Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 1)

Author(s): Anurag Gupta and Anjali Chauhan * .

DOI: 10.2174/9789815313024124030003

Technologies to Solve the Routing Issues in IoVs

Pp: 1-50 (50)

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Abstract

This book chapter explores the challenges and technologies involved in solving routing issues in the context of the Internet of Vehicles (IoV). The IoV represents a dynamic and complex network environment that connects vehicles, infrastructure, and various other entities. Efficient routing is crucial for timely and reliable information exchange in such networks. The chapter begins by discussing the unique challenges associated with routing in IoV, such as frequent topology changes, limited bandwidth, and high vehicle mobility. It emphasizes the need for robust and efficient routing protocols to ensure seamless data delivery in vehicular networks. Next, the chapter provides a comprehensive review of existing routing techniques and protocols designed specifically for IoV. It covers geographic routing, cluster-based routing, and hybrid routing approaches, examining their strengths, limitations, and applicability to different IoV scenarios. The chapter also discusses the importance of considering quality-of-service (QoS) metrics, such as latency, reliability, and energy efficiency, when designing routing solutions for IoV. Furthermore, the chapter explores advanced technologies that can enhance routing performance in IoV. It delves into the integration of IoV with cloud computing, edge computing, and the Internet of Things (IoT). These technologies offer additional computational resources, data storage capabilities, and real-time data processing at the network edge, leading to improved routing efficiency and reduced latency. The chapter also highlights the role of artificial intelligence (AI) and machine learning (ML) techniques in addressing routing challenges in IoV. It explores how AI and ML algorithms can analyze and predict vehicular mobility patterns, optimize routing decisions, and mitigate network congestion. The chapter emphasizes the potential of AI and ML to adaptively optimize routing strategies based on real-time network conditions. Finally, the chapter concludes by discussing open research challenges and future directions for solving routing issues in IoV. It identifies areas such as intelligent routing protocols, energy-efficient routing schemes, and security mechanisms as critical research domains. The chapter underscores the importance of ongoing research and development to ensure the efficient and secure operation of IoV routing. Overall, this book chapter provides a comprehensive overview of the technologies proposed to address routing issues in the IoV. It serves as a valuable resource for researchers, practitioners, and policymakers working in the field of vehicular networking, offering insights into the challenges, solutions, and future directions for efficient and reliable routing in IoV environments.