@Article{cmc.2024.055614,
AUTHOR = {Zeshuang Song, Xiao Wang, Qing Wu, Yanting Tao, Linghua Xu, Yaohua Yin, Jianguo Yan},
TITLE = {A Task Offloading Strategy Based on Multi-Agent Deep Reinforcement Learning for Offshore Wind Farm Scenarios},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {81},
YEAR = {2024},
NUMBER = {1},
PAGES = {985--1008},
URL = {http://www.techscience.com/cmc/v81n1/58347},
ISSN = {1546-2226},
ABSTRACT = {This research is the first application of Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers to offshore wind farms, providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms. The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally, which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal. Finally, the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem, and a task offloading model based on Multi-Agent Deep Reinforcement Learning (MADRL) is established. The Adaptive Genetic Algorithm (AGA) is used to explore the action space of the Deep Deterministic Policy Gradient (DDPG), which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space. The simulation results show that the proposed algorithm, AGA-DDPG, saves approximately 61.8%, 55%, 21%, and 33% of the overall overhead compared to local MEC, random offloading, TD3, and DDPG, respectively. The proposed strategy is potentially important for improving real-time monitoring, big data analysis, and predictive maintenance of offshore wind farm operation and maintenance systems.},
DOI = {10.32604/cmc.2024.055614}
}