Collaborative framework for UAVs-assisted mobile edge computing: a proximity policy optimization approach

Abstract

Recently, unmanned aerial vehicles (UAVs) have been widely used in mobile edge computing (MEC) scenarios due to their flexibility, rapid deployment, and ability to expand communication coverage. This paper explores how UAVs can compute offloading more effectively when serving as mobile edge servers in the scenario of UAVs-assisted MEC. We aim to maximize the utility of the UAV operator by joint flight trajectory of UAVs, users’ offloading decisions, and collaborative strategy among UAVs while considering the latency and energy consumption. To this end, we first propose a dynamic latency-aware collaborative framework based on deep reinforcement learning. This framework utilizes a queuing model to implement the dynamic task backlog and models the reward of latency-sensitive tasks using a service level agreement. Then, considering the dynamics of the environment and the complexity of the problem, we describe the problem as a markov decision process and propose a proximal policy optimization-based computation offloading algorithm to solve the problem. The experimental results finally demonstrate that the proposed offloading algorithm converges rapidly and exhibits better performance compared to benchmark algorithms.