Blockchain and Machine Learning in Internet of Vehicles: Applications, Challenges, and Opportunities

The Internet of Vehicles (IoV) has emerged as a promising technology for transforming transportation systems by leveraging intelligent services and data-driven decision-making. Leveraging machine learning (ML) techniques, IoV data offers various benefits, including enhanced traffic management, improved road safety, and personalized user experiences. However, centralized ML methods face challenges in scalability and security, hampering their effectiveness in large-scale IoV deployments. This article presents a scalable and secure framework that incorporates distributed machine learning and blockchain technologies into the IoV ecosystem to overcome these limitations. The proposed framework enables the distribution of ML algorithms among participating vehicles, with each vehicle training a local model using its data. By executing a consensus algorithm, Roadside Units (RSUs) aggregate local models to provide more personalized and intelligent services in a scalable manner. Furthermore, the integration of blockchain ensures safety, transparency, and untampered features, thereby enhancing the overall security of the IoV system. This framework holds the potential to advance the efficiency, scalability, and security of IoV applications, paving the way for the widespread adoption of intelligent services in the transportation domain.