The digitalization of Water Distribution Systems (WDSs) is becoming a key objective in modern society. The increasing complexity of contemporary WDSs, driven by urbanization, fluctuating consumer demand, and limited resources, makes their management particularly challenging, especially in large-scale scenarios. This paper proposes the SWI-FEED framework designed to facilitate the widespread deployment of the Internet of Things (IoT) for enhanced monitoring and optimization of WDSs. The framework aims to investigate the utilization of massive IoT in monitoring and optimizing WDSs in different contexts, with a particular focus on four use cases such as optimal node activation, IoT gateways deployment, distributed leakage detection and water demand disaggregation. SWI-FEED has been tested with predefined network models available in the Open Water Analytics community public repository. Specifically, the four use cases are evaluated using a large network consisting of 4,419 sensor nodes, 3 tanks and 5,066 pipes. Overall, this comprehensive framework provides a holistic approach to address possible challenges of a WDS and optimize the efficiency of large-scale IoT deployments. It reduces the energy consumption of IoT devices within the WDS while enhancing leak detection and localization capabilities in real-world water networks. Our adopted theoretical methodology is based on graph theory, which allows IoT gateways to be strategically positioned to maximize network coverage and minimize infrastructure redundancy. This makes it possible to significantly reduce the number of gateways required and, consequently, the overall system energy consumption.

Introducing and evaluating SWI-FEED: A smart water IoT framework designed for large-scale contexts / Pagano, Antonino; Garlisi, Domenico; Giuliano, Fabrizio; Cattai, Tiziana; Taloma, Redemptor Jr Laceda; Cuomo, Francesca. - In: COMPUTER COMMUNICATIONS. - ISSN 0140-3664. - 237:(2025). [10.1016/j.comcom.2025.108146]

Introducing and evaluating SWI-FEED: A smart water IoT framework designed for large-scale contexts

Garlisi, Domenico;Giuliano, Fabrizio;Cattai, Tiziana;Taloma, Redemptor Jr Laceda;Cuomo, Francesca
2025

Abstract

The digitalization of Water Distribution Systems (WDSs) is becoming a key objective in modern society. The increasing complexity of contemporary WDSs, driven by urbanization, fluctuating consumer demand, and limited resources, makes their management particularly challenging, especially in large-scale scenarios. This paper proposes the SWI-FEED framework designed to facilitate the widespread deployment of the Internet of Things (IoT) for enhanced monitoring and optimization of WDSs. The framework aims to investigate the utilization of massive IoT in monitoring and optimizing WDSs in different contexts, with a particular focus on four use cases such as optimal node activation, IoT gateways deployment, distributed leakage detection and water demand disaggregation. SWI-FEED has been tested with predefined network models available in the Open Water Analytics community public repository. Specifically, the four use cases are evaluated using a large network consisting of 4,419 sensor nodes, 3 tanks and 5,066 pipes. Overall, this comprehensive framework provides a holistic approach to address possible challenges of a WDS and optimize the efficiency of large-scale IoT deployments. It reduces the energy consumption of IoT devices within the WDS while enhancing leak detection and localization capabilities in real-world water networks. Our adopted theoretical methodology is based on graph theory, which allows IoT gateways to be strategically positioned to maximize network coverage and minimize infrastructure redundancy. This makes it possible to significantly reduce the number of gateways required and, consequently, the overall system energy consumption.
2025
edge computing; leak detection; LoRaWAN; massive-IoT; Water Distribution Systems (WDSs)
01 Pubblicazione su rivista::01a Articolo in rivista
Introducing and evaluating SWI-FEED: A smart water IoT framework designed for large-scale contexts / Pagano, Antonino; Garlisi, Domenico; Giuliano, Fabrizio; Cattai, Tiziana; Taloma, Redemptor Jr Laceda; Cuomo, Francesca. - In: COMPUTER COMMUNICATIONS. - ISSN 0140-3664. - 237:(2025). [10.1016/j.comcom.2025.108146]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1736339
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