OPTICS-Based Outlier Detection with Newton Classification

Abstract

In today's time, Wireless Sensor Networks (WSNs) and Internet of things (IoTs) have attracted a lot of interest from scientific and businesses communities. They are made up of limited-resource sensors that collect data for various applications (medical, manufacturing, militarily, etc.). However, data collected by sensors are susceptible to have outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. In this paper, we propose a centralized outlier detection and classification approach for WSN. Our solution can distinguish between errors due to a faulty sensor and those due to an event. We also consider the spatial-temporal correlation between sensors' data values and neighbouring sensor nodes. Our approach, titled O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC for OPTICS-Based Outlier Detection with Newton Classification, combines the benefits of the OPTICS algorithm with a new method for outlier detection based on computing the variance and the average of the reachability distances. Furthermore, O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC uses a new approach based on the Newton interpolation and the K-Nearest Neighbours (KNN) algorithms to classify the outliers. For evaluation, we conduct a comparison study between our approach and two works from the literature and thus for the multivariate data case. Simulation results with both synthetic and real-life datasets show that the O <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DNC outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR) and Receiver Operating Characteristic (ROC) curve.