Using Smart Water Meters In (Near) Real-Time On The iWIDGET System

Devices and technologies to measure and report water consumption at sub-daily intervals are growing in popularity. Data from these devices are creating new opportunities to manage the supply and demand of water in near real-time. To this end, The EU FP7 iWidget project is developing a state-of-the art analytics platform for the integrated management of urban water. Key challenges include extracting useful insights from high-resolution consumption data and exploring a range of decision-support tools for water utilities and consumers. To overcome these challenge iWIDGET is developing a distributed, open, robust, collaborative architecture that allows partners and utilities to collect and process data from a large number of sensors in parallel and analyze data on demand. We present a distributed system that enables flexible, near real-time monitoring of water networks by providing four critical mechanisms. First, a means to regularly poll water utility raw data systems. Second, assimilation of fresh data into a purposely designed, high-performance database. Third, geographically local or remote analytic systems poll the database to incorporate the latest consumption information in their analysis. Lastly, an online portal based platform is used to trigger analysis and review results. A key architectural feature of this system is a loose coupling between central storage and analytic systems. Communication between the central storage and processing components utilizes standard techniques, including WaterML, over RESTful web services. This arrangement avoids restrictions on the underlying technologies in analytical components and allows analytic systems to execute on different operating systems and run-times. The system is under active development and will enable a wide variety of tools for water utilities and individual consumers. Acknowledgement: The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 31827