Joint Position Deployment and Hierarchical Caching Optimization for UAV-Assisted V2V Caching Networks Towards Dynamic Vehicle Distribution

The rapid development of the Internet of Vehicles (IoV) has significantly increased data transmission demands, frequently causing backhaul congestion and service delays in traditional static cellular networks. To address these challenges, this paper proposes a joint position deployment and hierarchical caching optimization solution for unmanned aerial vehicle (UAV)-assisted vehicle-to-vehicle (V2V) caching networks towards dynamic vehicle distribution. Firstly, a hierarchical caching architecture is proposed, where the file library is classified into core, supplementary, and infrequent layers based on file popularity, applying deterministic caching, probabilistic caching, and no-caching strategies, respectively, to achieve efficient utilization of caching resources. Secondly, the mathematical expressions for the caching hit rate and service delay are derived, and a joint optimization problem is formulated to minimize service delay, addressing the dual challenges of hierarchical caching and UAV deployment. To address this problem, a decoupled iterative method is designed, decomposing the original problem into hierarchical caching and UAV deployment subproblems. Based on this, a grid search–tail distribution function fitting-based approach and a K-means clustering-based approach are proposed to optimize these subproblems, respectively. Finally, simulation results demonstrate that, compared to existing strategies, the proposed strategy effectively reduces service latency under multi-vehicle distribution while maintaining high cache file coverage. Under typical conditions, the proposed strategy reduced average service latency by 10% to 20%, thereby validating its effectiveness and superiority.