Anomaly Localization by Joint Sparse PCA in Wireless Sensor Networks

Principal Component Analysis based anomaly detection approaches have been extensively studied recently. However, none of these approaches address the problem of anomaly localization. In this paper, we proposed a novel approach based on PCA to perform anomaly detection and localization in sensor networks simultaneously. By enforcing the joint sparsity across the Principal Components in the abnormal subspace, we can accurately localize the abnormal sensor nodes from normal nodes. We demonstrate the localization performance in the experimental study on two real world data sets.