Abstract
Wireless sensor networks (WSNs) are important platforms for collecting environmental data and monitoring phenomena. So, outlier detection process is a necessary step in building sensor network systems to assure data quality for perfect decision making. Over the last few years Kernel Principal Component Analysis (KPCA) is considered as a natural nonlinear generalization of PCA, which extracts nonlinear structure from the data. Wireless sensor networks had been deployed in the real world to collect large amounts of raw sensed data. Then, the key challenge is to extract high level knowledge from such raw data. So, the accuracy of sensor readings is without a doubt one of the most important measures to evaluate the quality of a sensor and its network. For this case, the task amounts to create a useful model based on KPCA to recognize data as normal or outliers. However, KPCA based reconstruction error (RE) has found several applications in outlier detection but is not perfect to detect outlier. 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 three real datasets are reported showing that the proposed method performs better in finding outliers in wireless sensor networks when compared to the original RE based variant and the One-Class SVM detection approach.