as a Service in the Context of ITS
4.3.1 Evolution of MaaS
4.3.2 Importance and Benefits of MaaS
4.4 Functional Components of MaaS
4.4.1 Integration of Transport Modes
4.4.2 Digital Platforms
4.5 Design Considerations for MaaS
4.5.1 User Interface (UI) Considerations
4.5.2 User Experience (UX) Considerations
4.5.3 Practical Steps for Implementation
4.6 MaaS Payment Systems
4.6.1 Key Components of MaaS Payment Systems
4.6.2 Types of MaaS Payment Systems
4.6.3 Challenges and Considerations
4.6.4 Unified Payment Systems for Multi-Modal Transport
4.6.4.1 Key Features of Unified Payment Systems
4.6.4.2 Implementation Strategies
4.6.4.3 Challenges and Solutions
4.6.5 Security and Privacy Concerns in Payment Integration
4.6.5.1 Security Concerns
4.6.5.2 Privacy Concerns
4.6.5.3 Best Practices for Security and Privacy in MaaS Payment Integration
4.7 Technological Foundations of MaaS
4.7.1 Role of Big Data in Data Integration and Analytics
4.7.2 Predictive Analytics for Demand Forecasting and Route Optimization
4.7.3 Role of IoT and Connectivity in MaaS
4.7.4 Policy and Regulatory Considerations
4.7.4.1 Government Roles in Promoting MaaS Adoption
4.7.4.2 Regulatory Challenges and Solutions in Multi-Modal Transport
4.8 Future Trends and Innovations
4.8.1 Artificial Intelligence (AI) and Machine Learning (ML) Applications in MaaS
4.8.2 Autonomous Vehicles (AV) and Their Potential Impact on MaaS
4.9 Conclusion
References
5. Green Energy Solutions for Sustainable Vehicular Technologies
Arpit Anil Panwar, Tamilselvan Ganesan, Desin Manan E., Dhanush R. and Niresh Jayarajan
5.1 Introduction
5.2 EV Charging: Standards and Protocols
5.3 Alternative Energy Sources and Fuels
5.3.1 Solar-Powered EVs
5.3.2 Biofuels
5.3.2.1 Alcohol-Based Fuels
5.3.2.2 Biodiesel
5.3.2.3 Straight and Waste Vegetable Oils
5.3.2.4 Gaseous Biofuels
5.3.3 Hydrogen Fuel Cell Vehicles
5.4 Renewable Energy for Charging Infrastructure
5.4.1 Solar Energy
5.4.2 Wind Energy
5.4.3 Hybrid System
5.5 Conclusion
References
6. Internet of Vehicle Things–Assisted Green Edge Computing: Path for Pollution Monitoring and Control
D. Balaji, V. Bhuvanewari and M. Priyadharshini
6.1 Introduction
6.2 Monitoring and Detection
6.3 Future Scope
6.3.1 IoT-Based Pollution Control
6.3.2 IoT-Based Pollution Detection
6.3.3 IoT-Based Air Purification
6.3.4 Edge Computing–Based Pollution Monitoring
6.3.5 On-Board Pollution Monitoring
6.4 Conclusion
References
7. Security and Privacy Issues in Vehicular Communication: A Blockchain-Based Approach
S. Muthu Lakshmi, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Mahmoud Ahmad Al-Khasawneh
7.1 Overview of VC (V2V, V2I, V2X) and Its Role in Smart Transportation Systems
7.1.1 Benefits of VC
7.2 Security and Privacy Challenges in VANETs
7.2.1 Security Issues
7.2.2 Privacy Issues
7.3 Blockchain Overview
7.3.1 Core Components of Blockchain
7.3.1.1 Blocks and Chains
7.3.1.2 Cryptographic Hashing
7.3.1.3 Consensus Mechanisms
7.3.1.4 Smart Contracts
7.3.2 Advantages of Blockchain in VC
7.4 Blockchain-Based Framework for VC
7.4.1 System Architecture
7.4.2 Functional Modules
7.5 Blockchain Use Cases in VC Systems
7.5.1 Use Cases
7.5.1.1 Secure Message Broadcasting
7.5.1.2 Privacy-Preserving Data Sharing
7.5.1.3 Incident Reporting and Forensics
7.5.1.4 Authentication and Authorization
7.5.1.5 Data Integrity and Immutability
7.5.1.6 Privacy Enhancement in VC
7.5.1.7 Smart Contracts for Automated Trust and Control
7.5.1.8 Consensus Algorithms for Data Validity
7.5.1.9 Decentralized Network Architecture
7.5.1.10 Forensic Support and Legal Accountability
7.6 Conclusion
References
8. Regulatory Standards and Policies in Shaping IoVT with Computational Intelligence
D. Balaji, V. Bhuvanewari and M. Priyadharshini
8.1 Introduction
8.2 Regulations for ITS with Computational Intelligence
8.3 Specific Standards for ITS with Computational Intelligence
8.4 Connected and Automated Vehicles with Computational Intelligence
8.5 European AI Act
8.6 Conclusion
References
9. Modern World of Travel Projection Smart-Sustainable Cities: Vehicular Ad Hoc Networks (VANETs) and Computational Intelligence for Revolutionizing Future of Transportation
Bhupinder Singh, Christian Kaunert and Saurabh Chandra
9.1 Introduction
9.1.1 Imperative for Advanced Transportation Solutions in Urban Environments
9.1.2 Vehicular Ad Hoc Networks and Computational Intelligence—Overview and Relevance
9.1.3 Objectives of the Chapter
9.1.4 Structure of the Chapter
9.2 VANETs: Functionality of VANETs
9.3 CI in Transportation
9.3.1 ML and AI Applications in Traffic Optimization
9.3.2 Intelligent Decision-Making Algorithms for Dynamic Traffic Scenarios
9.4 VANETs and CI: Synergy
9.5 Environmental Sustainability: Impact of Intelligent Transportation
9.5.1 Reduction of Greenhouse Gas Emissions through Optimized Traffic Flow
9.6 Safety and Security in Smart-Sustainable Cities
9.6.1 Cybersecurity Measures for Protecting Connected Vehicles
9.7 Challenges in Implementation of VANETs and CI: Futuristic Transportation in Smart-Sustainable Cities
9.8 Conclusion
9.9 Future Scope
Bibliography
10. AI-Enabled Decision-Making and Predictive Analytics in Autonomous Vehicles
A. Reethika, P. Kanaga Priya, Malathy Sathyamoorthy, Rajesh Kumar Dhanaraj and Firoz Khan
10.1 Introduction
10.1.1 Communication of Autonomous Vehicle
10.1.2 Traffic Management System Among Autonomous and Human-Driven Vehicle
10.2 Literature Review
10.3 Background
10.3.1 Phase 1
10.3.2 Synchronous Intersection Protocols
10.3.3 Cooperative Perception
10.3.4 DSIP for Mixed Traffic
10.3.5 Decentralized Vehicle Synchronization
10.3.6 Cooperative Perception–Based High-Definition Map
10.3.7 Phase 2
10.3.8 I2V-Based Smart Rerouting
10.4 Experimental Setup and Result of Phases 1 and 2
10.4.1 Phase 1: Implementation and Evaluation
10.4.2 Performance Metric
10.4.3 Evaluation of DSIP Using Mixed and Homogeneous Traffic
10.4.4 Phase 2
10.5 Conclusion
References
11. Reinforcement Learning in Autonomous Vehicle Control: Role of Machine Learning in Action
Malini S., Anusha N. and Bhuvaneswari M.
11.1 Introduction
11.1.1 Reinforcement Learning
11.1.1.1 RL for Autonomous Driving
11.2 ML in Autonomous Driving
11.2.1 ML Techniques for Autonomous Vehicle Object Detection Lane Changing and Collision-Free Path Planning
11.2.1.1 Strengths
11.2.1.2 Weakness
11.2.1.3 Opportunities of ML in Autonomous Driving
11.2.1.4 Threats to ML in Autonomous Driving
11.2.2 Deep Learning Techniques in Autonomous Driving
11.2.2.1 Deep Learning Techniques for Autonomous Vehicle Object Detection, Lane Changing, and Path Planning
11.2.2.2 Strengths
11.2.2.3 Weakness
11.2.2.4 Subtasks in Autonomous Driving Using Deep Learning Techniques
11.2.3 RL Techniques for Autonomous Driving
11.2.3.1 RL Techniques for Autonomous Traffic Congestion Control
11.2.3.2 Reinforcement Learning Techniques for Autonomous Multiple-Lane
Changing Task
11.2.3.3 RL Techniques for Autonomous Path Planning Task in Complex Traffic Environment
11.3 Conclusion
References
12. Transformative Role of Artificial Intelligence in Empowering Decision-Making through Predictive Analytics for Autonomous Vehicles
R. Manjula Devi, P. Keerthika, Anusha N., M. Sangeetha and P. Suresh
12.1 Introduction
12.1.1 Levels of Automation
12.2 AI and Predictive Analytics: The Perfect Synergy
12.3 AI-Enabled Decision-Making Framework for AV System
12.3.1 Perception System
12.3.1.1 Sensor Fusion and Data Processing
12.3.1.2 Object Detection Using CNNs
12.3.1.3 Object Tracking Algorithm Using SORT
12.3.1.4 Ego-Vehicle Localization
12.3.1.5 Decision-Making Algorithms
12.3.1.6 Control and Execution
12.4 Conclusion
References
13. Quantum Computing Implications on Internet of Vehicle Technologies
Gnanasankaran Natarajan, Sundaravadivazhagan Balasubramanian, Raja S. R. and Manjula Arumugam
13.1 Unveiling the Mystery: An Introduction to Quantum Computing
13.1.1 Tapping into the Quantum World
13.1.2 The Building Blocks: Qubits
13.1.3 The Power of Entanglement
13.1.4 Untangling the Potential
13.1.5 Challenges and the Road Ahead
13.2 Cruising into the Future: A Deep Dive into Internet of Vehicles Technology
13.2.1 The Connected Car: Building Blocks of IoV
13.2.2 The Symphony of Communication: V2X in Action
13.2.3 The Benefits of a Connected Future
13.2.4 Trials and Contemplations
13.3 Merging Minds: The Powerful Convergence of Internet of Things and Quantum Computing
13.3.1 The Power of IoT: A Masterpiece of Sensors
13.3.2 The Quantum Advantage
13.3.3 Synergy in Action: Real-World Applications
13.3.4 Contests and Reflections: Bridging the Gap
13.4 Instrumental Navigation
13.4.1 IoV
13.4.2 Quantum Computing
13.4.3 The Combined Effect
13.4.4 The Overall Benefit
13.4.5 Encounters and the Road Ahead While the Capability is Significant, Demanding Situations Remain
13.5 Implications of Quantum Computing over IoV Technology
13.5.1 Data Processing and Analysis
13.5.2 Optimization Problems
13.5.3 Secure Communication
13.5.4 Machine Learning and AI
13.5.5 Sensor Data Fusion
13.5.6 Energy Optimization
13.5.7 Simulation and Modeling
13.6 Classic Encryption Techniques Utilized in IoV for Security Enhancements
13.6.1 Symmetric Encryption
13.6.2 Asymmetric Encryption (Public-Key Encryption)
13.6.3 Hash Function
13.6.4 Message Authentication Codes
13.6.5 Digital Signatures
13.6.6 Key Management Systems
13.7 Need of Quantum-Resistant Cryptographic Techniques in IoV
13.7.1 Vulnerability of Current Cryptography to Quantum Attacks
13.7.2 Long-Term Security
13.7.3 Protection of Sensitive Information
13.7.4 Preservation of Trust and Reliability
13.7.5 Administrative Consistence and Principles
13.8 Communication Networks: A Key Component for IoV Technology
13.8.1 Wireless Communication Technologies
13.8.2 Ad Hoc Networking
13.8.3 DSRC
13.8.4 V2I Communication
13.8.5 V2C Communication
13.8.6 Security and Protection Considerations
13.8.7 Quality-of-Service Management
13.8.8 Scalability and Resilience
13.9 The Effective Use of Quantum Technologies into IoV Communication Frameworks the Overall Network Performance
13.9.1 QKD for Secure Communication
13.9.2 Quantum Entanglement for Instantaneous Communication
13.9.3 Quantum Teleportation for Efficient Data Transfer
13.9.4 Quantum-Secure Communication Protocols
13.9.5 Quantum Machine Learning for Traffic Prediction and Optimization
13.10 Overall Challenges and Consideration in Integrating Quantum Computing with IoV
13.10.1 Existing Quantum Hardware Constraints
13.10.2 Scaling Issues
13.10.3 Necessity for a Seamless Integration Plan
13.11 Conclusion and Future Scope
References
14. 5G and B5G Networks: Algorithms, Architectures, and Implementations
Gopal S.B., Nanthiya D., Malathy Sathyamoorthy and Nithya Rekha Sivakumar
Introduction
Bibliography
15. Revolutionizing Automotive Industry with Cloud-Based AI Analytics and IoT-Enabled Autonomous Vehicles
Suresh P., Keerthika P., Devendran K. and Savitha S.
15.1 Introduction
15.1.1 Role of Cloud Computing, AI, and IoT
15.1.2 Cloud Developments in Future in Automobile Industry
15.1.3 Benefits of Cloud Computing in the Automobile Sector
15.2 Cloud-Based AI Analytics in Automotive Industry
15.3 AI Technologies in Automotive Applications
15.4 IoT in Automotive Industry
15.4.1 IoT Applications in Automotive Industry
15.4.1.1 Enhanced Driver Experience and Safety
15.4.1.2 Infotainment
15.4.1.3 Effective Supply Chain and Inventory Management
15.4.2 Future Trends and Successful Opportunities for IoT in Automotive Industry
15.5 Trends in Data Analytics and Automotive Industry
15.5.1 Autonomous Vehicles
15.5.2 Connected Cars
15.5.3 Smart City Integration
15.5.4 Digital Twins
15.5.5 Mobility-as-a-Service
15.5.6 Automotive App Ecosystems
15.6 Types of Connectivity in AVs
15.7 Case Studies on AI-Based Analytics in the Automotive Industry
15.7.1 Tesla Autopilot and Full Self-Driving
15.7.2 Waymo’s AVs
15.7.3 GM and OnStar
15.7.4 BMW and Intelligent Personal Assistant
15.8 Conclusion
References
16. Agricultural Applications of UAVs: Precision Farming Challenges
Kiruthika J. K., Rajesh Kumar S., Saranya S., Geetha S. K. and Yawanikha T.
16.1 Introduction
16.1.1 Background and Motivation
16.1.2 Objectives of the Chapter
16.2 Overview of UAV Technology
16.2.1 UAV Classification and Types
16.2.2 UAV Components and Specifications
16.2.3 Sensors and Payloads
16.2.4 Flight Planning and Data Collection Techniques
16.3 Precision Farming: Concepts and Technique
16.3.1 Impact of Precision Farming in Modern Agriculture
16.3.2 Key Components
16.3.3 Historical Development History of Precision Agriculture
16.3.3.1 AI-Powered Crop Yield Forecast Model
16.3.3.2 AI-Enabled Agricultural Sensors
16.4 Applications of UAVs in Precision Farming
16.4.1 Crop Health Monitoring and Assessment
16.4.2 Soil and Field Analysis
16.4.3 Irrigation Management
16.5 Challenges in Using UAVs for Precision Farming
16.5.1 Technical Challenges
16.5.1.1 Data Quality and Resolution
16.5.1.2 Battery Life and Flight Duration
16.5.1.3 Weather Dependency
16.5.1.4 Integration with Existing Technologies
16.5.2 Operational Issues
16.5.2.1 Regulatory and Legal Issues
16.5.2.2 Pilot Training and Experience
16.5.2.3 Pilot Training and Expertise
16.5.3 Economic Challenges
16.5.3.1 Costs of UAV and Sensor
16.5.3.2 Return on Investment
16.5.3.3 Access to Finance and Support
16.6 Case Studies and Real-World Applications
16.6.1 Successful Implementations
16.6.1.1 Case Study 1: Vineyard Management in California
16.6.1.2 Case Study 2: Rice Farming in Japan
16.6.1.3 Case Study 3: Cereal Crops in the United Kingdom
16.6.1.4 Real-World Application: Potato Farming in the Netherlands
16.6.2 Lessons Learned
16.6.3 Comparative Analysis
16.7 Future Trends and Directions
16.7.1 Advances in UAV Technology
16.7.2 Integration with AI and Machine Learning
16.7.3 Potential for Autonomous Operations
16.7.4 Emerging Standards and Best Practices
16.8 Conclusion
References
17. Integrating Blockchain and IoV for Secure and Efficient Autonomous Mobility Solutions
Tamilselvan Ganesan, Puja Das, Niresh Jayarajan and Kirubakaran Ganesan
17.1 Introduction
17.2 Overview of IoV and Blockchain
17.2.1 Basic Architecture of IoV
17.2.2 Overview of Blockchain for IoV
17.2.2.1 Security Services for Blockchain
17.2.2.2 Consensus Algorithms for Autonomous Vehicles
17.2.3 Sensors Used in Autonomous Driving
17.2.3.1 LiDAR Sensors
17.2.3.2 GNSS Sensors
17.2.3.3 Camera Systems
17.2.3.4 Inertial Measurement Units
17.2.3.5 Radar Systems
17.2.4 Levels of Automation in Vehicles
17.3 Blockchain for IoV
17.3.1 Blockchain and IoV Architecture
17.3.1.1 Sensing Layer
17.3.1.2 Communication Layer
17.3.1.3 Blockchain Layer
17.3.1.4 Computing Layer
17.3.1.5 Application Layer
17.3.2 V2G Technology
17.3.3 Vehicular Communication and Crowdsharing Applications
17.3.4 Traffic Monitoring and Prediction
17.3.5 Collision Avoidance
17.3.6 Traditional Blockchain Architecture for Autonomous Vehicles
17.3.7 Challenges in the Traditional Approach
17.4 Integration of ML and Blockchain for IoV
17.4.1 Computational Offloading
17.4.2 Predictive Maintenance
17.4.3 Security in Data Sharing and Analytics
17.4.4 Vehicle Charging and Payment Infrastructure
17.5 Conclusion
References
Index

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