Joint Hybrid Caching and Replacement Scheme for UAV-Assisted Vehicular Edge Computing Networks

Due to the flexible deployment and availability of line-of-sight (LoS) link, unmanned aerial vehicle (UAV) is able to assist the roadside unit (RSU) to provide timely computing resources to the covered vehicle users in the temporary congestion roads. In UAV-assisted vehicular edge computing networks (VECNs), caching necessary data in RSU/UAV reduces task execution delay and bandwidth cost significantly. However, for the constrained storage and computation capacities of UAV and the dynamic requests of users, the efficient caching data selection and replacement schemes are needed. In this article, we propose a novel joint hybrid caching and replacement scheme in a scene that a single UAV assists RSU to cover a set of vehicle users with large number of iterative calculation tasks. In particular, both the content caching and service caching are considered for the RSU and UAV. To minimize the whole task completion delay of users, we joint optimize the hybrid caching data selection of UAV and the task offloading strategy of users, a deep Q-network (DQN)-based solution is proposed to improve the utility of UAV. Then, we design an optimal evaluation function, and propose a caching replacement scheme for both the RSU and UAV. For RSU and UAV can update its caching data separately, a double-DQN (DDQN)-based solution is proposed. Extensive simulation results show that the proposed algorithms have good convergence, and the designed schemes reduce the task completion delay efficiently.