An efficient system for water leak detection and localization based on IoT and lightweight deep learning

Abstract

Nowadays, Water Distribution Networks (WDNs) efficiency is limited due to leakage issues, which result in large water losses that exceed about one third of the input volume. Considering the problems of growing population and water scarcity especially in Africa and Asia continents, an urgent humanitarian need arisen to efficiently detect and localize pipeline water leaks. To address this need, this paper proposes an end-to-end system for leak detection and localization in water pipelines based on IoT and deep learning. The IoT solution is deployed allowing real time monitoring of pipelines by using non-invasive IoT devices equipped with acoustic sensors which are installed on the pipeline surface. A lightweight one-Dimensional Convolutional Neural Network (1D-CNN) classification model is embedded on these devices to detect leaks. This model uses raw audio signals generated by sensors as input information. Information about the detected leaks is then remotely transmitted using long range and low energy LoRaWAN protocol to the LoRa gateway. The gateway relays messages to a data processing Server. A 1D-CNN regression model has been then created and deployed in this server to directly estimate the 2D position of the leak. As an illustration, An IoT device prototype is designed and used as a case study to test the performance of the proposed system. We demonstrated that our proposal achieves an accuracy of 97 % for detecting leaks and can fix the 2D position of leak with an error of 0.0006 on the X coordinate and 0.0004 on the Y coordinate. Experiments confirm the effectiveness of the proposed solution.