| 摘要: |
| 研究了无人机雾辅助无线能量收集网络，其中无人机（unmanned aerial vehicle，简称UAV）作为可以移动的无线能量供应源和雾服务器，传感器设备可利用从UAV信号中所采集的能量完成本地计算任务或将计算任务卸载给UAV进行计算.系统目标是通过联合优化任务卸载调度，计算资源的分配和无人机飞行轨迹，在预定时间内完成给定计算任务和能量收集需求的前提下，最小化无人机的总能量消耗.为此，建立了多变量联合优化问题.由于该问题非凸，提出了一种基于连续凸近似（successive convex approximation，简称SCA）的有效求解方法.仿真结果表明，利用该联合优化方法可以大大降低无人机能耗，其中，通过轨迹优化对UAV能耗的降低效果最为明显.另外，实验结果发现：给定任务完成时间越长，无人机的轨迹越长，随着传感器设备能量收集阈值的升高或能量收集效率的降低，无人机轨迹向传感器偏移得越明显.与传感器均匀分布相比，当传感器位置全部分布在某一侧时，无人机轨迹会向其所在方向偏移. |
| 关键词: 无人机 无线能量收集 雾计算 轨迹优化 |
| DOI： |
| 分类号: |
| 基金项目:中央基本科研业务费项目（2019JBM401） |
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| UAV-assisted Wireless Energy Harvesting Fog Computing Network Optimization Method |
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ZHANG Li-Tong1, XIONG Ke1, ZHANG Yu2
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1.School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2.State Grid Energy Research Institute Co., Ltd., Beijing 102209, China
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| Abstract: |
| This paper investigates a fog-assisted wireless energy harvesting network, where the UAV acts as the mobile wireless energy source and the fog server to charge and provide computation service to the sensors simultaneously. With the harvested energy, the sensors complete their computation tasks locally or offload them to the UAV. For such a system, a total energy consumption minimization problem for the UAV by jointly optimizing the UAV's flying trajectory is formulated, the task offloading and CPU frequency subject to the tasks computing requirements and the energy harvesting requirements being satisfied. Since the problem is non-convex and with no known solution, an efficient solution method is designed on the basis of Successive convex approximation (SCA) method. Simulation results show that the UAV energy consumption can be greatly reduced by using our proposed design, and the trajectory plays a dominant factor on the energy consumption of the UAV. Moreover, the longer the given time, the longer the trajectory length of the UAV. Additionally, with the increasing of the sensors' energy harvesting threshold or the decreasing of the energy conversion efficiency, the trajectory shifts toward the sensors more obviously. Compared with the uniform distribution of sensors, when the sensors are distributed concentrated, the UAV should fly closer to the sensors. |
| Key words: UAV energy harvesting fog computing trajectory optimization |
