Enhanced Security in Vehicular Ad-Hoc Networks Using Blockchain, Homomorphic Based Encryption, and Reinforcement Learning Model

Vehicular Ad-Hoc Networks (VANETs) offer substantial benefits in relation to traffic efficiency, safety, and infotainment. Nonetheless, a target vehicle with a dynamic and large-scale environment faces challenges, especially with respect to security, privacy, and scalability of the systems. To address these challenges, this study proposes an improved security solution for VANETs by utilizing a Homomorphic Encryption, Reinforcement Learning (RL), and Blockchain to deliver a secure data storage mechanism. In this method, Homomorphic Encryption, to encrypted traffic data, enabling backend computations on the encrypted data and preserving privacy. Thereafter, Reinforcement Learning utilized to optimize decision-making in the network, while providing efficient management of dynamic traffic scenarios and improving the VANET performance. Blockchain used to provide a secure, distributed data storage, to improve trust and transparency between the VANET communication devices. The effectiveness of this solution is demonstrated through extensive simulation results, showing improvements in various operational metrics, including throughput, communication overhead, latency, encryption/decryption times, and packet loss.