Complete outlier detection and classification framework for WSNs based on OPTICS

Abstract

Wireless Sensor Networks (WSNs) consist of multi-functional sensors with limited resources that collect information in various applications (medical, manufacturing, militarily, etc.). However, data gathered by sensors are susceptible to outliers, which need to be detected and classified into errors and events using outlier detection and classification methods. This paper proposes a centralised outlier detection and classification approach for WSNs, which can distinguish between errors due to a faulty sensor and those due to an event. Also, it considers the spatial–temporal correlation between neighbouring sensor nodes and sensors’ data values. Our approach, titled OPTICS-K, combines the benefits of the Ordering Points To Identify the Clustering Structure (OPTICS) algorithm for clustering, the Inter-Cluster Distance (ICD) method and the K-Nearest Neighbours (KNN) algorithm for outlier detection. Furthermore, OPTICS-K uses a new method based on Kriging interpolation to classify the outliers. For evaluation, we conduct a comparison study between OPTICS-K 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 OPTICS-K outperforms the studied techniques in terms of several metrics like Detection Rate (DR), False Alarm Rate (FAR), Accuracy rate (ACC), F1_score and Area Under the Curve (AUC).