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Related Experiment Video

Updated: Jul 10, 2025

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
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Smart Buildings: Water Leakage Detection Using TinyML.

Othmane Atanane1, Asmaa Mourhir1, Nabil Benamar1,2

  • 1School of Science and Engineering, Al Akhawayn University in Ifrane, P.O. Box 104, Hassan II Avenue, Ifrane 53000, Morocco.

Sensors (Basel, Switzerland)
|November 25, 2023
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Summary
This summary is machine-generated.

This study introduces a TinyML-powered system for detecting water leaks in buildings using acoustic data. The EfficientNet model achieved over 97% accuracy, enabling efficient, real-time water management with minimal intervention.

Keywords:
CNNEfficientNetTinyMLaccelerometeracoustic datadeep learningscalogram

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Area of Science:

  • Environmental Science
  • Computer Science
  • Engineering

Background:

  • Global water scarcity necessitates efficient water management strategies.
  • Water wastage from undetected leakages in building pipelines is a significant issue.
  • Aging infrastructure and inefficient practices exacerbate water loss.

Purpose of the Study:

  • To develop an effective, low-intervention water leak detection method for smart buildings.
  • To explore the application of edge computing and TinyML for real-time water management.
  • To enhance water utilization efficiency in water-stressed regions.

Main Methods:

  • Utilized an acoustic dataset of water leakages in PVC pipelines.
  • Preprocessed acoustic data into scalograms for analysis.
  • Applied transfer learning with five Convolutional Neural Network (CNN) variants (EfficientNet, ResNet, AlexNet, MobileNet V1, MobileNet V2).
  • Optimized the EfficientNet model for deployment on an Arduino Nano 33 BLE edge device using quantization.

Main Results:

  • The EfficientNet model achieved maximum testing accuracy of 97.45%, recall of 98.57%, precision of 96.70%, and F1 score of 97.63%.
  • The quantized EfficientNet model demonstrated low inference time (1932 ms), minimal RAM usage (255.3 KB), and small flash requirement (48.7 KB).
  • The proposed TinyML solution enables efficient, localized decision-making for water leak detection.

Conclusions:

  • TinyML and edge computing offer a viable solution for real-time water leak detection in smart buildings.
  • The developed system can significantly reduce water wastage with minimal human intervention.
  • The efficient model deployment on edge devices paves the way for cost-effective and scalable smart water management systems.