Anomaly Detection in Urban Water Distribution Grids Using Fog Computing Architecture

Abstract

Efficient monitoring and quick feedback control are the main requirements of smart cities to guarantee the stability and safety of urban infrastructures. Real-time monitoring in order to detect anomalies can lead to the data intensive processing requires a new computing scheme to offer large-scale and low latency services. Fog architecture by extending computing to the edge of network, provides the ability to accurate and fast detection of abnormal patterns. The hierarchical fog computing architecture and the efficient hyperellipsoidal clustering algorithm presented in the previous studies have been applied in this paper to identify anomalous behaviors in water distribution grids. We created an urban water distribution grid dataset using Epanet2w simulator software by recording grid measured features as (pressure and head) for several scenarios. To evaluate the effect of applying the hierarchical anomaly detection model, we implemented the data and computing nodes at different layers by docker containers. The evaluation results proved the effectiveness of the hierarchical anomaly detection model in significant reduction of the communication latency, while preserving the detection accuracy compared to the centralized scheme.