东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2009, Vol. 30 ›› Issue (3): 325-328.DOI: -

• 论著 • 上一篇    下一篇

基于欧氏距离的分布式网格定位估计方法

吴成东;贾子熙;张云洲;黄月;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-22 修回日期:2013-06-22 出版日期:2009-03-15 发布日期:2013-06-22
  • 通讯作者: Jia, Z.-X.
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60874103)

Distributed grid location estimation based on Euclidean distance

Wu, Cheng-Dong (1); Jia, Zi-Xi (1); Zhang, Yun-Zhou (1); Huang, Yue (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-22 Revised:2013-06-22 Online:2009-03-15 Published:2013-06-22
  • Contact: Jia, Z.-X.
  • About author:-
  • Supported by:
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摘要： 针对网格定位估计方法,提出一种基于欧氏距离的分布式网格定位估计的改进方法.研究了通过比较被测节点与信标节点之间的跳数信息,实现被测节点快速自身定位的方法.研究了利用欧氏距离替代匹配阈值提高定位准确率和利用分布式计算减少网络能量消耗的方法;利用迪杰斯特拉算法研究了计算节点间最小跳数的算法.仿真实验结果表明,该方法具有定位快速、节约成本、节省能量、定位准确率高和鲁棒性好的特点.

关键词: 无线传感器网络, 定位, 欧氏距离, 分布式处理, 迪杰斯特拉算法

Abstract: Based on the Euclidean distance, a distributed grid location estimation was proposed as follows to improve the original estimation. Comparing the information on hop count between the measured nodes and beacon nodes, the quick self-locating of measured nodes was implemented. How the Euclidean distance is used to substitute for the matched threshold so as to improve the accuracy of location and how to use the distributed computation to save the energy consumption of network were studied, then the Dijkstra algorithm was used to compute the minimum hop count between nodes. Simulation results showed that the improved estimation can locate the nodes quickly and accurately with energy saving at low cost especially with the high robustness provided.

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