Partial Task Offloading for AAV-Assisted Mobile Edge Computing With Energy Harvesting

Due to the adaptable deployment and extensive coverage, autonomous aerial vehicles (AAVs) have gained considerable attention in the field of mobile edge computing (MEC). Nevertheless, meeting the requirements of task offloading in AAV-assisted MEC remains challenging due to limitations in computing power and battery life. To address this challenge, we investigate the problem of distributed joint task partial offloading and energy harvesting (EH) in AAV-assisted MEC to minimize both task execution latency and energy cost of the AAV system. We formulate this problem as a mixed-integer nonlinear programming problem and decompose this problem into two subproblems: 1) task offloading strategy problem and 2) task offloading ratio and EH problem. We propose a distributed algorithm to determine task offloading strategies of AAVs iteratively. Given the task offloading strategies of AAVs, the subproblem of task offloading ratio and EH duration is solved by the sine cosine algorithm. Experimental results demonstrate that our proposed algorithm can effectively reduce the task execution latency and energy cost of the AAV-assisted MEC system, and outperforms other baselines significantly.