Enhancing Vehicular Communication with Blockchain and PPO-Optimized MEC Caching

In vehicular communication and mobile edge computing (MEC) networks, limited storage resources and challenges related to vehicles data security pose significant concerns. To address these issues while reducing communication latency and enhancing network security, blockchain technology is introduced. Additionally, deep reinforcement learning (DRL) is leveraged to optimize content caching strategies. By formulating a Markov decision process (MDP) model and applying the proximal policy optimization (PPO) algorithm, efficient cache management and optimal resource allocation are achieved. Simulation results demonstrate that the proposed approach effectively improves cache hit rates and significantly reduces latency in vehicular communication environments, yielding superior performance.