Learning-Based Collaborative Computation Offloading in UAV-Assisted Multi-Access Edge Computing

Unmanned aerial vehicles (UAVs) have gained considerable attention in the research community due to their exceptional agility, maneuverability, and potential applications in fields like surveillance, multi-access edge computing (MEC), and various other domains. However, efficiently providing computation offloading services for concurrent Internet of Things devices (IOTDs) remains a significant challenge for UAVs due to their limited computing and communication capabilities. Consequently, optimizing and managing the constrained computing, communication, and energy resources of UAVs are essential for establishing an efficient aerial network infrastructure. To address this challenge, we investigate the collaborative computation offloading optimization problem in a UAV-assisted MEC environment comprising multiple UAVs and multiple IODTs. Our primary objective is to obtain efficient offloading strategies within a multi-heterogeneous UAV environment characterized by limited computing and communication capabilities. In this context, we model the problem as a multi-agent markov decision process (MAMDP) to account for environmental dynamics. We employ a multi-agent deep deterministic policy gradient (MADDPG) approach for task offloading. Subsequently, we conduct simulations to evaluate the efficiency of our proposed offloading scheme. The results highlight significant improvements achieved by the proposed offloading strategy, including a notable increase in the system completion rate and a significant reduction in the average energy consumption of the system.