Fog-empowered anomaly detection in IoT using hyperellipsoidal clustering

Abstract

Anomaly detection is important for time-critical Internet of Things (IoT) applications, such as healthcare and emergency management. The recent introduction of Fog computing architecture provides an efficient platform for delay sensitive IoT applications. Exploiting the advantages of Fog computing for anomaly detection provides the ability to detect abnormal patterns in an accurate and timely manner. Use of Centralized and Distributed anomaly detection methods suffer from significant latency and energy consumption issues. Hence, we propose a novel anomaly detection method, called Fog-Empowered anomaly detection, by harnessing the processing power of the Fog computing platform and using an ef..