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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Distributed Edge Caching for Zero Trust-Enabled Connected and Automated Vehicles
T2 - A Multi-Agent Reinforcement Learning Approach
AU - Xu, Xiaolong
AU - Zhou, Xuanhong
AU - Zhou, Xiaokang
AU - Bilal, Muhammad
AU - Qi, Lianyong
AU - Xia, Xiaoyu
AU - Dou, Wanchun
PY - 2024/4/30
Y1 - 2024/4/30
N2 - 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.
AB - 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.
U2 - 10.1109/MWC.001.2300414
DO - 10.1109/MWC.001.2300414
M3 - Journal article
AN - SCOPUS:85190809097
VL - 31
SP - 36
EP - 41
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
SN - 1536-1284
IS - 2
ER -