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Fault-Localization in Water Distribution Networks using Hierarchical Anomaly Analysis

Water Distribution Networks (WDN) are vulnerable to unexpected events of the environment, system aging and security attacks. Real-time monitoring of WDNs is essential to detect disruptions, localize faulty elements and make fast decisions. Various anomaly detection methods are used to detect abnormal behaviors at these systems. One important challenge is to find a proper computing architecture for analyzing the collected sensory data from WDN in order to accurately localize the faulty element in the network that caused the anomalous behavior. We created an urban Water Distribution Network dataset using Epanet2w simulator software by measuring WDN hydraulic features: pressure and head. We added several distinct events (unexpected behavior) with different scales during the simulation time. To evaluate the impact of the computing architectures on the accuracy of fault localization, we examined several architectures: Centralized Cloud Architecture (CCA), three-layer Fog Computing Architecture (FGA) and the proposed Hierarchical Architecture (HA). The data and computing nodes at different layers were executed as docker containers with limited resources. The evaluation results showed a significant increase in fault localization accuracy using the proposed HA compared to other architectures.