Blockchain-based technology has completely revolutionized the development of the Internet of Vehicles (IoV) framework. This has led to increasing blockchain-based Internet of Vehicles application over the last decade. However, challenges persist, including scalability, interoperability, and security issues. This paper first presents the state-of-the-art overview on IoV systems along with their applications. Then, we explore novel technologies, including blockchain-based IoV and machine learning-based IoV and highlight how the blockchain technology could be integrated with machine learning for intelligent transportation systems in the IoV ecosystem. This paper has shown the potential of machine learning integration in addressing the technical challenges in individual blockchain-based Internet of Vehicles applications.
The rapid integration of connectivity and automation in modern vehicles has significantly expanded the attack surface of in-vehicle networks, particularly the Controller Area Network (CAN) bus, which lacks native security mechanisms. This study investigates machine learning-based…
Abnormal traffic flow prediction is crucial for reducing traffic congestion. Most recent studies utilized machine learning models in traffic flow detection systems. However, these detection systems do not support real-time analysis. Centralized machine learning methods face a num…
The integration of machine learning (ML), blockchain, and the Internet of Things (IoT) in smart cities represents a pivotal advancement in urban innovation. This convergence addresses the complexities of modern urban environments by leveraging ML’s data analytics and predictive c…
Recently, the advent of blockchain (BC) has sparked a digital revolution in different fields, such as finance, healthcare, and supply chain. It is used by smart healthcare systems to provide transparency and control for personal medical records. However, BC and healthcare integra…
The Internet of Vehicles (IoV) presents complex cybersecurity challenges, particularly against Denial-of-Service (DoS) and spoofing attacks targeting the Controller Area Network (CAN) bus. This study leverages the CICIoV2024 dataset, comprising six distinct classes of benign traf…
To combat the growing danger of zero-day attacks on IoT networks, this study introduces a Cluster-Based Classification (CBC) method. Security vulnerabilities have become more apparent with the growth of IoT devices, calling for new approaches to identify unique threats quickly. T…