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Cyber-Physical System for Environmental Monitoring Based on Deep Learning.

Íñigo Monedero1, Julio Barbancho1, Rafael Márquez2

  • 1Tecnología Electrónica, Escuela Politéncia Superior, Universidad de Sevilla, Calle Virgen de África 7, 41012 Sevilla, Spain.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary

This study introduces a deep learning sound classification system using convolutional neural networks (CNNs) for cyber-physical systems (CPS). The system accurately identifies anuran vocalizations, demonstrating feasibility for environmental monitoring.

Keywords:
Internet of Thingsconvolutional neural networkcyber-physical systemsdeep learningmachine learningpassive active monitoring

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Area of Science:

  • Bioacoustics
  • Machine Learning
  • Cyber-Physical Systems (CPS)

Background:

  • Internet of Things (IoT) monitoring presents challenges in extracting valuable environmental data.
  • Cyber-physical systems (CPS) offer a flexible platform for advanced monitoring applications.

Purpose of the Study:

  • To develop and evaluate a deep learning sound classification system for deployment on CPS.
  • To classify vocalizations of anuran species using convolutional neural networks (CNNs).

Main Methods:

  • Implementation of a CNN-based sound classification system.
  • Utilizing mel-spectrograms for audio feature extraction.
  • Deployment and execution on cyber-physical systems (CPS) with Internet of Things (IoT) nodes.

Main Results:

  • Achieved excellent classification accuracy of 97.53% for anuran vocalizations.
  • Demonstrated the effectiveness of CNNs and mel-spectrograms for environmental sound classification.
  • Confirmed the feasibility of deploying CNNs on low-cost CPS for extensive monitoring.

Conclusions:

  • The proposed CNN system is highly effective for classifying biological acoustic targets and analyzing biodiversity.
  • CPS integration enables dynamic configuration and remote updates of CNN models for IoT nodes.
  • This approach is a promising, resource-efficient solution for monitoring natural environments.