Edge Collaborative Caching Based on Incentive-Driven D3QN Combined With User Preferences in UAV-Assisted Vehicular Networks

Mobile Edge Cache allows CDN edge nodes to be deployed closer to users, reducing content transmission latency in Vehicular Networks (VANETs). However, how to effectively utilize the limited storage space of cache nodes is the main issue in current research. To address this problem, we propose an incentive-driven hierarchical collaborative caching algorithm based on D3QN combined with user preferences (PC-ID3QN). Firstly, we constructed a UAV-assisted vehicular content-centric network framework, designing different collaborative caching strategy for various layers based on user preferences. Secondly, we proposed a hierarchical incentive mechanism based on social priority to motivate users to participate in collaborative sharing, thereby enhancing the utilization of system caching resources. Next, we modeled the cache placement problem as a system utility maximization problem and proved its NP-hardness. Then, by adjusting the constraint conditions, we conducted theoretical analysis and transform it into a linear programming(LP) problem to obtain an offline theoretical solution. This solution was validated against the simulation optimal solution obtained using the proposed PC-ID3QN algorithm, demonstrating the effectiveness of the algorithm. Finally, we validate the proposed caching strategy using the MovieLens dataset and conduct extensive experiments to verify the applicability and superiority of our solution in improving cache utility. Compared with DDQN, Dueling DQN and DQN, our proposed ID3QN algorithm reduces the request delay by 1.03%,1.73% and 2.21%, and reduces the energy cost by 38.8%, 19.6% and 17.2%, respectively.