基于神经网络的无线传感器网络异常数据检测方法

Outlier Detection Methods Based on Neural Network in Wireless Sensor Networks

传感器网络的异常数据检测对于环境监测具有十分重要的意义。基于BP神经网络模型和线性神经网络模型,分别提出了两种无线传感器网络异常数据检测方法。提出的方法在每个当前时刻通过最近的固定长度的历史数据集训练神经网络,来完成下一时刻的预报。通过神经网络的模型残差,确定概率为P的置信区间。当下一时刻数据落入置信区间内,则该数据被判为正常;反之,则为异常。为了比较和验证两种检测方法的性能,在Matlab环境下完成了仿真实验。实验结果表明,基于线性神经网络的异常数据检测方法的检测率(detection rate)达到了97.9%,误报率(false positive rate)不超过0.76%;基于BP神经网络的异常数据检测方法的检测率为96.7%,误报率不超过0.84%。

Outlier detection in wireless sensor network（WSN）is of great significance for environmental monitoring.Two outlier detection methods for WSNs were proposed based on BP neural network and linear neural network in this paper.Latest historical data with fixed length of data window was used to train a neural network model,and then these methods can predict the sensor data of the next time.A confidence interval with probability p was calculated with the help of the model residual.The new measurement will be identified as normal one if it falls inside the prediction interval.Otherwise,it will be classified as an abnormal record.In order to compare and demonstrate the performance of the proposed methods,we finished the simulation experiments in Matlab environment.The experiment results show that the detection rate of outlier detection based on linear neural network reaches 97.9%,and the false positive rate is less than0.76%.While the detection rate of outlier detection based on BP neural network reaches 96.7%,the false positive rate is less than 0.84%.