Fog-Empowered Anomaly Detection in IoT Using Hyperellipsoidal Clustering

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 efficient hyperellipsoidal clustering algorithm. The end nodes in the Fog computing architecture do not perform any processing or clustering on the data. The Fog layer and the Cloud layer nodes perform the clustering and anomaly detection process, thus helping to achieve anomaly detection in a timely manner. The evaluation using synthetic and real datasets demonstrates that our proposed approach achieves a significant reduction in latency and energy consumption compared to the Distributed and Centralized schemes, while achieving a comparable detection accuracy compared to a Centralized scheme.