Shahrood University of Technology Journal of AI and Data Mining 2322-5211 1 1 2013 01 28 Outlier Detection in Wireless Sensor Networks Using Distributed Principal Component Analysis 1 11 98 10.22044/jadm.2013.98 EN Mohammad Ahmadi Livani Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran Mahdi Abadi Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran Meysam Alikhany Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran Meisam Yadollahzadeh Tabari Department of Computer Eng. & IT, Shahrood University of Technology,Shahrood,Iran Journal Article 2013 01 09 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, in this paper we present a PCA-based centralized approach and a DPCA-based distributed energy-efficient approach for detecting outliers in sensed data in a WSN. The outliers in sensed data can be caused due to compromised or malfunctioning nodes. In the distributed approach, we use distributed principal component analysis (DPCA) and fixed-width clustering (FWC) in order 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. We demonstrate that the proposed distributed approach achieves comparable accuracy compared to the centralized approach, while the communication overhead in the network and energy consumption is significantly reduced. https://jad.shahroodut.ac.ir/article_98_581145b2adcc49bf156eee44c1df7703.pdf