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The distribution of water in urban areas involves several challenges, such as maintaining pipelines, controlling pressure and flow, and monitoring water quality. In particular, the measurement of the flow rate and pressure in pipelines is essential for optimizing water distribution in cities. In recent decades, new technologies have been used to address these challenges, such as hydraulic modeling systems with software, smart sensors, and automated control systems. Among the new possibilities, the use of wireless sensor networks has been highlighted. In this sense, IoT-based nodes have been proposed as a low-cost alternative, with the ability to communicate over the Internet with low energy consumption. Thus, this work describes the necessary steps, challenges, and solutions for the development of an autonomous IoT node applicable to monitoring pressure and flow in a water supply network. In the second part of the work, the data collected by the IoT nodes was processed to eliminate outliers and used to train a model based on artificial neural networks that are capable of predicting the flow in the system under monitoring. The results show that, based on the data measured by the proposed IoT node, it is possible to predict the flow in distribution systems operating in real time.
Silva et al. (Wed,) studied this question.
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