The current methods don't meet the security and performance needs of Internet of Vehicles (IoV) apps, and they also don't give the end user a low-latency, secure edge-computing service at the same time, while in the context of vehicles. This study presents a blockchain-enabled edge computing architecture that employs Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) for consensus, aiming to simultaneously enhance latency, energy efficiency, and security. The containerised architecture uses Hyperledger Fabric with Kubernetes to efficiently manage micro-services and move tasks off of them. In urban, suburban, and highway settings, the framework consistently outperforms baseline algorithms, with a 30–45% improvement in end-to-end latency and a 55% reduction in energy use under moderate to heavy loads. The system finished more than 95% of its tasks while keeping block consensus times under 1.2 seconds at peak loads. The architecture also showed consistent performance with different levels of vehicle density and used zero-knowledge proofs with attribute-based security to protect data from cyber threats from bad actors. These findings indicate that the integration of DDQN and blockchain will mitigate security issues in the Internet of Vehicles (IoV) by enabling secure edge computing for future vehicular networks.
Dr.B.Swathi et al. (Wed,) studied this question.