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:
- Smart water IoT infrastructure design;
- Massive IoT (through LPWAN IoT technologies) design to accommodate an efficient collection of data from devices scattered in the WSS;
- Use graph signal processing to provide new and efficient AI methods;
- Smart contracts definition.
WITS is part of Spoke 8 – Intelligent and Autonomous Systems
- Developing an IoT framework for water distribution systems, using hydraulic analysis, graph signal processing and a LoRaWAN network-based simulation environment.
- Application of clustering techniques and edge processing for improved network monitoring and leakage detection using machine learning at the network edges.
- Exploring deep learning soft-clustering to model and measure the uncertainty in water demand profiling.
- Application of topological signal processing for water flow reconstruction and leakage detection. Definition and implementation of the method and optimization algorithm.
- Investigation of community detection algorithms based on graph signal processing with graph neural networks to generate a digital twin of water supply systems.
- Exploring deep learning from non-intrusive load monitoring to water end-use separation.
- Conceptualization and development of an integrated synthetic dataset that merges application-level data with network-related information.
- An innovative approach for Smart Water Monitoring based on IoT Throughout the project's evolution, an innovative approach to streamline water resource monitoring was introduced. The 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.
- A comprehensive analysis of methods using ai for water management
WITS 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.
The key contributions of this study are the following:
- Conducting a survey of machine learning methods for smart water metering applications that includes state estimation and non-intrusive monitoring.
- Defining a taxonomy of smart metering applications that distinguishes, for the first time, the infrastructure analysis and users’ demand analysis.
- Collecting publicly accessible datasets from various sources, including government agencies, research institutions, and online repositories like competitions.
- In the WP3 the project is creating a comprehensive IoT integration framework including components for hydraulic analysis identification, graph signal processing, strategic placement of measurement points and a simulation environment, with a main focus on a LoRaWAN network. WITS also explores the amalgamation of smart orchestration, workload control and edge computing to manage water network complexities.
- The project proposed a deep neural network for near real-time multi-appliance water disaggregation as a tool to monitor, manage and save water resources more effectively in the residential sector.
- WITS developed a novel method based on topological signal processing (TSP) to characterize interacting data in water distribution networks. TPS aims to study signals with any order structure for modeling more appropriately complex data interactions. Therefore, it proposes a novel framework to simultaneously reconstruct flows and pressure values in a water distribution networks, also able to detect water leakages.
WITS project took part in/organized 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”
- WITS researchers are organizing 1st International Workshop on Smart Water Management (SmartWater) in IFIP/IEEE Networking 2024, to be held in Thessaloniki, Greece, June 3-6, 2024
- R. Taloma participated to “Individuare le perdite idriche con l'IA?” for podcast “Tutto Connesso”
- R. Taloma delivered a 5-minute interview about the RESTART Grand Challenge “Digitalizzare l’ambiente per un mondo più sostenibile”
- Publications
Expected: at least 9 publications on 36 months
Accomplished: 2 (2 conference publications)
Readiness level: 66% - Joint Publications
Expected: >=30% joint publications on 36 months
Accomplished: 2 joint publications over 3
Readiness level: 66% - Talks/Communication events
Expected: 15 talks or event chairing/organizing within WITS activities on 36 months
Accomplished: 6 (among dissemination events and conference presentations)
Readiness level: 100% - Demo/PoC
Expected: 1 PoCs expected by the end of the project
Accomplished: 0
Readiness level: 0% (work according to plan) - Project Meetings
Expected: > 36 meetings
Accomplished: 20 meetings
Readiness level: 55% - Personnel Recruitments
Expected: 1 RTD-A
Accomplished: 1 RTD-A
Readiness level: 100%
Milestones:
- First year report including project dissemination activities and the delivery of D1 and D2 (due date: M12)
- Second year report including project dissemination activities and the delivery of D3 and D4 (due date: M24)
- Final report including project dissemination activities and the delivery of D5 and D7 (due date: M36)
- D1 - Deliverable on Smart Water Supply Systems (due date: M5)
- D2 - Deliverable on Massive IoT Access (due date: M12)
- D3- Deliverable on Continuum data collection and smart network orchestration (due date: M24)
- D4- Deliverable on Sensed processing using graph signal processing (due date: M17)
- D5- Deliverable on Development of utilities/smart contracts for users and operators (due date: M27)
- D6 - Performance assessment – Case Studies (due date: M36)
Project PI: Francesca Cuomo
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;
- water demand forecasting.
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 the project PI.