Localization Model in Wireless Sensor Networks

Abstract

位置信息在无线传感器网络应用中日益重要,针对该网络中的定位问题,提出一种新颖的基于模糊识定位模型.在该模型中,定位空间被一些样本点划分为若干个小区域,每个样本点唯一地对应一个信号向量,通算未知点信号向量与各个样本点对应向量的贴近度,可以最终确定未知点的坐标.该定位模型直接采用了射频对未知点进行定位,不但避免了一些range-based 定位模型中出现的误差叠加等问题,而且还降低了计算复杂度. 最后,借助NS-2 仿真手段对该定位模型进行了验证.结果表明,该定位模型具有较高的性能,适合无线传感器网用.;The position function is becoming more and more important for applications in WSNs, so a novel localization model, which is based fuzzy, is aimed at the localization problem. In this model, a space is divided into several small areas by some swatch nodes; every swatch node has its unique signal vector. An unknown node’s position can be calculated by computing the approach degree between the signals of the swatch and unknown nodes. This model adopts the RF signal to locate the unknown node. It cannot only avoid the overlapped error in a range-based localization model, but can also avoid the high complex. The experimental results in NS-2 show that this model performs well and is suitable for applications in WSNs.