广东工业大学学报 ›› 2015, Vol. 32 ›› Issue (04): 127-131.doi: 10.3969/j.issn.1007-7162.2015.04.023

• 综合研究 • 上一篇    下一篇

无线传感器网络自定位算法的研究

黄婷婷,刘广聪,陈海南   

  1. 广东工业大学 计算机学院,广东 广州 510006
  • 收稿日期:2014-04-11 出版日期:2015-12-04 发布日期:2015-12-04
  • 作者简介:黄婷婷(1990-),女,硕士研究生,主要研究方向为无线传感器网络.
  • 基金资助:

    广州市科技计划项目(201508020030);广州市中小企业技术创新基金资助项目(2013J4400159)

Self-localization Algorithm of Wireless Sensors Network

Huang Ting-ting, Liu Guang-cong, Chen Hai-nan   

  1. School of Computers, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2014-04-11 Online:2015-12-04 Published:2015-12-04

摘要: 针对无线传感器网络节点稀疏时会产生定位盲区的问题,在研究经典AOA(三角测量法)算法的基础上,提出了一种改进的无线传感器网络自定位算法,在节点自身基准线相对于绝对坐标轴夹角已知的情况下,未知节点只需一个邻居锚节点就能实现自定位.仿真实验证明,在节点数相同的情况下,改进的无线传感器网络自定位算法相比于经典AOA算法,有更高的有效定位率和更低的平均定位误差,适用于节点稀疏的网络环境.

关键词: 自定位; 三角测量法; 稀疏网络; 定位盲区

Abstract: In order to solve the problem of positioning blind areas in case of sparse nodes, an improved self-localization algorithm of wireless sensors network is proposed based on the traditional AOA algorithm (triangulation method). By applying this new algorithm, the unknown node needs only one neighbor anchor node before realizing the positioning when the angle of the node’s baseline and the absolute coordinate axis is given. Simulation experiments prove that on the condition of the same amount of nodes the improved self-localization algorithm of wireless sensor network enjoys higher positioning efficiency and lower average localization error compared with the traditional AOA algorithm. Therefore, the improved algorithm is applicable in the network environment of sparse nodes.

Key words: self-localization; triangulation; sparse network; blind area of locating

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