Blockchain Storage Optimization Mechanism using Hyper-heuristic Algorithm based on Reinforcement Learning in the Internet of Vehicles

Blockchain technology in the Internet of Vehicles (IoV) can effectively address security challenges. However, due to the high dynamics and resource constraints of the IoV, large-scale deployment of blockchain applications remains challenging. Although many methods for storage optimization have been proposed, none have comprehensively considered the high dynamic nature of the IoV and the impact of transaction pruning on the system. Therefore, this paper innovatively introduces a Directed Acyclic Graph (DAG) lattice structure and proposes an effective blockchain storage optimization mechanism. This paper presents a pruning utility evaluation model that takes three factors into account, namely the number of transaction copies, transaction generation time, and transaction size. Given the highly dynamic nature of the IoV, this paper innovatively employs a hyper-heuristic algorithm with reinforcement learning (HHRL) to optimize the system’s comprehensive utility, solving the problem of not being able to customize optimization algorithms to determine the optimal pruning transaction strategy in dynamic IoV environments. Experimental results demonstrate that the proposed blockchain storage optimization mechanism based on HHRL outperforms other optimization methods in terms of the average target deviation, average execution time, target variance, and success rate. The experiments show that the proposed mechanism can effectively reduce the impact of pruning transactions on blockchain compared to other approaches and better support the implementation of blockchain applications in the IoV and real-world asset tokenization scenarios.

推荐引用: Xiaodong Zhang, Ru Li, Leixiao Li, Gang Wang, Jianxiong Wan and Pengfei Yue, "Blockchain Storage Optimization Mechanism using Hyper-heuristic Algorithm based on Reinforcement Learning in the Internet of Vehicles", Engineering Applications of Artificial Intelligence, vol. 168, pp. 113964, 2026.

阅读全文