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

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.

The Internet of Vehicles (IoV) is an emerging field that has attracted a lot of attention from researchers and practitioners alike. It encompasses a range of technologies and applications that enable communication and data exchange between vehicles, infrastructure, and other connected devices. As the IoV continues to evolve, it is important to understand the intellectual structure of the research that underpins this field. In this paper, we conduct a bibliometric analysis of IoV research to map its intellectual structure and identify emerging technologies and applications. We conducted a systematic review of the literature using bibliometric analysis techniques, including co-citation analysis and network visualization. We analyzed the publication and citation patterns of IoV research, identified the most influential authors, journals, and institutions, and explored the intellectual structure of the field using network analysis techniques. Our results show that IoV research has grown rapidly over the past decade, with a significant increase in publications and citations in recent years. The study also identified several emerging technologies and applications in IoV research, including connected vehicles, vehicular networks, autonomous driving, and smart transportation systems. These emerging technologies and applications have the potential to transform the transportation industry and improve road safety, traffic management, and energy efficiency.

The integration of Wireless Sensor Networks (WSNs) and the Internet of Vehicles (IoV) has emerged as an area of growing interest in recent years. WSNs provide an efficient means of gathering data from the environment, while the Internet of Vehicles empowers communication between vehicles, infrastructure, and among vehicles. However, the integration of WSNs and the Internet of Vehicles is challenging due to the high mobility of vehicles and the limited bandwidth of wireless communication. This bibliometric analysis examines the research trends and patterns in the area of Wireless Sensor Networks and metaheuristics for the Internet of Vehicles (IoV). Through a systematic analysis of publications in the Web of Science database, the study found that research on Wireless Sensor Networks for the Internet of Vehicles has been steadily increasing since 2010, with a peak in 2019. China was identified as the leading country in terms of research output, followed by the United States and India. The most common keywords associated with wireless sensor networks for IoV include “Internet of Things,” “routing,” “security,” “energy efficiency,” and “vehicleto- vehicle communication.” The analysis also revealed that the most popular research areas include routing protocols, energy efficiency, security, and vehicle-to-vehicle communication. This study provides valuable insights into the current state of research on WSNs for IoV and highlights the gaps between these two. Also, it shows the future research works done in this field discussing routing issues. Lens.org is used for data collection, and VoSviewer is used for data analysis.

The Internet of Vehicles (IoV) is an emerging technology that allows vehicles to communicate with each other and with the infrastructure around them. This technology has the potential to revolutionize the transportation industry, but it also raises concerns about the security of the data that is shared among vehicles, with their base stations and infrastructure.

In this context, secure data-sharing methodologies are essential to protect sensitive information, such as location, driving patterns, data of the people travelling in the vehicle, and protection of shared data from malicious factors. This chapter explores some of the methods that can be used for secure data sharing in the IoV. One approach is to use encryption and decryption techniques to protect data in transit and at rest. This method involves encoding the data in a way that only authorized parties can access it, and decoding it when it reaches its destination. Another approach is to use blockchain technology, which provides a decentralized and immutable ledger that can be used to store and verify data. Additionally, access control mechanisms, such as role-based access control, can be used to limit the access of different users to specific data sets. This method ensures that only authorized parties can access sensitive data.

In conclusion, secure data-sharing methodologies are crucial for the successful implementation of the IoV. Encryption and decryption, blockchain technology, and access control mechanisms are some of the methods that can be used to protect sensitive information and maintain the privacy and security of the data.

With the rapid growth of the Internet of Vehicles (IoV), there is an increasing need for effective and secure data sharing among vehicles, infrastructure, and other entities within the IoV ecosystem. However, traditional data-sharing mechanisms face numerous challenges, such as heterogeneity of data formats, privacy concerns, and scalability issues. In this study, we propose adaptive solutions for data sharing in IoVs, which aim to address these challenges and facilitate efficient and secure data exchange. Our approach leverages adaptive techniques to dynamically adjust data-sharing mechanisms based on the context and requirements of the IoV environment. We present a comprehensive overview of the proposed solutions, including data format transformation, privacy-preserving techniques, and scalable datasharing protocols. We also discuss the potential benefits and limitations of our approach and provide insights into future research directions in the field of data sharing in IoVs

This chapter focuses on the applications and challenges of the Internet of Vehicles (IoV) and how Natural language processing is used in safety applications in IoV. The Internet of Things (IoT) is used to identify the internet of vehicles. The tremendous growth in the smart automotive sectors has recently led to a huge rise in interest in Internet of Vehicles (IoV) technology. IoV is used to connect objects, vehicles, and surroundings so that data and information may be transferred between networks. It also lets cars transmit and gather information about other vehicles and roadways. By easing traffic congestion, enhancing traffic management, and assuring road safety, IoV is introduced to improve the experience of road users. The challenges and problems that the contemporary IoV system faces are covered in this study. How to manage the privacy of huge groups of data and cars in IoV systems is one of the critical issues that researchers need to deal with. IoV networks may benefit from the numerous clever solutions provided by artificial intelligence (AI) technology to handle all the queries and problems. There is a deep connection between IoT and AI. Similarly, IoV being a subset of IoT and natural language processing (NLP) being a subset of AI are also deeply connected. Without NLP, it is difficult to run the voice control systems in IoV. The hands-free interface, which is provided by NLP, benefits the IoV in many ways.

NLP techniques can be used to improve safety concerns in IoV. For instance, using sensory data from the surrounding area, NLP may be used to analyze driving behavior and the surroundings in order to prevent traffic accidents. This chapter consists of a detailed survey on IoV, with its applications and challenges, and NLP technologies that can be used for safety applications.

Federated learning is a machine learning approach that allows many parties to collaborate on training a model without disclosing their raw data. Federated learning is critical in the context of the Internet of Vehicles (IoVs) because it allows cars to exchange sensitive data while maintaining privacy and security. This chapter of the book delves into federated learning-based frameworks for trustworthy and secure communication in IoVs. The chapter investigates the difficulties associated with training machine learning models in IoVs and evaluates the various federated learning frameworks offered for this context. The chapter examines the significance of secure communication and privacy protection in federated learning and the many strategies and procedures utilized to achieve these objectives. It investigates federated learning's possible applications in IoVs, such as traffic prediction and management, intelligent routing optimization, and vehicle safety and security enhancement. Finally, the chapter discusses future research areas for federated learning in IoVs and their implications for the discipline. While numerous federated learning frameworks have been developed for IoVs, privacy and security issues must be solved before federated learning can realize its full potential in IoVs. The chapter suggests several potential future research areas, including developing new federated learning frameworks that better address the challenges of IoVs, exploring additional federated learning applications in this context, and evaluating the performance and efficiency of different federated learning approaches in IoVs.