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The Watering IoTs (WITS) project focuses on the use of IoT for monitoring and optimizing Water Supply Systems, by combining short and long term decisions based on continuous and massive data collection, signal on network processing and AI analysis.

WITS key objectives are:

  1. Smart water IoT infrastructure design;
  2. Massive IoT (through LPWAN IoT technologies) design to accommodate an efficient collection of data from devices scattered in the WSS;
  3. Use graph signal processing to provide new and efficient AI methods;
  4. Smart contracts definition.

WITS is part of   Spoke 8 – Intelligent and Autonomous Systems


  • Identification of key methodologies to combine IoT and water management. We were able to emulate a Water Supply System (WSS) as for the collection of data to be used to validate new approaches to derive the water flow and to use AI techniques to analyze the system status.
  • Preparation of the project datasets and source code made public available at
  • Use of the NS-3 framework to perform simulations to evaluate energy consumption of LoRaWAN sensors for flow monitoring in WSS.
  • Integration of IoT energy models with graph signal processing with a view to developing sustainable IoT solutions.
  • Identification of a methodology to model, by using graph signal processing, the water flow behavior and its reconstruction under a smart sampling of the water status through sensors.
  1. An innovative approach for Smart Water Monitoring based on IoT. Throughout the project's evolution, we have introduced an innovative approach to streamline water resource monitoring. Our method offers a precise determination of the minimum sensors required and their optimal placements for accurately measuring water flow within the network. This framework enhances the efficiency of water resource monitoring by reducing the energy consumption of the associated network components. This approach holds significant value for water management companies as it empowers them to quantify water resources effectively, curbing wastage and enabling proficient network management while conserving energy in the process.
  1. A comprehensive analysis of methods using ai for water management. We surveyed the main literature dealing with the utilization of Machine Learning (ML) and IoT for the development of intelligent systems that enhance water management, optimize distribution networks, and enable efficient resource allocation. ML methods, such as supervised learning and unsupervised learning are employed to analyze vast volumes of data collected by IoT devices embedded in the water infrastructure.
WITS project took part in important dissemination events:
  • Redemptor Jr Taloma (PhD student) participated as speaker in the Live Webinar Servizi a Rete “Progettazione di soluzioni IoT per reti idriche intelligenti” on April 27th 2023, discussing the applications of machine learning in the literature about smart water management.
  • Two public talks given in Paris, at CNAM, by Tiziana Cattai, “Graph model for Water Distribution Networks with IoT applications” and by Francesca Cuomo “Towards Edge Computing in LoRaWAN: new architectural models and future applications”.
  • Talk in Rome at the AEIT 2023 conference by Tiziana Cattai, “A graph based method for efficiently monitoring of water supply systems”
  1. Publications
    • Expected: at least 9 publications on 36 months
    • Accomplished: 2 (2 conference publications)
    • Readiness: 66%
  2. Joint Publications
    • Expected: >=30% joint publications on 36 months
    • Accomplished: 2 joint publications over 3
    • Readiness: 66%
  3. Talks/Communication events
    • Expected: 15 talks or event chairing/organizing within WITS activities on 36 months
    • Accomplished: 5 (among dissemination events and conference presentations)
    • Readiness: 100%
  4. Demo/PoC
    • Expected: 1 PoCs expected by the end of the project
    • Accomplished: 0
    • Readiness: 0% (work according to plan)
  5. Project Meetings
    • Expected: > 36 meetings
    • Accomplished: 15 meetings
    • Readiness:160%
  6. Personnel Recruitments
    • Expected: 1 RTD-A
    • Accomplished: 1 RTD-A
    • Readiness:100%


Collaboration proposals
The WITS project is open to cooperation with experts as well as nonprofit organizations in the following areas: 

  • water network degradation prevention;
  • water consumption in precision agriculture;
  • water network monitoring in emergency scenarios;
  • water distribution system incorporating self-managing features.

WITS received a first collaboration proposal by University of Gabes-Tunisia, for the formation of a consortium for the application to the PRIMA project (Main topic: Sustainable Water Management). The project is seeking for other partners interested in this Consortium.

For any proposal of collaboration within the project please contact francesca.cuomo at