An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks

Chen Ji-feng; Liu Guang-cong; Peng Cheng-ping

doi:10.12052/gdutxb.150128

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An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks

更新时间：2026-05-27

- An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks
- Vol. 34, Issue 2, Pages: 80-85,102(2017)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.150128
  CLC： TP393
- Online First：09 March 2017，
  
  Published：2017
- 稿件说明：
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陈继峰, 刘广聪, 彭成平. 一种改进的无线传感器网络DV-Hop定位算法[J]. 广东工业大学学报, 2017, 34(2): 80-85,102.

Chen Ji-feng, Liu Guang-cong, Peng Cheng-ping. An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks[J]. 2017, 34(2): 80-85,102.
陈继峰, 刘广聪, 彭成平. 一种改进的无线传感器网络DV-Hop定位算法[J]. 广东工业大学学报, 2017, 34(2): 80-85,102. DOI： 10.12052/gdutxb.150128.

Chen Ji-feng, Liu Guang-cong, Peng Cheng-ping. An Improved DV-Hop Localization Algorithm for Wireless Sensor Networks[J]. 2017, 34(2): 80-85,102. DOI： 10.12052/gdutxb.150128.

摘要

针对传统的DV-Hop定位算法的定位误差大、参与定位的信标节点选取不合理问题，提出一种改进的方法，将共线度方法运用在信标节点组的选取上，使其共线度满足预定的范围.选出所有满足共线度范围的信标节点组，在每个组合中，判断信标节点与未知节点之间的位置关系.利用信标节点之间的真实距离，通过任意两个估计距离值对未知节点进行初步的定位.在每组中，采用多次循环选取两个估计值获得多个未知节点估计位置，通过数据挖掘领域内的聚类分析方法对定位结果进行优化，获得最终的未知节点估计坐标.仿真实验表明，在未增加额外节点硬件开销的情况下，改进的算法比改进前的算法更有效地减少定位误差，提高了定位的准确性，同时，不受个别定位误差较大对最终结果的影响，健壮性较强.

Abstract

Aiming at the big estimation error and the unreasonable selection to the participant positioning beacon nodes in the traditional DV-Hop localization algorithm

an improved DV-Hop localization algorithm for wireless sensor networks is proposed. The degree of collinearity method is used in the selection of the beacon node group. As long as the degree of collinearity of the beacon node group meets a predetermined range

the beacon node group will be selected. In each of these groups

the position relationship between beacon nodes and unknown nodes is discerned

the real distance between beacon nodes made full use of

and the localization of unknown nodes made by using any two estimated values

with a number of cycles in each group selecting two estimates to get a lot of unknown nodes estimate position

and finally the estimated coordinates of unknown nodes are obtained by clustering analysis in the field of data mining. The simulation shows that the improved algorithm can reduce the localization error and improve the accuracy of the algorithm and robustness without any increase in the hardware overhead

and at the same time

it is insensitive to some of the larger errors in the final result.

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references

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