Social-Aware Assisted Edge Collaborative Caching Based on Deep Reinforcement Learning Joint With Digital Twin Network in Internet of Vehicles

With the development of Intelligent Transportation Systems (ITS), edge caching has gradually emerged as a critical technology to reduce transmission delay and optimize network load. However, the limited storage capacity and service scope of individual cache servers significantly degrade the performance of edge caching. To address this issue, we propose a social-aware assisted edge collaborative caching algorithm based on Dueling Double Deep Q-Network and Digital Twin Network (SACTD-D3). The algorithm can dynamically adjust the caching decision based on the similarity of user semantic information and the availability of edge services to fully utilize the caching capacity of edge servers. Firstly, vehicle clusters are formed based on users' semantic similarity, and an on-board cloud is constructed to reduce user request delay by sinking edge services. Secondly, based on the establishment of the three-layer structure of macro base station, roadside units and on-board cloud, the content heat-based caching decision policy is utilized to effectively improve the content cache hit rate. Moreover, an optimization problem is formulated to maximize the overall utility of the system subject to transmission delay and system cost, and thus the optimal solution is obtained using the proposed $\varepsilon$ -greedy SACTD-D3 algorithm. Furthermore, due to the dynamic complexity of the network topology, digital twin is used to simplify and map the network topology into digital twin networks for analysis and processing to improve network efficiency. Finally, the simulation results demonstrate the effectiveness of the proposed algorithm in improving the system performance. Compared with Double DQN, Dueling DQN and DQN, the proposed SACTD-D3 algorithm reduces the request delay by 2.62 $\%$ , 3.06 $\%$ and 3.95 $\%$ , and reduces the energy cost by 26.07 $\%$ , 47.05 $\%$ and 49.90 $\%$ , respectively.