Outlier detection in wireless sensor networks using distributed principal component analysis

Detecting anomalies is an important challenge for intrusion detection and fault diagnosis in wireless sensor networks (WSNs). To address the problem of outlier detection in wireless sensor networks, this paper focuses on a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in the sensed data in a WSN. The outliers in the sensed data can be caused due to compromised or malfunctioning nodes. In the distributed approach, we use distributed a principal component analysis (DPCA) and fixed-width clustering (FWC) to establish a global normal pattern and to detect outlier. The process of establishing the global normal pattern is distributed among all sensor nodes. We also use weighted coefficients and a forgetting curve to periodically update the established normal profile. The proposed distributed approach in this paper achieves comparable accuracy compared to the centralized approach, while the communication overhead in the network and energy consumption is significantly reduced.