An Unsupervised Local Outlier Detection Method for Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have provided many applications, which need precise sensing data analysis, in many areas. However, sensing datasets contain outliers sometimes. Although outliers rarely occur, they seriously reduce the precision of the sensing data analysis. In the past few years, many researches focused on outlier detection. However, many of them ignored one factor that WSNs are usually deployed in a dynamic environment that changes with time. Thus, we propose a new method, which is an unsupervised learning method based on mean-shift algorithm, for outlier detection that can be used in a dynamic environment for WSNs. To make our method adapt to a dynamic environment, we define two new distances for outlier detection. Moreover, the simulation shows that our method performed on real sensing dataset has ideal results; it finds outliers with a low false positive rate and has a high recall. For generality, we also test our method on different synthetic datasets.