基于约简策略与自适应SVDD的无线传感网络离群检测方法

Outlier detection in wireless sensor networks based on reduction strategy and adaptive SVDD

Wireless sensor networks are often deployed in the harsh and unattended environment,and the sensor data loss or error usually happens for the sake of bad weather,hardware or software fault,energy dissipation or the ad-verse attack. Outlier detection of the sensor data streams is critical for improving the system's availability. In this pa-per,an outlier detection method( RASVDD) based on the data reduction and adaptive SVDD is proposed. RASVDD uses the Mahalanobis distance criterion to reduce the data set and the training samples,and then the data distribu-tion density criterion and the temporal correlation of data stream are applied to update the training model adaptively. The simulation results for the Intel Berkeley dataset and the SensorScope System dataset showed that,RASVDD had an average true positive rate of 98% and an average false positive rate of 1%,and reduced the model training time more than 20% compared to traditional SVDD.