Permissioned Blockchain and Deep Reinforcement Learning for Content Caching in Vehicular Edge Computing and Networks

Abstract

Vehicular Edge Computing (VEC) is a promising paradigm to enable huge amount of multimedia content to be cached in proximity to vehicles. Since vehicles are equipped with a certain amount of caching resource, they can be regarded as edge nodes to expand the caching capacity of the network edge. However, with much sensitive personal information, vehicles may be not willing to cache their content to an untrusted vehicle. Permission blockchain has the potential to address such an issue. In this paper, we utilize permissioned blockchain to design a secure content caching scheme between vehicles. Since high mobility of vehicles makes a dynamic caching environment, we exploit deep reinforcement learning approach to design the content caching scheme. Moreover, we propose a new block verifier selection metric, Proof-of-Utility (PoU), to enable a lightweight permissioned blockchain. Security analysis shows that our proposed blockchain empowered content caching can achieve security and privacy protection. Numerical results based on the Uber dataset indicate the DRL-inspired content caching scheme significantly outperforms two benchmark policies.