An Energy-Efficient Outlier Detection Based on Data Clustering in WSNs

Sensor nodes in wireless sensor networks are prone to malfunction because they are exposed to the nearby environment directly. Consequently, wrong sensor readings occurred from sensor nodes and these readings are called an outlier. Commonly, since an outlier deviates from normal sensor readings and it can bring about some problems, various techniques to detect the outliers have been proposed. In this paper, we propose an efficient outlier detection technique based on data clustering. In order to decide the width of the cluster that consists of the sensor readings, we applied the Pigeonhole Principle and then detected the outliers based on clusters. In experiments, we demonstrate the efficiency of our proposed technique compared to other outlier detection techniques.