Cognitive Radio-Based Internet of Vehicles Architectures, Applications and Open Issues
The incorporation of Cognitive Radio (CR) into the Internet of Vehicles (IoV) has emerged as the Intelligent Transportation System (ITS). In CR-IoV, ML and Data Science can be collaboratively used to further enhance road safety through inter-vehicle, intra-vehicle, and beyond-vehicle networks.
| Otros Autores: | , , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
[2025]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009869126206719 |
Tabla de Contenidos:
- Cover
- Half Title
- Title
- Copyright
- Contents
- About the Editors
- List of Contributors
- Section 1 Introduction to CR-IoV
- 1 Intelligent Transportation System in Cognitive Radio Internet of Vehicles
- 1.1 Introduction
- 1.2 Cognitive Radio in the IoV
- 1.2.1 Cognitive Radio
- 1.2.2 Cognitive Cycle
- 1.3 Applications of CR-based IoV in ITSs
- 1.3.1 Safety
- 1.3.2 Service-Oriented Applications
- 1.4 Application Requirements
- 1.4.1 Delay
- 1.4.2 Guaranteed Delivery
- 1.4.3 Bandwidth
- 1.5 Issues and Challenges
- 1.5.1 Unreliable Delivery
- 1.5.2 Dynamic Topology
- 1.5.3 Routing Overhead
- 1.5.4 Scalability
- 1.5.5 Energy
- 1.5.6 Spectral Efficiency
- 1.6 Conclusion
- References
- Section 2 Machine Learning in CR-IoV
- 2 Machine Learning Applications in CR-IoV
- 2.1 Introduction
- 2.2 Overview of ML Techniques
- 2.2.1 Categorization of ML Techniques
- 2.2.2 Supervised Learning
- 2.2.3 Distributed Training and Inference of ML Models
- 2.3 ML Challenges and Enablers in the CR-IoV
- 2.3.1 Definitions
- 2.3.2 The CR-IoV
- 2.3.3 ML in CR
- 2.3.4 ML in the IoV
- 2.3.5 Towards a ML-Enabled CR-IoV
- 2.4 ML Applications in the CR-IoV
- 2.4.1 Spectrum Sensing and Channel Allocation
- 2.4.2 Road Safety
- 2.4.3 Security Issues
- 2.5 Challenges, Issues, and Future Directions
- 2.5.1 Machine Learning-Related Challenges
- 2.5.2 State and Network Dynamics
- 2.5.3 Multi-Agent RL (MARL) in the CR-IoV
- 2.5.4 Distributed Learning and the CR-IoV
- 2.5.5 Scalability and Universality in the CR-IoV
- 2.6 Conclusion
- References
- Section 3 Protocol and Infrastructure
- 3 Machine Learning Techniques in Conjunction with Data Science Applications for CR-IoV
- 3.1 Introduction
- 3.2 The Communication of CR-IoV
- 3.3 Demand of Machine Learning and Data Science in CR-IoV.
- 3.4 Utilizations of Data Science to CR-IoV
- 3.5 Machine Learning in Conjunction with Data Science for CR-IoV
- 3.6 Patterns Identification from CR-IoV Data using Data Science and Machine Learning
- 3.7 Conclusion
- References
- Section 4 Data Science Applications
- 4 Minimized Channel Switching and Routing Protocol for Cognitive Radio-Based Internet of Vehicles
- 4.1 Introduction
- 4.1.1 Cognitive Radio Architecture
- 4.1.2 Cognitive Radio in the IoV
- 4.1.3 Channel Assignment in Cognitive Radio
- 4.2 Literature Review
- 4.3 Problem Statement
- 4.4 Research Questions
- 4.5 Proposed MCSR
- 4.5.1 Channel Assignment Algorithm
- 4.5.2 Case Study
- 4.6 Performance Evaluation
- 4.6.1 Simulation Environment
- 4.6.2 Results and Discussion
- 4.6.3 Conclusion and Future Work
- References
- 5 Time Synchronization in Cognitive Radio-Based Internet of Vehicles
- 5.1 Time Synchronization and Its Importance
- 5.2 Fundamentals
- 5.2.1 Clocks
- 5.2.2 Physical/Hardware Clocks
- 5.2.3 Logical/Software Clocks
- 5.2.4 Imperfections of Clocks
- 5.2.5 Synchronized Clocks
- 5.2.6 Clock Adjustments
- 5.3 Synchronization Concerns
- 5.3.1 Global Time Access
- 5.3.2 Limitations
- 5.3.3 Critical Metrics
- 5.3.4 Basic Principles
- 5.3.5 Challenges
- 5.3.6 Various Types
- 5.4 Approaches to Time Synchronization in Wired Media
- 5.5 Approaches to Time Synchronization in Wireless Media and the CR-IoV
- 5.5.1 Protocols
- 5.5.2 Significance in the CR-IoV
- 5.5.3 Requirement Analysis for Time Synchronization in the CR-IoV
- 5.6 Advances towards CR-IoV Time Synchronization
- 5.6.1 Timing Synchronization Function-Based Synchronization in the CR-IoV
- 5.6.2 Global Navigation Satellite System-Based Synchronization in the CR-IoV
- 5.6.3 Single Timestamp-Based Cross-Layer Synchronization in the CR-IoV.
- 5.7 Implementing Time Synchronization in the CR-IoV
- 5.7.1 Mathematical Model
- 5.7.2 Simulation
- 5.7.3 Experimentation
- 5.7.4 Mathematical Analysis
- 5.8 Summary
- References
- 6 Data Science Applications, Approaches, and Challenges in Cognitive Radio-Based IoV Systems
- 6.1 Introduction
- 6.2 Data Science
- 6.3 Data Science Approaches and Applications in the CR-IoV
- 6.3.1 Data Science Approaches
- 6.3.2 Applications of the CR-IoV
- 6.4 Challenges and Issues
- 6.4.1 Understanding of Learning
- 6.4.2 Computing Systems for Data-Intensive Applications
- 6.4.3 Incomplete Data or Data Noise
- 6.4.4 Heterogeneous Data Sources
- 6.4.5 Privacy and Data Security
- 6.4.6 Precious Data
- 6.4.7 Causal Reasoning
- 6.4.8 Trustworthy AI
- 6.5 Conclusion
- References
- Section 5 Security and Quality of Service
- 7 Security Threats and Counter Measures in the Internet of Vehicles
- 7.1 Introduction
- 7.2 Security Threats in the IoV
- 7.2.1 Attacks on Confidentiality of IoV Communication
- 7.2.2 Attacks on Integrity of IoV Communication
- 7.2.3 Attacks on Availability of IoV Communication
- 7.3 Solutions to Security Threats in the IoV
- 7.3.1 Counter Measures for Confidentiality of Information
- 7.3.2 Counter Measures on Integrity of Information
- 7.3.3 Counter Measures on Availability Attacks
- 7.4 Conclusion
- References
- 8 QoS Provisioning in CR-Based IoV
- 8.1 QoS Requirements of IoV Applications
- 8.1.1 Vehicle Platooning
- 8.1.2 Sensor and State Map Sharing
- 8.1.3 Remote Driving
- 8.1.4 Advanced Driving
- 8.2 Priority-Wise Classification of Data
- 8.2.1 Latency, Throughput, and Data Type Considerations
- 8.2.2 Priority Classes
- 8.3 Transmission Scheduling Process
- 8.4 QoS-Aware Cognitive Communication
- 8.4.1 Concept of a Hybrid NB/WB Waveform
- 8.4.2 Hybrid Cognitive Module
- 8.5 Conclusion.
- References
- Index.
