一种基于RBF神经网络的无线传感器网络定位模型

RBF Neural Network Based Localization Model for Wireless Sensor Networks

在无线传感器网络覆盖区域内的不同位置采集信号强度值,利用径向基函数(RBF)神经网络建立信号强度到节点坐标之间的映射模型,将采集到的信号强度值作为神经网络的输入矢量进行训练,利用训练好的神经网络实现未知节点的定位。实验结果表明,该模型具有较好的定位精度,其平均定位误差低于10%。

We collect signal strength at different positions in wireless sensor networks covering areas.We also employ Radial Basis Function（RBF） neural network to construct a mapping model between signal strength and the coordinate of a node.We then train such a neural network with the collected signal strength value（an input vector） before use the trained neural network to localize an unknown node.Experimental results show that this model has higher accuracy,whose average localization error is less than 10%.