AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture

• URL: http://arxiv.org/abs/2511.15870v1
• Date: Wed, 19 Nov 2025 20:53:50 GMT
• Title: AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture
• Authors: Qiming Guo, Bishal Khatri, Wenbo Sun, Jinwen Tang, Hua Zhang, Wenlu Wang,
• Abstract summary: AquaSentinel is a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks.<n>We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure.
• Score: 11.644739814142502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Related papers

• Multi-Agent Collaborative Intrusion Detection for Low-Altitude Economy IoT: An LLM-Enhanced Agentic AI Framework [60.72591149679355]
  The rapid expansion of low-altitude economy Internet of Things (LAE-IoT) networks has created unprecedented security challenges.<n>Traditional intrusion detection systems fail to tackle the unique characteristics of aerial IoT environments.<n>We introduce a large language model (LLM)-enabled agentic AI framework for enhancing intrusion detection in LAE-IoT networks.
  arXiv  Detail & Related papers  (2026-01-25T12:47:25Z)
• HiFiNet: Hierarchical Fault Identification in Wireless Sensor Networks via Edge-Based Classification and Graph Aggregation [11.108171977551619]
  HiFiNet is a hierarchical fault identification framework for wireless networks.<n>It produces more accurate predictions by capturing both local temporal patterns and network-wide spatial dependencies.<n>It significantly outperforms existing methods in accuracy, F1-score, and precision.
  arXiv  Detail & Related papers  (2025-11-06T16:15:19Z)
• AI-Driven Forecasting and Monitoring of Urban Water System [5.652933022735071]
  Underground water and wastewater pipelines are vital for city operations but plagued by anomalies like leaks and infiltrations.<n>In recent years, artificial intelligence has advanced rapidly and is increasingly applied to urban infrastructure.<n>We propose an integrated AI and remote-sensor framework to address the challenge of leak detection in underground water pipelines.
  arXiv  Detail & Related papers  (2025-10-08T04:28:38Z)
• Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks [6.036207670620086]
  Water Distribution Networks (WDNs) face challenges such as pipe blockages and background leakages.<n>This paper proposes an unsupervised, online learning framework that aims to detect two types of faults in WDNs.
  arXiv  Detail & Related papers  (2025-08-22T12:23:40Z)
• Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics [3.360922672565235]
  Stealthy False Data Injection Attacks (SFDIAs) can compromise system operations while avoiding detection.<n>This paper presents a systematic analysis of sensor attacks against water distribution networks (WDNs)<n>We propose several attack formulations that range from tailored strategies satisfying both physical and detection constraints to simpler measurement manipulations.
  arXiv  Detail & Related papers  (2025-04-24T02:54:20Z)
• Enhancing Network Security Management in Water Systems using FM-based Attack Attribution [43.48086726793515]
  We propose a novel model-agnostic Factorization Machines (FM)-based approach that capitalizes on water system sensor-actuator interactions to provide granular explanations and attributions for cyber attacks.<n>In multi-feature cyber attack scenarios involving intricate sensor-actuator interactions, our FM-based attack attribution method effectively ranks attack root causes, achieving approximately 20% average improvement over SHAP and LEMNA.
  arXiv  Detail & Related papers  (2025-03-03T06:52:00Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System [52.93806509364342]
  This paper proposes a radial threat estimation method for energy pipelines based on distributed optical fiber sensing technology. We introduce a continuous multi-view and multi-domain feature fusion methodology to extract comprehensive signal features. We incorporate the concept of transfer learning through a pre-trained model, enhancing both recognition accuracy and training efficiency.
  arXiv  Detail & Related papers  (2023-12-18T12:37:35Z)
• Graph Neural Networks for Pressure Estimation in Water Distribution Systems [44.99833362998488]
  Pressure and flow estimation in Water Distribution Networks (WDN) allows water management companies to optimize their control operations. We combine physics-based modeling and Graph Neural Networks (GNN), a data-driven approach, to address the pressure estimation problem. Our GNN-based model estimates the pressure of a large-scale WDN in The Netherlands with a MAE of 1.94mH$$O and a MAPE of 7%.
  arXiv  Detail & Related papers  (2023-11-17T15:30:12Z)
• AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection [40.532331552038485]
  We present a novel Amplitude-Modulated Perturbation and Vortex Convolutional Network, AMSP-UOD. AMSP-UOD addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. Our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity.
  arXiv  Detail & Related papers  (2023-08-23T05:03:45Z)
• Graph Neural Network-Based Anomaly Detection for River Network Systems [0.8399688944263843]
  Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology. Anomaly detection is crucial for identifying erroneous patterns in sensor data. This paper presents a solution to the challenging task of anomaly detection for river network sensor data.
  arXiv  Detail & Related papers  (2023-04-19T01:32:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.