Novel abnormal data detection method in environmental wireless sensor networks

被引：0

作者：

Li, Guanghui ^{[1
]}

Pan, Yuanyang ^{[1
]}

Xu, Yongjun ^{[2
]}

机构：

[1] School of Information Engineering, Zhejiang Agriculture and Forestry University

[2] Institute of Computing Technology, Chinese Academy of Sciences

来源：

International Journal of Digital Content Technology and its Applications | 2012年 / 6卷 / 18期

关键词：

Abnormal detection; Clustering; Environmental monitoring; Sensor network;

D O I：

10.4156/jdcta.vol6.issue18.27

中图分类号：

学科分类号：

摘要：

With the development of environment monitoring technology using sensor network, abnormal data detection has drawn more attention from both the academic and industrial fields in recent years. A novel abnormal data detection method based on DBSCAN (Density-Based Spatial Clustering of Application with Noise) is proposed, which uses the distance to define the similarity of data for the cluster partitioning, and the feature set of environment can be extracted by the DBSCAN algorithm, then the abnormal data can be detected with the feature set. Experimental results on an indoor sensor network show that the proposed method can detect the abnormal data correctly and real-timely.

引用

页码：234 / 241

页数：7

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