Joint Service Caching and Computation Offloading Scheme With 3D UAV Deployment for ICVs in UAV-Assisted VEC

Unmanned Aerial Vehicle (UAV)-assisted Vehicular Edge Computing (VEC) has emerged as a novel communication paradigm for compute-intensive and latency-sensitive applications in Intelligent Connected Vehicles (ICVs). In urban hotspots with traffic congestion, UAVs can alleviate the overload on edge servers, enhancing the ICVs’ quality of service (QoS) and safety. However, due to the high mobility of vehicles, effective solutions that can support low-latency and higher QoS for ICVs still lacked. Based on this, we first investigated the UAV deployment problem of maximizing vehicle coverage and system energy efficiency, and we proposed an adaptive population differential evolution (APDE) algorithm to address it. Then, we investigate the service caching and computation offloading problem of minimizing the system energy consumption and task response delay. DRL has effectively solved sequential decision-making in dynamic ICV environments, and we proposed a DRL-based Bias Correction-A3C-Gradient Sharing (BC-A3C-GS) algorithm to address it, which improves the traditional A3C algorithm in bias correction and global model update. Finally, experiment results show that the proposed UAV deployment algorithm can bring about 11.58% vehicle coverage rate improvement, and BC-A3C-GS algorithm can decrease about 30.08% task response delay and 17.4% energy consumption, and increase about 10.73% service cache hit rate.