Water stress is increasing worldwide due to economic, social and environmental factors related to climate changes. This situation is aggravated by ageing infrastructures, which are often responsible for remarkable water loss during distribution (e.g., 60% in some areas). According to the WorldBank the value of water loss worldwide is around $14 billions/year, but this estimate does not account operational and maintenance costs, which are the prominent costs. Moreover, water leaks and network inefficiencies often lead to increased energy consumption, and have consequences for the community.
As most pipelines are underground, detecting leaks is time-consuming and expensive operation. IoT technologies combined with automatic big data analytics and AI have the potential to provide the following benefits:
– Improve operational efficiency and quality of service;
– Reduce costs and response time in detecting/repairing leaks; – Prevent/mitigate damages;
– Perform preventive maintenance;
– Save water and energy;
– Help strengthening customer relationship;
– Create new data-based business opportunities.
I will talk about the experience developed at Sense4Green, a startup that offers an innovative IoT-based software for automatically analyzing at real-time heterogeneous data from the infrastructure, detecting water leaks, anomalous events, and trends that might lead to critical states and inefficiencies. Our system assists decisions and maintenance planning, thus improving operational efficiency and reducing costs, and it can be applied to save water and energy.
As our system relies on distributed intelligence for scalability and accuracy purposes, I will discuss the pros and limitations of employing edge intelligence, lessons learnt, accomplishments and resistances encountered while applying our system to a water district affected by severe water dispersion.
Our system detected different types of anomalies such as sensor malfunctioning, hydraulic configuration problems, anomalous water use, and probable background leaks. It is worth noting that although the district was equipped with a data collection system, data was not analyzed as it would have required the intervention of an expert.
Finally, I will discuss some aspects that can help accelerating the innovation process in the water sector, particularly the adoption of IoT and big data analytics/AI systems:
– Foster an interdisciplinary and innovative culture to employ technologies outside the traditional scope of competences;
– Improve the perceived value of IoT/big data solutions through an adequate communication and information on advanced technologies and issues;
– Facilitate utilities in their innovation process and investments for sensors deployment through an incremental approach.
– Address increasing concern for replacing workforce by automatic systems by communicating pros and limitations of automatic systems and identify areas where the human contribution is unreplaceable.
Daniela Tulone, Ph.D.
Founder and CEO Sense4Green
Daniela is a digital innovator with expertise in IoT, big data, and AI. She has a unique combination of scientific and business experiences, having worked as Computer Scientist for over 12 years in research labs such as MIT and Bell-Labs, as a digital leader in the European R&D industry, and at the European Commission. She founded and led an innovative startup, called Sense4Green, that offers intelligent monitoring systems for water management and that is in the process of being integrated into a larger project.
She received several recognitions for her innovative work, and gave several talks as invited speaker.
Daniela holds a Ph.D. in Computer Science from MIT and University of Pisa, a M.S. in Computer Science from NYU, a B.S. and M.S. in Mathematics and in Industrial Modeling from University of Catania.
Government, Enterprise, Small / Medium Enterprise, OEM
smart water, smart city, energy-efficiency, sustainability, automatic systems, big data analysis, AI, real-time monitoring, anomaly detection/analysis, critical infrastructures
CxO, VP / Director, Middle Management, Technical, Business Line Management, Operations
Government / Public Sector
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