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Microcontroller Based Edge Computing for Pipe Leakage Detection

  • 2023
  • OriginalPaper
  • Chapter
Published in:
Authors
Fulvio Lo Valvo
Giacomo Baiamonte
Giuseppe Costantino Giaconia
Published in
Applications in Electronics Pervading Industry, Environment and Society
Publisher
Springer Nature Switzerland

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Abstract

The chapter delves into the application of microcontroller-based edge computing for detecting pipe leakages, a critical issue in water distribution. It compares the computational complexity and memory footprint of three machine learning models—Multilayer Perceptron, Support Vector Machine, and Classification Tree—when implemented in a constrained microcontroller environment. The study uses an accelerometer to sense vibrations caused by water leaks and feeds the data into these ML models. The results highlight the performance and resource requirements of each model, with a particular focus on their applicability in real-world scenarios. The chapter also discusses the advantages of using low-resource algorithms like decision trees for such applications and suggests future work to improve performance while reducing hardware demands.
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previous chapter Efficient Uploading of.Csv Datasets into a Non-Relational Database Management System
next chapter eSysId: Embedded System Identification for Vibration Monitoring at the Extreme Edge
Title
Microcontroller Based Edge Computing for Pipe Leakage Detection
Authors
Fulvio Lo Valvo
Giacomo Baiamonte
Giuseppe Costantino Giaconia
Copyright Year
2023
Book
Applications in Electronics Pervading Industry, Environment and Society

Print ISBN: 978-3-031-30332-6

Electronic ISBN: 978-3-031-30333-3

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-3-031-30333-3_3
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