一种移动无线传感器网络的节点位置预测方法

Node Position Prediction Method for Mobile Wireless Sensor Networks

针对目前移动无线传感器网络中现有位置预测方法的预测精度较低以及需要依靠大量的历史运动路径数据的不足,提出了一种基于不确定性支持向量机的"角度-分类"(A-USVC)位置预测方法。该方法利用节点收集的节点隶属度向量来构建归类预测模型,根据所构建的预测模型和计算的移动节点偏转方向来确定未知节点所在的区域,从而完成对移动未知节点的位置预测。仿真实验表明:在精度方面,该方法相比于传统的马尔科夫模型预测方法提高了35%,相比于神经网络预测方法提高了19%。A-USVC位置预测方法有效地提高了位置预测的精度,且计算量小,在小样本的情况下依然能保持良好的预测能力。

In view of the defects that the prediction accuracy of the existing position prediction method is low and a large number of historical movement path data need to be relied on in mobile wireless sensor network,this paper proposed an A-USVC position prediction method based on uncertain supporting vector machines.This method uses the node membership vector collected by nodes to construct classification prediction model.On the basis of the constructed prediction model and the calculated moving deflecting direction of mobile node,the location of unknown node is determined.Therefore,the position of unknown mobile node can be predicted.The simulation tests show that the proposed method improves the accuracy by 35% compared with the traditional Markov model prediction method,and improves the accuracy by 19% compared with the neural network prediction method.The A-USVC position prediction method can improve the position prediction accuracy effectively,which has low computational complexity and can also maintain good prediction ability in the case of small samples.