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计算机工程 ›› 2019, Vol. 45 ›› Issue (1): 78-83,90. doi: 10.19678/j.issn.1000-3428.0048166

• 移动互联与通信技术 • 上一篇    下一篇

基于Voronoi图划分的节点模糊信息定位算法

李芬芳1,党小超1,2,郝占军1,2   

  1. 1.西北师范大学 计算机科学与工程学院,兰州 730070; 2.甘肃省物联网工程研究中心,兰州 730070
  • 收稿日期:2017-07-29 出版日期:2019-01-15 发布日期:2019-01-15
  • 作者简介:李芬芳(1990—),女,助教、硕士,主研方向为无线传感器网络;党小超,教授;郝占军,副教授
  • 基金资助:

    国家自然科学基金(61762079,61363059,61662070);甘肃省科技重点研发项目(1604FKCA097,17YF1GA015);甘肃省科技创新项目(17CX2JA037,17CX2JA039)

Node Fuzzy Information Localization Algorithm Based on Voronoi Diagram Partition

LI Fenfang 1,DANG Xiaochao 1,2,HAO Zhanjun 1,2   

  1. 1.College of Computer Science and Engineering,Northwest Normal University,Lanzhou 730070,China; 2.Gansu Province Internet of Things Engineering Research Center,Lanzhou 730070,China
  • Received:2017-07-29 Online:2019-01-15 Published:2019-01-15

摘要:

针对基于接收信号强度的无线传感器网络节点定位算法精度低的问题,提出一种基于Voronoi图划分的节点模糊信息定位算法。根据锚节点个数对定位区域进行Voronoi图划分,将整个定位区域划分为不同的Voronoi区域,同时获得各个Voronoi区域的顶点坐标。使用高斯滤波方法筛选出可以作为参考节点的顶点坐标,通过顶点坐标和锚节点联合定位未知节点。利用模糊信息定位方法计算出未知节点的最终位置。实验结果表明,相比MANLFI算法和FINL-DT算法,该算法能够有效提高节点定位精度,降低网络能耗。

关键词: 节点定位, Voronoi图划分, 模糊信息, 高斯滤波, 定位精度

Abstract:

In order to solve the problem of low accuracy of node localization algorithm based on Received Signal Strength Indicator(RSSI) in wireless sensor network,a node fuzzy information location algorithm based on Voronoi graph partition is proposed named NFIL-VD.According to the number of anchor nodes,the location area is divided into different Voronoi cell,and the vertex coordinates of each Voronoi cell are obtained.The vertex nodes which can be used as reference nodes are selected by the Gaussian filtering method,and the unknown nodes are located jointly by vertex nodes and anchor nodes.The final location of unknown nodes is calculated by fuzzy information location method.Experimental results show that compared with MANLFI algorithm and FINL-DT algorithm,this algorithm can effectively improve node localization accuracy and reduce network energy consumption.

Key words: node localization, Voronoi diagram partition, fuzzy information, Gaussian filtering, localization accuracy

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