Water Distribution Networks (WDNs) are complex, dynamic systems critical to modern society but increasingly difficult to manage due to urbanization, fluctuating demands, and resource constraints. To address these challenges, Smart Water Distribution Networks (SWDNs) utilize Internet of Things (IoT) devices and protocols like Long Range Wide Area Network (LoRaWAN) for real-time monitoring and analysis, enabling smarter and more efficient water management. This demo presents SWIM (Smart Water Interaction & Monitoring), an innovative application designed to modernize SWDNs. SWIM integrates Digital Twins (DTs), established simulation tools like EPANET, and Machine Learning (ML) to provide predictive analytics, anomaly detection, and real-time control. By employing neural networks, SWIM achieves high-accuracy hydraulic predictions with minimal input data. Built on IoTs and Low Power Wide Area Networks (LPWANs), SWIM delivers scalable, efficient, and user-friendly solutions. It aligns with the principles of Industry 5.0, demonstrating the potential to revolutionize water distribution networks and ensure their sustainability in the face of modern challenges.

Restuccia G., Giuliano F., Garlisi D. (2025). LoRaWAN AI-Powered Digital Twins for Smart Water Distribution Networks. In 2025 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-3) [10.1109/WCNC61545.2025.10978232].

LoRaWAN AI-Powered Digital Twins for Smart Water Distribution Networks

Restuccia G.;Giuliano F.;Garlisi D.
2025-01-01

Abstract

Water Distribution Networks (WDNs) are complex, dynamic systems critical to modern society but increasingly difficult to manage due to urbanization, fluctuating demands, and resource constraints. To address these challenges, Smart Water Distribution Networks (SWDNs) utilize Internet of Things (IoT) devices and protocols like Long Range Wide Area Network (LoRaWAN) for real-time monitoring and analysis, enabling smarter and more efficient water management. This demo presents SWIM (Smart Water Interaction & Monitoring), an innovative application designed to modernize SWDNs. SWIM integrates Digital Twins (DTs), established simulation tools like EPANET, and Machine Learning (ML) to provide predictive analytics, anomaly detection, and real-time control. By employing neural networks, SWIM achieves high-accuracy hydraulic predictions with minimal input data. Built on IoTs and Low Power Wide Area Networks (LPWANs), SWIM delivers scalable, efficient, and user-friendly solutions. It aligns with the principles of Industry 5.0, demonstrating the potential to revolutionize water distribution networks and ensure their sustainability in the face of modern challenges.
2025
Settore INFO-01/A - Informatica
979-8-3503-6836-9
Restuccia G., Giuliano F., Garlisi D. (2025). LoRaWAN AI-Powered Digital Twins for Smart Water Distribution Networks. In 2025 IEEE Wireless Communications and Networking Conference (WCNC) (pp. 1-3) [10.1109/WCNC61545.2025.10978232].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/683823
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