Zuo Weihao, Xian Yongju
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Sensors (Basel). 2024 Jul 4;24(13):4337. doi: 10.3390/s24134337.
This study investigates the dynamic deployment of unmanned aerial vehicles (UAVs) using edge computing in a forest fire scenario. We consider the dynamically changing characteristics of forest fires and the corresponding varying resource requirements. Based on this, this paper models a two-timescale UAV dynamic deployment scheme by considering the dynamic changes in the number and position of UAVs. In the slow timescale, we use a gate recurrent unit (GRU) to predict the number of future users and determine the number of UAVs based on the resource requirements. UAVs with low energy are replaced accordingly. In the fast timescale, a deep-reinforcement-learning-based UAV position deployment algorithm is designed to enable the low-latency processing of computational tasks by adjusting the UAV positions in real time to meet the ground devices' computational demands. The simulation results demonstrate that the proposed scheme achieves better prediction accuracy. The number and position of UAVs can be adapted to resource demand changes and reduce task execution delays.
本研究探讨了在森林火灾场景中利用边缘计算进行无人机（UAV）的动态部署。我们考虑了森林火灾的动态变化特征以及相应的不同资源需求。基于此，本文通过考虑无人机数量和位置的动态变化，对双时间尺度无人机动态部署方案进行了建模。在慢时间尺度上，我们使用门控循环单元（GRU）来预测未来用户数量，并根据资源需求确定无人机数量。相应地更换能量较低的无人机。在快时间尺度上，设计了一种基于深度强化学习的无人机位置部署算法，通过实时调整无人机位置来满足地面设备的计算需求，从而实现计算任务的低延迟处理。仿真结果表明，所提方案具有更好的预测精度。无人机的数量和位置能够适应资源需求变化并减少任务执行延迟。
