Han Zihao, Zhou Ting, Xu Tianheng, Hu Honglin
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
Entropy (Basel). 2023 Sep 7;25(9):1304. doi: 10.3390/e25091304.
Unmanned aerial vehicles (UAVs) providing additional on-demand communication and computing services have become a promising technology. However, the limited energy supply of UAVs, which constrains their service duration, has emerged as an obstacle in UAV-enabled networks. In this context, a novel task offloading framework is proposed in UAV-enabled mobile edge computing (MEC) networks. Specifically, heterogeneous UAVs with different communication and computing capabilities are considered and the energy consumption of UAVs is minimized via jointly optimizing user association and UAV deployment. The optimal transport theory is introduced to analyze the user association sub-problem, and the UAV deployment for each sub-region is determined by a dragonfly algorithm (DA). Simulation results show that the energy consumption performance is significantly improved by the proposed algorithm.
提供额外按需通信和计算服务的无人机已成为一项很有前景的技术。然而，无人机有限的能量供应限制了它们的服务时长，这已成为无人机网络中的一个障碍。在此背景下，在基于无人机的移动边缘计算（MEC）网络中提出了一种新颖的任务卸载框架。具体而言，考虑了具有不同通信和计算能力的异构无人机，并通过联合优化用户关联和无人机部署，将无人机的能量消耗降至最低。引入最优传输理论来分析用户关联子问题，并通过蜻蜓算法（DA）确定每个子区域的无人机部署。仿真结果表明，所提算法显著提高了能量消耗性能。
