An Anomaly Detection Based on Data Fusion Algorithm in Wireless Sensor Networks

Abstract

In recent years, with the development of wireless sensor networks (WSN), it has been applied in more and more areas. However, energy consumption and outlier detection have been always the hot topics in WSN. In order to solve the above problems, this paper proposes a timely anomaly detection algorithm which is based on the data fusion algorithm. This algorithm firstly employs the piecewise aggregate approximation (PAA) to compress the original data so that the energy consumption can be reduced. It then combines an improved unsupervised detection algorithm of K-Means and artificial immune system (AIS) to classify the compressed data to normal and abnormal data. Finally, relevant experiments on virtual and actual sensor databases show that our algorithm can achieve a high outlier detection rate while the false alarm rate is low. In addition, our detection algorithm can effectively prolong the life because it is based on data fusion algorithm.