A mobile edge computing framework for task offloading and resource allocation in UAV-assisted VANETs

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc networks (VANETs) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on UAV due to the limited computation power. To counter the problems above, we first model and analyze the transmission model from the vehicle to the MEC server on UAV and the task computation model of the local vehicle and the edge UAV. Then, the problem is formulated as a multi-objective optimization problem by jointly considering the MEC selection, the resource allocation, and task offloading. For tackling this hard problem, we decouple the multi-objective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed algorithm achieves significant performance superiority as compared with other schemes in terms of the successful task processing ratio.