Abstract: In response to the computational needs of intelligent applications, multi-access edge computing (multi-access edge computing,MEC) provides computational services at the network edge. Unmanned aerial vehicles (unmanned aerial vehicle,UAV) possess excellent maneuverability and can serve as temporary aerial edge nodes to provide edge services for ground users within the MEC environment. However, the MEC environment is complex and dynamically variable, posing challenges in formulating suitable service strategies for multiple UAVs. Additionally, many existing UAV-MEC related works assume a fixed flight altitude for UAVs, operating within a two-dimensional plane, neglecting the importance of flight altitude. Under the premise of co-channel interference, this paper maximizes task completion through energy efficiency optimization, enabling multiple UAVs to collaborate in three-dimensional space to provide computational services for ground users. To achieve energy efficiency optimization goals, maximizing task completion while minimizing flight energy consumption requires the formulation of optimal flight strategies, sub-channel selection strategies, and task scheduling strategies. Based on the multi-agent deep deterministic policy gradient algorithm (multi-agent deep deterministic policy gradient,MADDPG), this paper proposes a curiosity-driven and twin-networks-structured multi-agent deep deterministic policy gradient algorithm (curiosity-driven and twin-networks-structured MADDPG,CTMADDPG) to address the aforementioned optimization issues, promoting agent state exploration through internal rewards and avoiding convergence to suboptimal strategies. Simultaneously, the use of dual critic networks reduces the probability of Q-value overestimation, achieving stable updates. Simulation results indicate that the CTMADDPG algorithm excels in maximizing overall system energy efficiency, outperforming other benchmark algorithms. Keywords:Multi-access edge computing; Multi-agent reinforcement learning; Unmanned aerial vehicle; Task schedulingAuthors:
| Li Yang1, Wei Ziling1, Su Jinshu1,2, Zhao Baokang1
Affiliation: 1National University of Defense Technology, College of Computer Science, Changsha, China,410073 2Academy of Military Sciences, Beijing, China,100091
Reference format: Yang LI, Ziling WEI, Jinshu SU, Baokang ZHAO, 2024. A multi-agent collaboration scheme for energy-efficient task scheduling in a 3D UAV-MEC space. Frontiers of Information Technology & Electronic Engineering, 25(6):824-838.https://doi.org/10.1631/FITEE.2300393 |
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About this Journal
Frontiers of Information Technology & Electronic Engineering (abbreviated as FITEE, Chinese name 《信息与电子工程前沿（英文）》, ISSN 2095-9184, CN 33-1389/TP) is a comprehensive English academic monthly journal in the field of information electronics, indexed in SCI-E and EI, with the latest impact factor of 2.7, ranked in the JCR Q2 zone. It was formerly known as the English edition C of the Journal of Zhejiang University: Computer and Electronics, founded in 2010, and was renamed in 2015. It is now a sub-journal of the Chinese Academy of Engineering’s journal in the field of information and electronic engineering. It covers fields such as computer science, information and communication, control, electronics, and optics. Article types include research papers, reviews, personal viewpoints, and commentaries. The current editor-in-chief is Academician Pan Yunque and Fei Aiguo of the Chinese Academy of Engineering. The journal implements an international peer review system, with initial feedback generally provided within 2-3 months. Once accepted, articles will be published online quickly.
In 2019, it was funded by the Excellent Action Plan for Chinese Science and Technology Journals launched by the China Association for Science and Technology and other seven ministries (Tiered Journals).From 2021 to 2022, it was selected for the high-quality science and technology journal grading directory in the information communication field (organized by the China Communication Society) and the computing field (organized by the China Computer Society), both listed as the highest T1 level; and selected for the recommended international academic conference and journal directory of the China Computer Society – 2022 (cross-disciplinary/comprehensive/emerging).
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