Distributed and Efficient One-Class Outliers Detection Classifier in Wireless Sensors Networks

Abstract

In the data mining literature, many outlier detection models can be found. However, these models are not suitable for the energy constrained WSNs because they assumed the whole data is available in a central location for further analysis. In this paper, we propose Distributed and Efficient One-class Outliers Detection Classifier (DEOODC) based on Mahalanobis Kernel used for outlier detection in wireless sensor networks (WSNs). For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. Recently, Kernel Principal component analysis (KPCA) has used for nonlinear case which can extract higher order statistics. Kernel PCA (KPCA) mapping the data onto another feature space and using nonlinear function. On account of the attractive capability, KPCA-based methods have been extensively investigated, and have showed excellent performance. Within this setting, we propose Kernel Principal Component Analysis based Mahalanobis kernel as a new outlier detection method using Mahalanobis distance to implicitly calculate the mapping of the data points in the feature space so that we can separate outlier points from normal pattern of data distribution. The use of KPCA based Mahalanobis kernel on real word data obtained from Intel Berkeley are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the One-Class SVM detection approach. All computation are done in the original space, thus saving computing time using Mahalanobis Kernel.