Approximation algorithm for MinSum linear barrier coverage with sink-based mobile sensors on the plane

被引:3
|
作者
Zou, Wenjie [1 ,2 ]
Guo, Longkun [2 ,3 ]
Hao, Chunlin [4 ]
Liu, Lei [4 ]
机构
[1] Jiangxi Sci & Technol Normal Univ, Coll Math & Comp Sci, Nanchang 330038, Peoples R China
[2] Fuzhou Univ, Coll Math & Stat, Fuzhou 360116, Peoples R China
[3] Qilu Univ Technol, Sch Comp Sci, Jinan 250316, Peoples R China
[4] Beijing Univ Technol, Dept Operat Res & Informat Engn, Beijing 100124, Peoples R China
基金
中国国家自然科学基金;
关键词
Barrier coverage; Mobile sensor; Sink station; Geometrically tangent segments; Auxiliary graph; DEPLOYMENT; NETWORKS;
D O I
10.1016/j.tcs.2022.10.046
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Emerging wireless and mobile applications, such as border intrusion detection with station -based drones, brought a new barrier coverage problem of using sink-based mobile sensors to cover a given line barrier with minimum energy consumption. In this paper, we focus on the uniform sink-based line barrier coverage (SLBC) problem, in which we are given a line barrier and k sink stations distributed on the plane which can emit an infinite number of sensors with an identical sensing radius. The problem aims to find their final positions on the barrier for the sensors emitted by the stations, such that the total moving distance of the sensors is minimized and each point of the barrier is within the sensing area of at least one sensor. We first observe the geometric structure of an optimal solution that any optimal solution can be considered as a set of intersecting tangent (disk) segments, where a tangent (disk) segment is a sequence of tangent disks. Then, we devise an algorithm to calculate all possible tangent (disk) segments and another algorithm to calculate the near -optimal positions for each of such segments. After computing all tangent (disk) segments and their near-optimal positions, an algorithm is proposed to transform uniform SLBC into an instance of the shortest path problem. It is shown the whole algorithm deserves a runtime O (k2 logkr epsilon) and consumes at most epsilon more movement than an optimal solution, where epsilon is any given positive real number, and r and k are the sensor radius and the number of sink stations, respectively. (c) 2022 Published by Elsevier B.V.
引用
收藏
页码:121 / 130
页数:10
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