AquaEIS

Abstract

Real-time event identification is critical in complex distributed infrastructures, e.g., water systems, where failures are difficult to isolate. We present AquaEIS, an event-based middleware tailored to the problem of locating sources of failure (e.g., contamination) in community water infrastructures. The inherent complexity of underground hydraulic systems combined with aging infrastructure presents unique challenges. AquaEIS combines online learning techniques, model-driven simulators and data from limited sensing networks to intelligently guide human participants (e.g., staff) in identifying contaminant sources. The framework integrates the necessary abstractions with event processing methods into a workflow that iteratively selects and refines the set of potential failure points for human-driven grab sampling. The integrated platform utilizes Hidden Markov Model (HMM) based representations along with field reports for event inference; reinforcement learning (RL) methods have also shown promise for further refining event locations and reducing the cost of human engagement. Our approach is evaluated in real-world water systems under a range of distinct events. The results show that AquaEIS can significantly reduce the number of sampling cycles, while ensuring localization accuracy (detected 100% of the failure events as compared to a baseline that can only identify 38% of the events).