Distributed fermat-point location estimation for wireless sensor network applications

Jiann-Liang Chen, Ming-Chiao Chen, Tsui-Lien Chiang, Yao-Chung Chang, F. Shih
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引用次数: 3

Abstract

This paper presents a distributed fermat-point range estimation strategy, which is important in the moving sensor localization applications. The fermat-point is defined as a point which minimizes the sum of distances from three sensors inside a triangle. This point is indeed at the trianglepsilas center of gravity. We solve the problems of large errors and poor performance in the bounding box algorithm. We obtain two results by performance analysis for a deployed environment with 200 sensor nodes. First, when the number of sensor nodes is below 150, the mean error decreases rapidly as the node density increases, and when the number of sensor nodes exceeds 170, the mean error stays below 1%. Second, when the number of beacon nodes is below 60, the normal nodes do not have sufficient number of accurate beacon nodes to help them estimate their locations. However, when the number of beacon nodes exceeds 60, the mean error changes slightly. Simulation results indicated that the proposed algorithm for sensor position estimation is more accurate than existing algorithms and improves on existing bounding box strategies.
无线传感器网络应用中的分布式发声器点定位估计
本文提出了一种在运动传感器定位应用中具有重要意义的分布式发马点距离估计策略。费马点的定义是使三角形内三个传感器的距离之和最小的点。这个点确实在三角形重心处。解决了边界盒算法误差大、性能差的问题。通过对具有200个传感器节点的部署环境的性能分析,我们得到了两个结果。首先,当传感器节点数小于150时,随着节点密度的增加,平均误差迅速减小,当传感器节点数大于170时,平均误差保持在1%以下。其次,当信标节点数低于60时,正常节点没有足够数量的准确信标节点来帮助它们估计自己的位置。但是,当信标节点数超过60时,平均误差变化不大。仿真结果表明,该算法比现有的传感器位置估计算法更准确,并且对现有的包围盒策略进行了改进。
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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