用于桥梁监测的无线充电传感器网络的无人机调度规划

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE IEEE Transactions on Sustainable Computing Pub Date : 2022-11-24 DOI:10.1109/TSUSC.2022.3224442
Chuanxin Zhao;Yang Wang;Xin Zhang;Siguang Chen;Changzhi Wu;Kok Lay Teo
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引用次数: 1

摘要

由于无线电力传输技术的突破,无线可充电传感器网络(WRSN)具有提供可持续工作的潜力。现有的大多数WRSN研究通常集中在移动充电车通过传感器自由移动的情况下。然而,对于一些应用,如桥梁监测,WRSN是在有障碍物的三维空间中实现的,因此充电路径可能会被障碍物阻挡。针对这一问题,研究了无人机桥梁监测无线充电传感器网络的充电调度问题。通过协同优化无人机导航路径和传感器能量分配,将该问题表述为一个优化问题。这个优化问题很难解决,因为路径导航和能量分配都需要同时优化。为了规避这一挑战,提出了一种改进的蚁群系统算法(IM-ACS)来规划无人机在传感器之间的轨迹。通过综合增强因子和动态信息素强度系数,加快了算法的收敛速度。然后,提出了一种两阶段算法来调度充电序列,并在每个充电周期分配无人机携带的有限能量的能量。实验和仿真表明,与比较方法相比,该方法实现了更短的可行轨迹路径和更长的网络寿命。
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UAV Dispatch Planning for a Wireless Rechargeable Sensor Network for Bridge Monitoring
Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.
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来源期刊
IEEE Transactions on Sustainable Computing
IEEE Transactions on Sustainable Computing Mathematics-Control and Optimization
CiteScore
7.70
自引率
2.60%
发文量
54
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