O2DCA: Online Outlier Detection and Classification Approach for WSN

Today's scientific and corporate communities are highly interested in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT). This kind of network consists of sensors with low resources that gather information for various real-life applications (healthcare, industrial, security, etc.), with streaming data requiring online processing. However, since outliers may occur in sensors collected data, it is necessary to identify and classify them into errors and events using online outlier detection and classification techniques suitable for the WSNs real-life applications. In this paper, we propose a centralised method for online outlier detection and classification in WSN. Our approach can differentiate between errors caused by malfunctioning sensors and errors caused by events. We also consider the spatial-temporal connection between sensor data vectors and nearby sensor nodes. Our approach, titled O
²
DCA, for Online Outlier Detection and Classification Approach, combines the benefits of the Fixed Width Clustering (FWC) and the Inter-Cluster Distance (ICD) algorithms for clustering outlier detection, respectively. For classification, we use the Inverse Distance Weighting (IDW) method, which allows us to classify outliers into errors that will be discarded and relevant events for which a necessary decision must be taken. We show through simulation using both synthetic and real-world datasets that our novel online approach is suitable for working with real-life applications where the Detection Rate (DR) performance metric stays stable and better than the offline approach.