Abnormal Data Detection Algorithm for WSN Based on K-means Clustering
In order to improve the reliability of Wireless Sensor Network(WSN) application system,it detects abnormal data from sensor environmental data set.An algorithm of abnormal data detection based on clustering of data mining is proposed in the paper,which not only adopts K-means clustering but also takes the characteristics of WSN data into account.This algorithm uses Euclidean distance to compare similarity of data for cluster partitioning,and identifies the abnormal data according to the distance between data point and cluster center.Experimental results show that when data is more than 1 000,compared with the algorithm based on Density-based Spatial Clustering of Applications with Noise (DBSCAN),the detection accuracy of this algorithm is higher and the false positive rate is lower under the same conditions.
K-means algorithmWireless Sensor Network (WSN)clusteringabnormal data detectiondensity clustering
国家科技期刊平台
NSTL
万方数据
维普
