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
Project PI: Francesca Cuomo
WP3:
- Assessing SWI-FEED in Different Massive IoT Contexts
- We tested and validated the SWI-FEED framework across four use cases: optimal node activation, gateway deployment, distributed leakage detection, and water demand disaggregation in large-scale smart water distribution systems (WDS).
- LoRaWAN-Based Cloud-Edge Computing Continuum
- We propose the integration of a processing module into a LoRaWAN network using the principles of edge computing. This approach incorporates a distinct computing module capable of processing data streams at the network edge.
- Digital Twin Development
- We developed SWIM (Smart Water Interaction & Monitoring), a platform that integrates Digital Twin technology, Machine Learning, and simulation tools such as EPANET and WNTR to enable predictive analytics, anomaly detection, and real-time control.
- Smart Contract Design and Development
- We continue to focus on the development of smart contract systems for WDS that facilitate the relationship between operators and customers. In particular, we are integrating a variety of tariffs that take into account the energy impact of the WDS pumps. Additionally, we have begun to implement algorithms designed to optimize costs and increase the profits of both users and operators.
- Deep Neural Networks for water demand analysis
- Non-Intrusive Load Monitoring (NILM) enables the estimation of individual appliances' energy footprints from the total household consumption without the cost and complexity of installing a specific smart meter per device. By providing users with real-time feedback on their energy consumption, NILM is known Deep Neural Networks for water demand analysis foster conservation habits. The widespread diffusion of smart home meters has enabled the unprecedented availability of a vast amount of consumption data observed at short intervals, posing an urgent need for designing deep learning models that can capture insightful long-range dependencies and overcome the computational cost of training and fine-tuning the same neural network for each device one at a time, as currently performed by single-appliance solutions for NILM. To cope with these challenges, we propose a sequence-to-sequence Multi-AppliaNce TRAnsformer (MANTRA) model that extracts long-term information and simultaneously estimates individual appliance powers, providing a scalable solution for NILM in real-world settings. Experiments on several time resolutions prove the robustness of MANTRA for multi-target energy disaggregation over other deep learning methods.
- Nodal Pressure Reconstruction via GNN
- We utilized GNNs to reconstruct nodal pressures in water networks, testing robustness against noise. A modified approach with Graph Laplacian Denoising improved unobserved pressure reconstruction, aiding in leak detection efforts.
- Topological Signal Processing
- We explored topological signal processing as a framework to model data on high-order networks, where node signals correspond to pressure values and edge signals represent water flow. We investigated the applicability of tools from topological signal processing in water distribution networks. We developed a topological representation for water flows that incorporates realistic factors such as water demands from users. This representation allows us to formulate learning problems for node and edge signals, specifically for pressure and flow reconstruction. We validated our approach on realistic water distribution networks, demonstrating the ability to reconstruct pressures and flows using a reduced number of samples. We extended our analysis by proposing a dynamic model for water flow that accounts for both water demands and leakages. Addressing the critical issue of water loss due to leaks, we design a leakage detection algorithm capable of accurately identifying leaks, even when only a limited subset of flow measurements is available. Our results show that integrating cell complexes improves performance in characterizing water distribution networks and detecting water leakages. Overall, this work highlights the potential of topology-based learning to efficiently monitor and analyze WDNs by capturing high-order interactions within the network.
As a key outcome of the WITS project, we have initiated the design of a comprehensive system in which a real water distribution network placed near Rome, outfitted with LoRaWAN devices, will be linked to the WITS platform and incorporated into the RESTART ecosystem. This endeavor will be amplified within the framework of the RESTART digital twin smart grand challenge. The activities are supported by UNIPA and UNIDATA partners.
A notable outcome of our efforts within the project is the successful attainment of institutional membership in the LoRa Alliance by our CNIT partner, marking a significant step forward in our collaborative endeavors.
WP3
We developed a comprehensive IoT integration framework to enhance the monitoring and optimization of Water Distribution Systems. This framework includes components for hydraulic analysis identification, graph signal processing application, strategic placement of measurement points, and a simulation environment, with a particular focus on a LoRaWAN network for controlled system testing and refinement. Moreover, the additional tasks of the WP3 explores the amalgamation of smart orchestration, workload control, and edge computing to manage water network complexities. By adjusting data stream flow based on application features and utilizing edge computing, it aims to boost the overall performance and reliability of water distribution systems while facilitating timely decision-making.
WP3
Motivated by remarkable advancements in non-intrusive load monitoring (NILM), we have explored the potentiality of deep learning by proposing a neural network for near real-time multi-appliance water disaggregation. Positive results across various sampling intervals prompt discussion on sparse data challenges. Overall, such investigation aims to explore deep learning for water disaggregation as a powerful tool to monitor, manage and save water resources more effectively in the residential sector.
WP4
Based on recent literature on topological signal processing (TSP), we have developed a novel method to characterize interacting data related to water distribution networks. TPS aims to study signals associated not only with graph nodes (as classical graph signal processing) but with any order structures in order to model in a more appropriate way the complex data interactions. In this context, we propose a novel framework to simultaneously reconstruct flows and pressure values in a water distribution networks that is also able to detect water leakages.
Full conceptualization, implementation and analysis of graph-based modeling and signal processing to monitor a water distribution network in efficient and accurate ways. Identification of a flow reconstruction algorithm and of the best node ranking on the graph in order to save energy in case there is the need to reduce the number of measurements in the network still keeping a given level of accuracy. Testing of the proposed approach in numerical simulations performed in a realistic LoRaWAN WDS scenario. We demonstrate that the water flow is accurately reconstructed by strategic placement of a reduced number of sensors, leading to significant energy savings, i.e. around 73%.
SOCIETAL IMPACT:
The proposed approach for designing and implementing smart water managemtn and smart contracts for smart water distribution networks has significant societal impact. It enhances resource management by ensuring efficient and equitable water distribution and promotes sustainability by identifying and mitigating inefficiencies. Additionally, it empowers local communities to manage their water resources actively, leading to more responsive and tailored solutions. The tool also offers economic benefits by reducing operational costs and water bills, and encourages innovation and technological advancement in water management, driving further research and development.
TOPOLOGICAL SIGNAL PROCESSING:
One of the next big advancements for water distribution networks is the optimal monitoring to improve water flow management. This can be achieved using innovative tools such as topological signal processing, that enables a deeper understanding and characterization of data within interconnected networks such as water distribution systems. Specifically, the project focuses on developing topological signal processing-based reconstruction algorithms to monitor water flow and detect anomalies, such as leakages, in real-time. By leveraging the structure of water distribution networks, this approach introduces more efficient data modeling methods that can predict and optimize water flow, while addressing challenges like water demand variability and leakage identification. These advancements represent a significant step toward more intelligent, adaptive, and sustainable water management systems.
SMART CONTRACTS:
One of the big things in the research area covered by this project is primarily focused on the intersection of smart contracts and water management. The project's main outcome, the ability to manage water distribution networks through Advanced Smart Contracts for Water Management, represents a significant leap forward in this field. The project proposes a pioneering tool for designing and implementing smart contracts specifically tailored to smart water distribution networks. This tool, which allows stakeholders to input parameters such as water sources, distribution points, consumption patterns, and contractual stipulations, could revolutionize the way water management contracts are created and executed.
DIGITAL TWIN:
development of a digital twin for water distribution networks. As an outcome, we will deliver a platform to support multiple data streams, including real data from the physical water distribution network, simulation data from the digital twin, and machine learning algorithms.
WATER DEMAND ANALYSIS:
Machine learning has great potential to revolutionize the energy and water distribution. It enables precise identification of individual appliance usage from smart meter data, providing real-time insights that can help users reduce consumption and save costs. Upcoming research will focus on making AI solutions more efficient, scalable, and adaptable to various household settings, driving smarter and more sustainable resource usage.
Papers:
Domenico Garlisi, Gabriele Restuccia, Ilenia Tinnirello, Francesca Cuomo and Ioannis Chatzigiannakis, “Real-Time Leakage Zone Detection in Water Distribution Networks: A Machine Learning-based Stream Processing Algorithm”, International Symposium on Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2023), Amsterdam 05/09/2023
Redemptor Laceda Taloma, Jr., Danilo Comminiello (Sapienza University of Rome, Italy) Patrizio Pisani (Unidata SpA, Italy) Francesca Cuomo (University of Rome Sapienza, Italy), "UNet-WD: Deep Learning for Multi-Appliance Water Disaggregation", IFIP 1st International Workshop on Smart Water Management (SmartWater) 2024, Salonicco, June 2024
Dimitrios Amaxilatis (Spark Works Ltd., Ireland), Tiziana Cattai (Sapienza University of Rome, Italy), Antonino Pagano (University of Palermo, Italy),Ioannis Chatzigiannakis (Sapienza University of Rome, Italy), Redemptor Laceda Taloma, Jr. (Sapienza University of Rome, Italy), Domenico Garlisi (University of Palermo & CNIT Italian National Consortium for Telecommunications, Italy), Themistoklis Sarantakos (Spark Works Ltd., Ireland), Varvara Vythoulka (University of Patras, Greece), Christos Zaroliagis (University of Patras, Greece), "A Tool to Facilitate the Design of Smart Contracts in Smart Water Distribution Networks", IFIP 1st International Workshop on Smart Water Management (SmartWater) 2024, Salonicco, June 2024.
T. Cattai, S. Colonnese, D. Garlisi, A. Pagano, and Francesca Cuomo. 2024. GraphSmart: a method for green and accurate IoT water monitoring. ACM Transactions on Sensors Networks (October 2024)
P. Spadaccino, D. Garlisi, A. Franceschi, I. Tinnirello and F. Cuomo, "Accelerating Network Resource Allocation in LoRaWAN via Distributed Big Data Computing," in IEEE Access, vol. 12, pp. 141237-141250, 2024, doi: 10.1109/ACCESS.2024.3465634
A. Pagano, D. Garlisi, I. Tinnirello, F. Giuliano, G.Garbo, M. Falco, F. Cuomo, “A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deployment”, Ad Hoc Networks, 2024
Stefano Milani, Domenico Garlisi, Carlo Carugno, Christian Tedesco, and Ioannis Chatzigiannakis. "Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things." Elsevier Internet of Things 27 (2024): 101266
A notable outcome of our efforts within the project is the successful attainment of institutional membership in the LoRa Alliance by our CNIT partner, marking a significant step forward in our collaborative endeavors.
WP3
We developed a comprehensive IoT integration framework to enhance the monitoring and optimization of Water Distribution Systems. This framework includes components for hydraulic analysis identification, graph signal processing application, strategic placement of measurement points, and a simulation environment, with a particular focus on a LoRaWAN network for controlled system testing and refinement. Moreover, the additional tasks of the WP3 explores the amalgamation of smart orchestration, workload control, and edge computing to manage water network complexities. By adjusting data stream flow based on application features and utilizing edge computing, it aims to boost the overall performance and reliability of water distribution systems while facilitating timely decision-making.
WP3
Motivated by remarkable advancements in non-intrusive load monitoring (NILM), we have explored the potentiality of deep learning by proposing a neural network for near real-time multi-appliance water disaggregation. Positive results across various sampling intervals prompt discussion on sparse data challenges. Overall, such investigation aims to explore deep learning for water disaggregation as a powerful tool to monitor, manage and save water resources more effectively in the residential sector.
WP4
Based on recent literature on topological signal processing (TSP), we have developed a novel method to characterize interacting data related to water distribution networks. TPS aims to study signals associated not only with graph nodes (as classical graph signal processing) but with any order structures in order to model in a more appropriate way the complex data interactions. In this context, we propose a novel framework to simultaneously reconstruct flows and pressure values in a water distribution networks that is also able to detect water leakages.
Full conceptualization, implementation and analysis of graph-based modeling and signal processing to monitor a water distribution network in efficient and accurate ways. Identification of a flow reconstruction algorithm and of the best node ranking on the graph in order to save energy in case there is the need to reduce the number of measurements in the network still keeping a given level of accuracy. Testing of the proposed approach in numerical simulations performed in a realistic LoRaWAN WDS scenario. We demonstrate that the water flow is accurately reconstructed by strategic placement of a reduced number of sensors, leading to significant energy savings, i.e. around 73%.
SOCIETAL IMPACT:
The proposed approach for designing and implementing smart water managemtn and smart contracts for smart water distribution networks has significant societal impact. It enhances resource management by ensuring efficient and equitable water distribution and promotes sustainability by identifying and mitigating inefficiencies. Additionally, it empowers local communities to manage their water resources actively, leading to more responsive and tailored solutions. The tool also offers economic benefits by reducing operational costs and water bills, and encourages innovation and technological advancement in water management, driving further research and development.
TOPOLOGICAL SIGNAL PROCESSING:
One of the next big advancements for water distribution networks is the optimal monitoring to improve water flow management. This can be achieved using innovative tools such as topological signal processing, that enables a deeper understanding and characterization of data within interconnected networks such as water distribution systems. Specifically, the project focuses on developing topological signal processing-based reconstruction algorithms to monitor water flow and detect anomalies, such as leakages, in real-time. By leveraging the structure of water distribution networks, this approach introduces more efficient data modeling methods that can predict and optimize water flow, while addressing challenges like water demand variability and leakage identification. These advancements represent a significant step toward more intelligent, adaptive, and sustainable water management systems.
SMART CONTRACTS:
One of the big things in the research area covered by this project is primarily focused on the intersection of smart contracts and water management. The project's main outcome, the ability to manage water distribution networks through Advanced Smart Contracts for Water Management, represents a significant leap forward in this field. The project proposes a pioneering tool for designing and implementing smart contracts specifically tailored to smart water distribution networks. This tool, which allows stakeholders to input parameters such as water sources, distribution points, consumption patterns, and contractual stipulations, could revolutionize the way water management contracts are created and executed.
DIGITAL TWIN:
development of a digital twin for water distribution networks. As an outcome, we will deliver a platform to support multiple data streams, including real data from the physical water distribution network, simulation data from the digital twin, and machine learning algorithms.
WATER DEMAND ANALYSIS:
Machine learning has great potential to revolutionize the energy and water distribution. It enables precise identification of individual appliance usage from smart meter data, providing real-time insights that can help users reduce consumption and save costs. Upcoming research will focus on making AI solutions more efficient, scalable, and adaptable to various household settings, driving smarter and more sustainable resource usage.
Papers:
Domenico Garlisi, Gabriele Restuccia, Ilenia Tinnirello, Francesca Cuomo and Ioannis Chatzigiannakis, “Real-Time Leakage Zone Detection in Water Distribution Networks: A Machine Learning-based Stream Processing Algorithm”, International Symposium on Algorithmic Aspects of Cloud Computing (ALGOCLOUD 2023), Amsterdam 05/09/2023
Redemptor Laceda Taloma, Jr., Danilo Comminiello (Sapienza University of Rome, Italy) Patrizio Pisani (Unidata SpA, Italy) Francesca Cuomo (University of Rome Sapienza, Italy), "UNet-WD: Deep Learning for Multi-Appliance Water Disaggregation", IFIP 1st International Workshop on Smart Water Management (SmartWater) 2024, Salonicco, June 2024
Dimitrios Amaxilatis (Spark Works Ltd., Ireland), Tiziana Cattai (Sapienza University of Rome, Italy), Antonino Pagano (University of Palermo, Italy),Ioannis Chatzigiannakis (Sapienza University of Rome, Italy), Redemptor Laceda Taloma, Jr. (Sapienza University of Rome, Italy), Domenico Garlisi (University of Palermo & CNIT Italian National Consortium for Telecommunications, Italy), Themistoklis Sarantakos (Spark Works Ltd., Ireland), Varvara Vythoulka (University of Patras, Greece), Christos Zaroliagis (University of Patras, Greece), "A Tool to Facilitate the Design of Smart Contracts in Smart Water Distribution Networks", IFIP 1st International Workshop on Smart Water Management (SmartWater) 2024, Salonicco, June 2024.
T. Cattai, S. Colonnese, D. Garlisi, A. Pagano, and Francesca Cuomo. 2024. GraphSmart: a method for green and accurate IoT water monitoring. ACM Transactions on Sensors Networks (October 2024)
P. Spadaccino, D. Garlisi, A. Franceschi, I. Tinnirello and F. Cuomo, "Accelerating Network Resource Allocation in LoRaWAN via Distributed Big Data Computing," in IEEE Access, vol. 12, pp. 141237-141250, 2024, doi: 10.1109/ACCESS.2024.3465634
A. Pagano, D. Garlisi, I. Tinnirello, F. Giuliano, G.Garbo, M. Falco, F. Cuomo, “A survey on massive IoT for water distribution systems: Challenges, simulation tools, and guidelines for large-scale deployment”, Ad Hoc Networks, 2024
Stefano Milani, Domenico Garlisi, Carlo Carugno, Christian Tedesco, and Ioannis Chatzigiannakis. "Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things." Elsevier Internet of Things 27 (2024): 101266
Our project's innovation is significantly enhanced by the active participation of our industrial partners. Unidata SpA brings a powerful infrastructure for collecting real LoRaWAN measurements for smart metering, providing accurate and reliable data essential for effective water management. Thanks to Unidata we get in touch with Acqua Pubblica Sabina SpA who has contributed valuable insights into their operational needs and challenges, ensuring that our solutions are tailored to real-world applications and are highly practical for end-users.
Our project's innovation is significantly enhanced by the active participation of our industrial partners. Unidata SpA brings a powerful infrastructure for collecting real LoRaWAN measurements for smart metering, providing accurate and reliable data essential for effective water management. Thanks to Unidata we get in touch with Acqua Pubblica Sabina SpA who has contributed valuable insights into their operational needs and challenges, ensuring that our solutions are tailored to real-world applications and are highly practical for end-users.
We started a research activity on the development of a digital twin for water distribution networks. The first phase of the activity involves a comprehensive study of the existing state of the art. We proceed to the definition of platform requirements. This platform is envisioned to support multiple streams, including real data from the physical water distribution network, simulation data from the digital twin, and machine learning algorithms. The goal is to create a system that can leverage all these data sources effectively, providing valuable insights for decision-making and predictive maintenance.
Our project's innovation is significantly enhanced by the active participation of our industrial partners. Unidata SpA brings a powerful infrastructure for collecting real LoRaWAN measurements for smart metering, providing accurate and reliable data essential for effective water management. Thanks to Unidata we get in touch with Acqua Pubblica Sabina SpA who has contributed valuable insights into their operational needs and challenges, ensuring that our solutions are tailored to real-world applications and are highly practical for end-users.
We started a research activity on the development of a digital twin for water distribution networks. The first phase of the activity involves a comprehensive study of the existing state of the art. We proceed to the definition of platform requirements. This platform is envisioned to support multiple streams, including real data from the physical water distribution network, simulation data from the digital twin, and machine learning algorithms. The goal is to create a system that can leverage all these data sources effectively, providing valuable insights for decision-making and predictive maintenance.
Program partners:
- Università di Catania
- Università di Roma Sapienza
- Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)
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”
- WITS researchers organized the 1st International Workshop on Smart Water Management (SmartWater) in IFIP/IEEE Networking 2024, held in Thessaloniki, Greece, June 3-6, 2024 (https://networking.ifip.org/2024/index.php/workshops/smartwater).
- R. Taloma participated to “Individuare le perdite idriche con l'IA?” for podcast “Tutto Connesso”.
- Francesca Cuomo gave a Keynote entitled “Unlocking IoT Potential: Empowering LoraWAN for Secure, Distributed Smart Water Management” at the IFIP NETWORKING Conference in Salonicco (Greece) in June 2024
Publications
- Expected: at least 9 publications on 36 months
- Accomplished: 10
- Readiness level: 160%
- Expected: >=30% joint publications on 36 months
- Accomplished: 10 joint publications over 3
- Readiness level: 333%
- Expected: 15 talks or event chairing/organizing within WITS activities on 36 months
- Accomplished: 17 (among dissemination events and conference presentations)
- Readiness level: 170%
- Expected: 1 PoCs expected by the end of the project
- Accomplished: 2 Demos and 1 PoC (use of a real data set by Acqua Pubblica Sabina)
- Readiness level: 300% (work according to plan)
- Expected: > 36 meetings
- Accomplished: 40 meetings
- Readiness level: 166%
- 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)
Researchers involved: We expect about 63 PM as overall. We already spent about 40 PM up to M24.
Collaboration proposals
Ioannis Chatzigiannakis and Domenico Garlisi are preparing a proposal titled “Driving the Digital Transformation of Water Supply Systems for Sustainability in the Next Generation and Beyond” (WaterBeyond) to be submitted to the HORIZON-MSCA-2024-DN-01-01 call.
The proposal aims to provide the required expertise and effort in research and training to a workforce of 15 Doctoral Candidates (DCs) in water management technologies and data analysis challenges at the frontier of the future ICT network sector.
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.
- Submitted a project to Bando Galileo 2025 Università Italo-Francese: title “Graph Machine Learning for Water Distribution Networks: from leakage detection to sustainable smart contracts” in collaboration with CentraleSupélec, Université Paris-Saclay
- We supported CNIT in becoming a new member of the LoRaWAN Alliance
- Collaboration proposal with “The WATER council”
- We submitted the Doctoral Network 2024 NETWORK HORIZON-MSCA-2024-DN-01-01 Project Acronym: WaterBeyond, Title: Driving the Digital Transformation of Water Supply Systems for Sustainability in the Next Generation and Beyond, PI Prof. Ioannis Chatzigiannakis 1 UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA IT Coordinator, (11 Partners and 4 Associated)
- A collaboration started with Prof. Andrea Cominola in the Smart Water Networks lab at the Einstein Center Digital Future and Technische Universität Berlin. A PhD student (Redemptor Jr Taloma, is spending part of his phd there).
For any proposal of collaboration within the project please contact the project PI.