Joint Optimization of AAV Deployment and Task Scheduling in Multi-AAV-Enabled Mobile Edge Computing Systems

mobile edge computing (MEC) is a highly promising approach for achieving low-latency and high-performance computing services for mobile users. However, traditional MEC systems face challenges in meeting the increasing demands of mobile users due to the limited coverage and flexibility of fixed MEC servers. Integrating autonomous aerial vehicles (AAVs) with MEC has gained significant attention as a promising way to improve the MEC networks’ performances and meet the demands of next-generation networks. AAVs can act as flying edge servers, providing mobile users with flexible and on-demand computing resources. This article shows a new way to use the grey wolf optimizer (GWO) [joint deployment and task scheduling using the GWO (JDTS-GWO)] algorithm to improve both the placement of AAVs and the scheduling of tasks in a multi-AAV MEC system. The objective is to minimize the overall system’s energy consumption while meeting various constraints, such as AAV coverage, collision avoidance, and task execution requirements. The proposed approach formulates the joint optimization approach, considering the deployment of AAVs, offloading decisions, and resource allocation. An encoding scheme is proposed to represent AAV deployment and task allocation within the JDTS-GWO framework. Simulations demonstrate significant improvements in energy efficiency and task completion compared to existing benchmarks, with up to 35% energy savings and a 98% task completion rate. Sensitivity analysis confirms the approach’s scalability and robustness. The problem is modeled as a mixed-integer nonlinear programming (MINLP) problem, taking into account the consumed energy of mobile nodes, AAVs, and the MEC system. The JDTS-GWO algorithm is adapted to solve the optimization problem efficiently.