Federated Learning Based Intelligent Systems to Handle Issues and Challenges in IoVs (Part 2)

Due to privacy issues and the scattered nature of data produced by vehicles, the Internet of Vehicles (IOV) poses considerable hurdles for data collecting. In this chapter, we examine the idea of “Federated Learning on Wheels” (FLoW), which provides a decentralised method for IOV data collection with a focus on privacy. FLOW makes use of the onboard computer resources of cars to carry out model training locally, making sure that private information stays on the cars and is not shared with a centralised server. This strategy overcomes the shortcomings of conventional centralised data collecting approaches while simultaneously protecting user privacy. We examine the fundamentals of federated learning and how they relate to IOV, highlighting the advantages of maintaining privacy. We also look at secure aggregation procedures and confidentiality safeguards as additional methods for privacy-enhanced data acquisition in FLOW. Additionally, we emphasise the significance of accuracy and performance issues in decentralised contexts and use examples that illustrate FLOW's usefulness. We also explore security and trust issues, talking about possible weaknesses and methods to secure the reliability of participants and model updates. We also consider how blockchain technology may be incorporated for improved security and openness. We conclude by discussing FLOW future directions, difficulties, and ethical issues in order to shed light on its possible significance and legal ramifications. Overall, this chapter clarifies the relevance of Federated Learning to Wheels as a ground-breaking approach to data collecting with increased privacy in the Internet of Vehicles.

Blockchain technology has emerged as a promising solution for secure and decentralized electronic payments. The emergence of the Internet of Things (IoT) and the Internet of Vehicles (IoV) has opened up new possibilities for the adoption of electronic payment systems based on blockchain technology. This book chapter examines the possibilities of blockchain technology for online transactions. We talk about the difficulties and restrictions faced by blockchain-based payment systems in the IoV, such as scalability, interoperability, and legal compliance. We also discuss likely solutions to address these challenges, such as new consensus mechanisms and off-chain transactions. Additionally, we explore emerging technologies and their potential impact on electronic payments in the IoV, including AI and IoT. Finally, we discuss opportunities for future research and advancement in the sphere of electronic payments in the IoV, and how we can enable a more seamless, secure, and innovative payment ecosystem in the IoV.

Federated learning-based data dissemination solutions for Internet of Vehicles (IoVs) are gaining interest owing to their capacity to increase data dissemination performance and privacy. This chapter examines the current state of the art in federated learning-based data dissemination systems for IoVs, as well as the obstacles and possibilities associated with their deployment. A literature study, analysis of data dissemination needs in IoVs, and assessment of performance and privacy implications of alternative federated learning techniques are all part of the process for creating and assessing federated learning-based data dissemination systems in IoVs. The findings of a literature analysis and tests evaluating the performance and privacy of federated learning-based data dissemination systems in IoVs reveal that these systems have the potential to increase data dissemination performance and privacy, but various problems must be addressed. This chapter adds to the current literature by offering a thorough examination of the state-of-the-art federated learning-based data distribution systems for IoVs. The chapter discusses important obstacles and possibilities, as well as insights into the approach used to create and evaluate these systems. The chapter explores the consequences for IoVs of federated learning-based data dissemination systems, such as better data dissemination performance and privacy. The chapter focuses on possible applications in smart transportation, urban planning, and public safety. The chapter investigates the implications of federated learning-based data dissemination systems for IoVs, such as improved data dissemination performance and privacy. The chapter focuses on smart transportation, urban planning, and public safety applications. 

The development of effective and safe machine learning systems for vehicle number plate recognition (VNPR) is now necessary due to the emergence of the Internet of Vehicles (IoV). However, traditional centralised techniques run into issues with data privacy, communication overhead, and centralised data access restrictions. This study explores the possibilities of decentralized federated learning for VNPR in the IoV to solve these constraints. Decentralised federated learning, which overcomes the centralization barrier, allows local model training on the edge devices of participating cars, protecting data privacy and cutting down on communication overhead. The ramifications of this paradigm change are examined in this research, including improved data privacy and security, shared intelligence, and resilience against errors and assaults. It also looks at the trade-off between performance and decentralisation while emphasising the balance attained via improved model aggregation and resource use. Additionally covered is the difficulty of consensus algorithms and blockchain-based networks, highlighting the need for further investigation and development. Decentralised federated learning has been identified as a possible strategy for overcoming the centralization barrier in VNPR systems, opening the door to the implementation of efficient, secure, and private machine learning in the IoV.

Artificial intelligence is widely used in a variety of industries. AI technology drives much of what we do. In a similar vein, as AI-based technologies advance, smart automobiles and the Smart Transport system will likewise experience revolutionary transformation. Different techniques are applied to create a system that is used to manage traffic and increase security inside the transportation network, different techniques are used. The automatic number recognition system (ANPR) described in this research can extract an image of a vehicle license plate by employing image processing methods. To make things easier, the proposed system may be operated without the installation of any extra GPS-like devices. The suggested system consists of image processing techniques, such as filters to eliminate blur and noise when distantly acquired photographs of moving vehicles are taken. To obtain the region of interest, its edges are detected, and an image is cropped. The procedure for better outcomes includes normalization, localization, image enhancement, restoration, and character retention approaches. Its effectiveness may be negatively impacted by the state of the license plate, unconventional formats, complex vision, camera quality, camera position, tolerance for distortion, motion blur, contrast-related issues, reflections, limitations in a processing unit, environmental factors, indoor/outdoor or time-independent shots, software tools, or other hardware-based restrictions.

Even with the greatest algorithms, a successful ANPR system implementation might need extra computer hardware to boost the proposed System’s accuracy.

Smart transportation networks are essential components of modern cities, and vehicle spatial monitoring plays a significant role in improving their efficiency and safety. This book chapter covers the purpose, approach, findings, contribution, repercussions, restrictions, and usefulness of smart transportation systems for vehicle geographical tracking. The goal of the chapter is to assess current research on smart transportation systems and vehicle location monitoring, as well as to examine realworld examples and case studies. The results suggest that smart transportation systems with vehicle location tracking may greatly boost transportation system efficiency, reduce traffic congestion, and improve safety by monitoring vehicle movements in real-time and identifying possible problems. Moreover, intelligent transportation systems may give essential data to city planners and lawmakers in order to improve transportation infrastructure and develop more sustainable and equitable transportation networks. While these systems have significant potential benefits for cities, such as reducing traffic congestion, increasing transportation efficiency, and increasing safety, there are some drawbacks to consider, such as privacy concerns about the collection and use of personal data, as well as the possibility of technological failures and cybersecurity risks. The importance of this chapter originates from its thorough examination of smart transportation systems for vehicle location tracking, study of real-world examples and case studies, and focus on the possible advantages and limits of these systems for modern cities. It is a fantastic resource for legislators, city planners, transportation engineers, and academics who want to learn more about the potential of smart transportation systems to improve the efficiency and safety of modern metropolitan transportation systems.

 IBMAS (Identity-based message authentication systems) is a viable option for safeguarding data transfers in Internet of Vehicles (IoV) settings. We cover the state-of-the-art in IBMAS for IoVs in this book chapter, including their goal, techniques, results, contributions, implications, limits, and relevance. The goal of this chapter is to give academics and practitioners an overview of IBMAS for IoVs, including their design and implementation, to assist them in understanding the potential benefits and limits of these systems. We provide a study of existing IBMAS for IoVs in the literature, assessing their features, performance, and security aspects. We also propose a technique for assessing the efficacy of IBMAS for IoVs, concentrating on essential parameters such as message authentication performance, scalability, and attack resistance. The results of our test show that IBMAS can offer excellent message authentication with little overhead in IoV situations. Our addition to the literature is a thorough examination of IBMAS for IoVs, including their benefits and drawbacks, as well as a practical approach for assessing their performance and security qualities. We also talk about the consequences of IBMAS for IoVs, such as how they could affect data security and privacy in smart transportation systems. However, IBMAS has significant disadvantages, such as its reliance on a trusted third party and the risk of key exposure. Notwithstanding these limitations, IBMAS is important for IoVs because of its capacity to offer efficient and secure message authentication, which is crucial for the safe and dependable functioning of IoV systems. Finally, this chapter is an excellent resource for academics and practitioners interested in IBMAS for IoVs, covering their design, implementation, and assessment, as well as their implications for data security and privacy in intelligent transportation systems.