Distributed refinement algorithm for WSN localization

Ying-biao YAO; Nan-lan JIANG

doi:10.11959/j.issn.1000-436x.2015020

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Distributed refinement algorithm for WSN localization

Academic paper | 更新时间：2024-06-05

- Distributed refinement algorithm for WSN localization
- Journal on Communications Vol. 36, Issue 1, Pages: 179-188(2015)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州 310018
  2. 东南大学移动通信国家重点实验室，江苏南京 211189
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61100044)
- DOI：10.11959/j.issn.1000-436x.2015020
  CLC： TP393
- Online First：2015-01，
  
  Published：25 January 2015
- 稿件说明：
移动端阅览
Ying-biao YAO, Nan-lan JIANG. Distributed refinement algorithm for WSN localization[J]. Journal on Communications, 2015, 36(1): 179-188.
DOI：

Ying-biao YAO, Nan-lan JIANG. Distributed refinement algorithm for WSN localization[J]. Journal on Communications, 2015, 36(1): 179-188. DOI： 10.11959/j.issn.1000-436x.2015020.

摘要

通过对WSN中基于测距的定位进行误差分析提出一种求精算法。该算法每次迭代中首先根据“磁极”思想确定“误差节点”和“有效节点”；然后在误差节点的邻居节点中选择2个相对偏差最小的节点作为圆心，以它们到误差节点的测距值为半径分别作圆，得到2个交点；最后在它的当前定位位置和这2个交点之中选择误差较小的作为本轮的求精位置。仿真结果表明，该算法能够降低多边定位模型产生的节点位置误差，有效提高网络的定位精度。

Abstract

A distributed refinement algorithm was proposed based on the error analyzing of range-based WSN localization.In each iteration

the proposed algorithm identified “error nodes” and “effective nodes” according to the idea of“magnetic pole”at first; then

it choosed two nodes with the minimum relative deviations in the 1-hop neighbors set of error node

and getted the intersection points of two circles whose centers were the positions of above two nodes and radiuses are the range distances from above two nodes to the error node respectively; at last

it choosed the position with minimum error

from its current position and above two intersection points as refinement position of error node after this iteration.Simulation results show that the proposed algorithm can reduce the localization error of multilateral positioning model and improve the localization accuracy efficiently.

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references

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