Distributed Edge Caching for Zero Trust-Enabled Connected and Automated Vehicles: A Multi-Agent Reinforcement Learning Approach
Xiaolong Xu, Xuanhong Zhou, Xiaokang Zhou, Muhammad Bilal, Lianyong Qi, Xiaoyu Xia, Wanchun Dou
Abstract
Zero Trust model enhances the security of wireless network environments, which is thought to be effectively applicable to Connected and automated vehicles (CAVs). Considering the abundance of real-time data in CAVs and the delay introduced by the data validation of the Zero Trust model, it may result in significant delay when processing real-time data. By caching popular content in advance on edge servers, edge caching can significantly reduce the response delay of real-time data in CAVs. However, achieving low-delay service responses requires ultra-dense deployments of edge servers, which increases the complexity of the wireless network. Therefore, it is challenging to achieve efficient cooperative caching between edge servers in Zero Trust-enabled CAVs. In this article, a Distributed Edge Caching method with Multi-Agent reinforcement learning for Zero Trust-enabled CAVs, named D-ECMA, is proposed. Specifically, a collaboration graph construction method is designed to obtain efficient collaborative relationships. Then a prediction method for the demand of services based on Spatial-Temporal Fusion Graph Neural Networks (STFGNN) is proposed to help edge servers adjust their caching policies. Following, a distributed edge caching method based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for Zero Trust-enabled CAVs is designed. Finally, the effectiveness of D-ECMA is demonstrated through comparative experiments.