基于无线传感器网络和线性神经网络的事件边界检测方法∗

摘要

Environmental monitoring is a typical application in wireless sensor network ( WSN ) , and event boundary detection is important for environmental monitoring. In this paper, a temporal-spatial data model of WSN is established, and then an event boundary detection method based on the linear neural network is presented. Firstly, the temporal correlation of data stream is analyzed, and the abnormal data set is determined based on linear neural network technique. Then, the event boundary is detected by using the spatial correlation of data stream between the neighbor nodes, and both the fault nodes and the event boundary nodes can be found. Thus, the location and the size of the event region can be estimated. Theoretical analysis and experimental results show that the proposed method has a high accuracy of fault node and event boundary detection and a low false positive rate.%环境监测是无线传感器网络的典型应用，事件边界检测是其中的重要内容。文中首先建立无线传感器网络数据的时空模型，提出基于线性神经网络的事件边界检测方法。该方法利用传感器数据流的时间相关性，基于线性神经网络预测与验证数据流，并确定异常数据集合。在此基础上，根据传感器节点之间的空间相关性进行事件边界检测，不仅可识别故障节点，而且能识别事件边界节点，从而准确估算事件发生的区域位置与大小。理论分析及实验表明，文中方法在获得较高的故障节点和事件边界节点的检测准确率的同时，保持较低的误判率。