A Novel Hybrid Intelligence-Driven Blockchain Framework for Secure and Efficient V2V Electricity Trading in the Internet of Vehicles

Abstract

With the rapid adoption of Electric Vehicles (EVs) and the increasing need for Vehicle-to-Vehicle (V2V) electricity trading, ensuring privacy, security, and efficiency in decentralized transactions remains a critical challenge. Existing solutions face vulnerabilities such as identity exposure, high computational costs, and inefficiencies in consensus mechanisms. To address these issues, this paper proposes a Novel Hybrid IntelligenceDriven Blockchain Framework for Secure and Efficient V2V Electricity Trading in the Internet of Vehicles (IoV). The framework integrates a hybrid privacy-preserving mechanism utilizing a dual-layer dynamic pseudo-identity system and certificateless aggregate signcryption to enhance transactional privacy while enabling fraudulent activity traceability. Additionally, a multilayered blockchain architecture leveraging digital twins, edge computing, and decentralized storage optimizes transaction verification and reduces latency. A novel Hybrid Reinforcement Learning-based Proof-of-Reputation and Adaptive Stake (HRLPoRAS) consensus mechanism is designed, dynamically adjusting validation participation based on EV reputation scores, stake levels, and environmental factors, ensuring scalability and energy efficiency. Furthermore, a Deep Q-Network and MultiAgent Deep RL (DQN-MADRL) hybrid optimization strategy is introduced to enhance smart contract execution and relay selection, minimizing computational overhead while maintaining network resilience. Performance evaluations demonstrate that the proposed framework significantly improves privacy protection, reduces communication overhead, and enhances transaction throughput compared to existing blockchain-based V2V electricity trading schemes. This work contributes to the development of a highly scalable, intelligent, and secure electricity trading system for the future IoV ecosystem.