An energy-aware drone trajectory planning scheme for terrestrial sensors localization

被引:11
|
作者
Kouroshnezhad, Sahar [1 ]
Peiravi, Ali [1 ]
Haghighi, Mohammad Sayad [2 ]
Jolfaei, Alireza [3 ]
机构
[1] Ferdowsi Univ Mashhad, Elect Engn Dept, Mashhad, Razavi Khorasan, Iran
[2] Univ Tehran, Sch Elect & Comp Engn, Tehran, Iran
[3] Macquarie Univ, Dept Comp, N Ryde, NSW, Australia
关键词
Drone; Energy efficiency; Range-based localization; RSS measurement; Terrestrial sensors; Sensor networks; Trajectory planning; WIRELESS; NETWORKS;
D O I
10.1016/j.comcom.2020.02.055
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Employing GPS-equipped drones to act as mobile anchors is a popular solution for terrestrial sensors positioning in a generic environment. Researchers have proposed several approaches, usually to reduce estimated locations error and increase localization coverage, though no efficient solution has been presented for energy conservation of the drone. Drones, such as the commercial quadcopters, have limited power supply and cannot fly long. Any localization algorithm should consider the energy constraints beside the performance indicators. Furthermore, there is no suggested strategy to mitigate the error of Received Signal Strength (RSS)-based distance measurements in the existing solutions. In this paper, we propose a novel scheme to plan the drone trajectory, called the "Weighted Energy-aware Trajectory with Adaptive Radius (WETAR)". The proposed scheme employs Linear Programming (LP) for trajectory planning in the presence of the sensors with given estimative regions which are acquired in a range-free pre-localization phase. It also specifies candidate waypoints, which are the projection of aerial anchor points on the ground, and assign weights to them based on two criteria: quality of the beacons that the sensors would receive and their coverage ratio. We assume that sensors utilize a range-based localization algorithm on the basis of RSS measurements. Simulation results show that the WETAR as an aerial anchor guiding mechanism, guides the drone effectively and reduces localization time, saves the drone energy, and improves the location error as well as the localization coverage.
引用
收藏
页码:542 / 550
页数:9
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