A water meter reading middleware for smart consumption monitoring

The efficient water resource management represents an always more modern and important theme. During last years, debates on water scarcity present on hearth have been quite heated, along with several considerations about the current water supply network inadequacy and obsolescence In fact, timeworn...

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Bibliographic Details
Published in2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) pp. 1 - 6
Main Authors Casale, Antonio, Spadafina, Luana, Porcelli, Alessandro, Matrino, Diego, Sarcina, Vincenzo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2016
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Summary:The efficient water resource management represents an always more modern and important theme. During last years, debates on water scarcity present on hearth have been quite heated, along with several considerations about the current water supply network inadequacy and obsolescence In fact, timeworn and often bad maintained infrastructures highly contribute to Non-Revenue Water (NWR) increasing. Various water consumption monitoring systems have been proposed taking advantage of the new ICT progresses, above all concerning sensors, connectivity and data processing. This paper covers the state of the art protocols and technologies for remote water metering and reading transmission, focusing on the typical Italian apartment building case study. Anyway, this case can be extended to a quite common context worldwide. Starting from the analysis and the obtained model, a software layer on top of this portion of water supply network is designed to allow third party software to access data and provide different services depending on the stakeholders (householders or water authority). This communication interface, often regarded as middleware, makes the whole monitoring system part of a wide informative network that could be assimilated to those technologies linked to the smart city subject. Finally, a possible smart application making advantage of water consumption dataset and other information and using classification algorithms is presented.
DOI:10.1109/EESMS.2016.7504814