Joint Energy-Efficient Task Scheduling and Trajectory Optimization for Multi-UAV MEC Networks in Low-Altitude Economy
Wang, Zhiying Chen, Jinghui Sun, Gang Yu, Hongfang Guizani, Mohsen
Wang, Zhiying
Chen, Jinghui
Sun, Gang
Yu, Hongfang
Guizani, Mohsen
Supervisor
Department
Machine Learning
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Type
Journal article
Date
2025
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Language
English
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Abstract
Driven by the increasing deployment of Internet of Things (IoT) devices and significant progress in Unmanned Aerial Vehicle (UAV) technologies, UAV-assisted Mobile Edge Computing (UMEC) has emerged as an effective approach to address the challenges of constrained ground network coverage and insufficient computational resources. Leveraging their mobility, flexible deployment, and low cost, UAVs can dynamically support edge networks by providing computation and communication services for latency and energy sensitive tasks. Nevertheless, task scheduling and UAV trajectory optimization in UMEC systems still face significant challenges. To address the task scheduling problem for energy-sensitive tasks in the UMEC system, this paper first formulates a mathematical model of the system’s energy consumption and establishes an optimization objective aimed to optimize the energy cost of UMEC system. Building upon this, we propose a task scheduling algorithm that integrates Multi-Agent Proximal Policy Optimization (MAPPO) with a bargaining game-based resource coordination mechanism. The proposed algorithm reduces the overall energy consumption and enhances the system performance through a bargaining-based resource allocation and matching strategy, alongside MAPPO-driven trajectory optimization. Simulation results show that our method outperforms the baseline algorithms in terms of system utility, achieving up to a 7.4% improvement.
Citation
Z. Wang, J. Chen, G. Sun, H. Yu and M. Guizani, "Joint Energy-Efficient Task Scheduling and Trajectory Optimization for Multi-UAV MEC Networks in Low-Altitude Economy," in IEEE Transactions on Cognitive Communications and Networking, doi: 10.1109/TCCN.2025.3627914
Source
IEEE Transactions on Cognitive Communications and Networking
Conference
Keywords
Deep Reinforcement Learning, Mobile Edge Computing, Task Offloading, trajectory optimization, Unmanned Aerial Vehicle
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Publisher
IEEE