Joint Cooperative Caching and UAV Trajectory Optimization Based on Mobility Prediction in the Internet of Connected Vehicles

In the Internet of Connected Vehicles, caching content frequently requested by users on edge devices can reduce access latency. Particularly in high-traffic density areas, Unmanned Aerial Vehicles (UAVs) can integrate into future cellular networks to enhance the network capacity and meet increased requests. Therefore, we formulate a joint optimization problem of cooperative caching of Base Station (BS) and UAVs and UAV trajectory planning to minimize network latency while considering the limited energy and storage capacity and dynamic vehicles. First, we propose a Temporal-evolving Bipartite Graph Neural Networks (TBGN) model for traveling areas prediction of vehicles. Then, regarding the coupling of optimization variables, we propose an Energy-aware Monte-Carlo Tree Search algorithm to optimize the UAV’s service trajectory by predicted spatio-temporal vehicle density. Finally, the optimization problem degenerates into a monotonic submodular function to optimize caching decisions. We utilize real vehicle trajectories for simulations. The results show that the TBGN outperforms other advanced models in terms of mobility prediction accuracy by 7.4%, and the proposed scheme reduces average latency by 16% compared to other schemes.