Intelligent Joint Optimization of Deployment and Task Scheduling for Mobile Users in Multi-UAV-Assisted MEC System

Mobile edge computing (MEC) servers integrated with multi-unmanned aerial vehicles (multi-UAVs) present a new system the multi-UAV-assisted MEC system. This system relies on the mobility of the UAVs to reduce the transmission distance between the servers and mobile users, thereby enhancing service quality and minimizing the overall energy consumption. Achieving optimal UAV deployment and precise task scheduling is crucial for improved coverage and service quality in this system. This problem is framed as a nonconvex optimization problem known as joint task scheduling and deployment optimization. Recently, an optimization technique based on a dual-layer framework: Upper layer optimization and lower layer optimization have been proposed to tackle this problem and achieved superior performance compared to the alternative methods. In this framework, the lower layer was responsible for task scheduling optimization, while the upper layer was designed to assist in optimizing UAV deployment and thus achieving improved coverage and enhanced task scheduling for mobile users, thereby minimizing the total energy consumption. However, further refinement of upper layer optimization is needed to improve the deployment process. In this study, the upper layer undergoes enhancement through key modifications: First, random selection of the solutions is replaced with sequential selection to maintain the unique characteristics of each individual throughout the optimization process, fostering both exploration and exploitation. Second, a selection of recently reported metaheuristic algorithms, such as spider wasp optimizer (SWO), generalized normal distribution optimization (GNDO), and gradient-based optimizer (GBO), are adapted to optimize UAV deployments. Both improved upper layer and lower layer optimization led to the development of novel, more effective optimization approaches, including IToGBOTaS, IToGNDOTaS, and IToSWOTaS. These techniques are evaluated using nine instances with a variety of mobile tasks ranging from 100 to 900 to test their stability and then compared to different optimization techniques to measure their effectiveness. This comparison is based on several statistical information to determine the superiority and difference between their outcomes. The results reveal that IToGBOTaS and IToSWOTaS exhibit slightly superior performance compared to all other algorithms, showcasing their competitiveness and efficacy in addressing the optimization challenges of the multi-UAV-assisted MEC system.