A PTAS to minimize mobile sensor movement for target coverage problem

Zhiyin Chen, Xiaofeng Gao, Fan Wu, Guihai Chen
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引用次数: 25

Abstract

Energy consumption is a fundamental and critical issue in wireless sensor networks. Mobile sensors consume much more energy during the movement than that during the communication or sensing process. Thus how to schedule mobile sensors and minimize their moving distance has great significance to researchers. In this paper, we study the target coverage problem in mobile sensor networks. Our goal is to minimize the moving distance of sensors to cover all targets in the surveillance region. Here initially all the sensors are located at k base stations. Thus we define this problem as k-Sink Minimum Movement Target Coverage. To solve this problem, we propose a PTAS, named Energy Effective Movement Algorithm (EEMA). We can divide EEMA into two phases. In the first phase, we partition the surveillance region into some subareas. In the second phase, we select subareas and schedule sensors to the selected subareas. We also prove that the approximation ratio of EEMA is 1 + ε and the time complexity is ηO(1/ε2 Finally, we conduct experiments to validate the efficiency and effectiveness of EEMA.
针对目标覆盖问题,采用PTAS最小化移动传感器运动
在无线传感器网络中,能量消耗是一个基本而关键的问题。移动传感器在移动过程中所消耗的能量远远大于通信或感知过程所消耗的能量。因此,如何调度移动传感器,使其移动距离最小,对研究人员具有重要意义。本文主要研究移动传感器网络中的目标覆盖问题。我们的目标是最小化传感器的移动距离,以覆盖监视区域内的所有目标。这里最初所有的传感器都位于k个基站。因此我们将此问题定义为k-Sink最小运动目标覆盖。为了解决这一问题,我们提出了一种PTAS,即能量有效运动算法(Energy Effective Movement Algorithm, EEMA)。我们可以将EEMA分为两个阶段。在第一阶段,我们将监测区域划分为若干子区域。在第二阶段,我们选择子区域并将传感器调度到所选的子区域。并证明了EEMA的近似比为1 + ε,时间复杂度为ηO(1/ε2),最后通过实验验证了EEMA的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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