Exponentially Weighted Ellipsoidal Model for Anomaly Detection

Abstract

Efficient localized data modeling techniques in Internet of Things (IoT) applications enable the nodes to change their behavior upon observing events of interest. Additionally, battery-powered IoT nodes can conserve their energy resources by limiting their data communications to specific events. Despite the real-time nature of the data collected in the IoT and limited memory and computational resources, most of the current data modeling approaches for the IoT involve batch training. Recently, an online efficient anomaly detection technique called iterative data capture anomaly detection has been proposed for environmental sensing and monitoring applications. However, this approach cannot han..