Energy Optimization for Computing Reuse in Unmanned Aerial Vehicle-assisted Edge Computing Systems

被引：0
作者：
Li, Bin [1 ]
Cai, Haichen [1 ]
Zhao, Chuanxin [2 ]
Wang, Junyi [3 ]
机构：
[1] School of Computer Science, Nanjing University of Information Science and Technology, Nanjing
[2] School of Computer and Information, Anhui Normal University, Wuhu
[3] School of Information and Communication, Guilin University of Electronic Technology, Guilin
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2024年 / 46卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Mobile edge computing; Resource allocation; Reusable tasks; Soft Actor-Critic (SAC) algorithm; Unmanned Aerial Vehicle (UAV);
D O I：
10.11999/JEIT231061
中图分类号：
学科分类号：
摘要：
To address the high computational performance demands of delay-sensitive tasks in complex terrains, the collaborative computation offloading scheme for reusable tasks in mobile edge computing with the assistance of Unmanned Aerial Vehicle (UAV) is proposed. Firstly, the minimization of the average total energy consumption is formulated by jointly optimizing user offloading, user transmission power, server assignment on UAV, computation frequencies of users and UAV servers, as well as UAV flight trajectory, while meeting the latency constraints. Secondly, a deep reinforcement learning approach is employed to solve the optimization problem, and a Soft Actor-Critic (SAC) based optimization algorithm is introduced. The SAC algorithm utilizes a maximum entropy policy to encourage exploration that enhances the algorithm’s exploration capabilities and accelerates the training convergence speed. Simulation results demonstrate that the proposed SAC algorithm effectively reduces the average total energy consumption of the system while exhibiting good convergence. © 2024 Science Press. All rights reserved.
引用
收藏
页码：2740 / 2747
页数：7
相关论文
共 15 条
  • [1] LIU Yaqiong, PENG Mugen, SHOU Guochu, Et al., Toward edge intelligence: Multiaccess edge computing for 5G and internet of things[J], IEEE Internet of Things Journal, 7, 8, pp. 6722-6747, (2020)
  • [2] JIA Shuxia, HAO Wanming, GAO Zihan, Et al., Resource allocation and task offloading strategy in mobile edge computing system for task migration[J], Telecommunication Engineering, 62, 8, pp. 1037-1043, (2022)
  • [3] TUN Y K, DANG T N, KIM K, Et al., Collaboration in the sky: A distributed framework for task offloading and resource allocation in multi-access edge computing[J], IEEE Internet of Things Journal, 9, 23, pp. 24221-24235, (2022)
  • [4] SUN Geng, LI Jiahui, LIU Yanheng, Et al., Time and energy minimization communications based on collaborative beamforming for UAV networks: A multi-objective optimization method[J], IEEE Journal on Selected Areas in Communications, 39, 11, pp. 3555-3572, (2021)
  • [5] HSU Y H, GAU R H., Reinforcement learning-based collision avoidance and optimal trajectory planning in UAV communication networks[J], IEEE Transactions on Mobile Computing, 21, 1, pp. 306-320, (2022)
  • [6] WANG Longjie, ZHOU Qiang, SHEN Yan, Computation efficiency maximization for UAV-assisted relaying and MEC networks in urban environment[J], IEEE Transactions on Green Communications and Networking, 7, 2, pp. 565-578, (2023)
  • [7] WANG Yong, RU Zhiyang, WANG Kezhi, Et al., Joint deployment and task scheduling optimization for large-scale mobile users in multi-UAV-enabled mobile edge computing[J], IEEE Transactions on Cybernetics, 50, 9, pp. 3984-3997, (2020)
  • [8] WANG Di, TIAN Jie, ZHANG Haixia, Et al., Task offloading and trajectory scheduling for UAV-enabled MEC networks: An optimal transport theory perspective[J], IEEE Wireless Communications Letters, 11, 1, pp. 150-154, (2022)
  • [9] ZHANG Guangchi, HE Zinan, CUI Miao, Energy consumption optimization of unmanned aerial vehicle assisted mobile edge computing systems based on deep reinforcement learning[J], Journal of Electronics & Information Technology, 45, 5, pp. 1635-1643, (2023)
  • [10] WESTERA W, PRADA R, MASCARENHAS S, Et al., Artificial intelligence moving serious gaming: Presenting reusable game AI components[J], Education and Information Technologies, 25, 1, pp. 351-380, (2020)
12