Performance enhancement of UAV-enabled MEC systems through intelligent task offloading and resource allocation

Abstract

The rapid and continuous growth of the Internet of Things has led to a rising demand for processing and storage solutions that can overcome the limitations of user devices. While cloud computing provides scalability, it often introduces considerable delay. To address this challenge, a computing paradigm that brings resources closer to user devices is preferable. Unmanned Aerial Vehicles (UAVs) have emerged as an effective solution to enhance communication quality and coverage in wireless systems, particularly in specific conditions. This paper presents a UAV-enabled Mobile Edge Computing (MEC) system, where UAVs equipped with computational capabilities provide task offloading services to users. In this system, users process part of their computing tasks locally, while offloading the remaining tasks to UAVs for processing. The primary goal of the proposed algorithm is to minimize processing delays, taking into account the environmental and energy constraints of the UAVs, such as movement boundaries, link blockages, transmission delays, and battery consumption for computation and flight. To achieve this, we propose a Deep Reinforcement Learning algorithm based on the Rainbow Deep Q-Network. This algorithm explores the dynamic and stochastic environment of MEC to deploy an appropriate computation offloading policy. The extensive experiments conducted in this study demonstrate the superior performance of the proposed method. The results show fast convergence to an optimal value and an average improvement of 12.63 percent in delay compared to state-of-the-art methods.