Freshness and Security-Aware Cache Update in Blockchain-Based Vehicular Edge Networks

Vehicular Edge Computing (VEC) integrated with blockchain technology holds great promise for delivering secure temporal data services. However, ensuring data freshness in edge nodes remains a difficult task due to the temporal nature, heterogeneity, and privacy concerns associated with the data. Secondly, the dynamic VEC environment degrades blockchain performance, resulting in low transaction throughput or excessive energy consumption. As a result, we formulate the problem of Blockchain-based Edge Cache Update (BECU), which aims at maximizing both edge cache benefit and blockchain performance by optimizing cache decisions and critical blockchain parameters, including primary node, block size, and block interval. Furthermore, we develop the Contextual Multi-Armed Bandit for Caching Update (CMAB-CU) algorithm for online cache decision-making, which evaluates rewards by training a linear function based on mobility features and temporal data characteristics. Additionally, we design the Deep Q-learning Network for Blockchain Parameter Optimization (DQN-BPO), which dynamically determines blockchain parameters to strike the balance between transaction throughput and energy consumption. Finally, we conduct simulations using realistic vehicular traces, demonstrating that the proposed algorithms outperform the UCB and FBI algorithms in terms of edge cache benefit and blockchain performance by 95.56% and 144.93%, respectively.