无人机辅助传感器网络中吞吐量与节点能量优化方法

A Throughput and Nodes Energy Optimization Method in UAV-assisted Sensor Network

无人机凭借其高机动性可以对传感节点进行无线数据采集和无线供能,然而无线数据采集与无线供能是相互耦合的,如何对两者的资源分配和无人机飞行轨迹进行优化是无人机辅助无线供能传感器网络性能的关键。为此,提出了一种联合优化方案,通过交替优化节点接入、无人机三维飞行轨迹和能量数据传输时间分配,最大化最小节点效用。针对无线供能传感器网络模型中最大化最小节点效用问题提出了一个联合优化方案。由于该问题为非凸问题,提出了一个最大化最小节点效用算法(Maximized the Minimum Node Utility Algorithm,MMNUA),采用块坐标下降法将原始问题转化成4个子问题,并利用连续凸近似技术在每次迭代时依次优化4个子问题,使最大化最小节点效用逐渐收敛。与基础无人机方案相比,MMNUA使系统性能分别提升了14.1%、567.5%和177.0%,有效提高了无人机的工作效率,同时所提算法具有良好的收敛性和较小的复杂度。

With its high mobility,unmanned aerial vehicles(UAVs)can perform wireless data acquisition and wireless power transmission to sensor nodes,but wireless data acquisition and wireless power transmission are coupled to each other,and how to optimize their resource allocation and the flight trajectory is the key to improve the UAV-assisted wireless powered sensor network performance.Therefore,a joint optimization scheme is proposed,which maximizes the minimum node utility by alternately optimizing node access,UAV three-dimensional flight trajectory,energy and data transmission time allocation respectively.A joint optimization scheme is proposed for maximizing the minimum node utility in the wireless powered sensor networks(WPCN)model.Since the problem is non-convex,a maximized the minimum node utility algorithm(MMNUA)is proposed,which uses the block coordinate descent method to transform the original problem into four subproblems,and uses successive convex approximation(SCA)technology to optimize the four subproblems in each iteration,so that the maximized minimum node utility gradually converges.Compared with the basic UAV scheme,the MMNUA scheme improves the system performance by 14.1%,567.5%and 177.0%,respectively,which effectively improves the work efficiency of the UAV,and the proposed algorithm has good convergence and less complexity.