Joint Optimization of Resource Scheduling and Mobility for UAV-Assisted Vehicle Platoons

In the era of the Internet of Everything, autonomous driving has put forward a higher ambition for data transmission capabilities. This paper studies joint scheduling of computation and communication resources in the collaborative networking of unmanned aerial vehicles (UAV s) and platooning vehicles in mobile edge computing (MEC) framework to maximize the energy efficiency. Considering the movement characteristics of vehicles, we integrate mobility, communication, computation, and energy consumption to establish a collective optimization problem. Since this multivariate coupled model is non-convex, we further propose a joint optimization method (JOM) algorithm based on the convex approximation theory, particularly quadratic programming. Experimental results verify that this algorithm converges quickly within a dozen iterations and proves to be superior to several other benchmark schemes.