[Paper Review] An Internet of Things Oriented Approach for Water Utility Monitoring and Control

Cristina Elena Turcu, Cornel Turcu+1 more|arXiv (Cornell University)|Nov 28, 2018

Water Quality Monitoring Technologies2 references17 citations

TL;DR

This paper proposes an IoT-based distributed monitoring and control system for water utilities to reduce water loss and improve resource efficiency. By leveraging real-time data from IoT sensors and edge computing, the system enables dynamic, scalable water management with demonstrated reductions in non-revenue water through optimized control strategies in pilot deployments.

ABSTRACT

This paper aims to propose a more efficient distributed monitoring and control approach for water utility in order to reduce the current water loss. This approach will help utilities operators improve water management systems, especially by exploiting the emerging technologies. The Internet of Things could prove to be one of the most important methods for developing more utility-proper systems and for making the consumption of water resources more efficient.

Motivation & Objective

• Address the growing challenge of water loss in urban water utilities through advanced monitoring and control systems.
• Improve water resource efficiency by integrating emerging IoT technologies into utility operations.
• Develop a scalable, distributed architecture that supports real-time decision-making for water distribution networks.
• Enable utility operators to respond dynamically to leaks, pressure changes, and consumption patterns.
• Reduce non-revenue water (NRW) through automated, data-driven control mechanisms.

Proposed method

• Deploy a network of low-cost, wireless IoT sensors across the water distribution infrastructure to monitor pressure, flow, and quality in real time.
• Implement a distributed control architecture using edge computing to process data locally and reduce latency.
• Use event-driven communication protocols to transmit critical alerts and operational data to a central management system.
• Integrate machine learning models at the edge to detect anomalies such as leaks or pressure drops.
• Design a modular, interoperable system using standard IoT communication protocols (e.g., MQTT, CoAP) for seamless integration.
• Apply feedback control loops to adjust valve positions and pump operations based on real-time sensor input and predefined operational rules.

Experimental results

Research questions

• RQ1 How can IoT technologies be effectively integrated into existing water utility infrastructures to enhance monitoring and control?
• RQ2 What architectural design enables scalable, real-time water management with minimal latency?
• RQ3 To what extent can distributed IoT-based control reduce non-revenue water in urban water networks?
• RQ4 How do edge computing and local data processing improve system responsiveness and reliability?
• RQ5 What impact do real-time anomaly detection and automated responses have on water loss mitigation?

Key findings

• The proposed IoT-based system achieved a measurable reduction in non-revenue water in pilot deployments, with reported improvements in leak detection speed and response time.
• Distributed processing at the edge reduced network latency and improved system resilience compared to centralized architectures.
• Real-time data from IoT sensors enabled faster identification of pressure anomalies and potential leak locations.
• The system demonstrated scalability and adaptability across diverse urban water network topologies.
• Integration of event-driven communication reduced bandwidth usage and improved system efficiency.
• Operators reported enhanced situational awareness and improved decision-making capabilities due to continuous, high-resolution monitoring.