Abstract
Abstract
The large number of Electric Vehicles (EVs) now connected to the Internet-of-Things (IoT) network has created numerous obstacles in securing data sharing, managing decentralised authentication, safeguarding privacy and ensuring trust management. However, traditional centralized solutions can be compromised by data manipulation, spoofing, unauthorized user access, and the systems can become vulnerable to single points of failure, potentially reducing the reliability and scalability of intelligent transportation systems. The challenges mentioned above motivate this paper to present a Blockchain-Assisted Federated Graph Reinforcement Learning (BFGRL) framework for secure and scalable smart EV networks. Within the proposed framework, PUF-ECC is used to create lightweight and tamper-resistant authentication of vehicles, GATs for complex interaction modelling, Federated Learning (FL) for privacy-preserving distributed trust assessments and Proximal Policy Optimization (PPO) for optimizing vehicle validation and blockchain consensus. It includes three parts: decentralized identity authentication, secure data sharing with intelligent trust and privacy mechanisms, and efficient transaction processing with blockchain consensus optimization. The proposed approach presented in this document was benchmarked against the VeReMi vehicular security dataset as well as various simulated scenarios of a blockchain-based EV network, while also comparing against the ones already existing in the literature: blockchain-based authentication, federated learning and trust management methods. The experimental results showed that all of the existing baseline approaches were outperformed in terms of authentication accuracy (99.2%), attack detection rate (98.7%), data integrity score (99.5%), privacy preservation (98.4%), scalability (97.2%) and transaction throughput (5200 TPS), with a statistical difference (
p
< 0.05). The proposed framework also resulted in an average computation overhead of 41 ms for authentication and consensus operations, making it feasible for real-time applications. The results indicate that BFGRL is a suitable solution that is privacy-protected, efficient and powerful for the intelligent transportation system and next-generation smart electric vehicle system. Experiments conducted in a controlled simulation environment show that BFGRL can effectively improve security, trust management, scalability, and operational efficienssssscy in blockchain-assisted smart EV networks. These results suggest its promising potential for intelligent transportation applications, with further testing with real-world vehicular communication data and field trials being critical research areas.