Optimizing cost through UAV deployment and task assignment in hybrid UAV-assisted MEC systems

Abstract

Unmanned Aerial Vehicle (UAV) technology has become a significant component in Mobile Edge Computing (MEC) systems. By integrating MEC servers with UAVs, efficient computing and communication services can be delivered in emergency environments, such as post-disaster emergency rescues and in remote mountainous regions. However, the integration of MEC servers with UAVs inevitably increases Capital Expenditures (CapEx). Furthermore, the UAV, burdened with the MEC server, must hover to provide computing and communication services, leading to heightened energy consumption. To address the challenges of optimizing UAV deployment costs and energy consumption, we propose a UAV-assisted MEC framework employing both traditional Transmission UAVs (T-UAVs) and MEC-enabled Computing UAVs (C-UAVs). By jointly optimizing UAV deployment, task assignment, and computing resource allocation, we formulate a problem aimed at minimizing the system’s Total Cost (TC), encompassing both CapEx and the Operational Expenditures (OpEx) associated with UAV energy consumption. To tackle this problem, we introduce a Bi-Level Alternative Optimization (BLAO) algorithm to derive the solution, with the upper-level addressing UAV deployment and the lower-level focusing on task assignment and computing resource allocation. Simulation results demonstrate that our algorithm consistently outperforms existing benchmark solutions across diverse scenarios.