A Deep Learning Approach for Task Offloading in Multi-UAV Aided Mobile Edge Computing

Abstract

Computation offloading has proven to be an effective method for facilitating resource-intensive tasks on IoT mobile edge nodes with limited processing capabilities. Additionally, in context of Mobile Edge Computing (MEC) systems, edge nodes can offload its computation-intensive tasks to a suitable edge server. Hence, they can reduce energy cost and speed up processing. Despite the numerous accomplished efforts in task offloading problems on the Internet of Things (IoT), this problem remains a research gap mainly because of its NP-hardness in addition to the unrealistic assumptions in many proposed solutions. In order to accurately extract information from raw sensor data from IoT devices deployed in complicated contexts, deep learning (DL) is a potential method. Therefore, in this paper, an approach based on deep reinforcement learning (DRL) will just be presented for optimize the offloading process for IoT in MEC environments. This approach can achieve the optimal offloading decision. A Markov Decision Problem (MDP) is used to formulate the offloading problem. Delay time and consumed energy are the main optimization targets in this work. The proposed approach has been verified using extensive simulations. Simulation results demonstrate that the proposed model effectively improves the MEC system latency, energy consumption, and significantly outperforms the Deep Q Networks (DQNs) and Actor Critic (AC) approaches.