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Updated: Jan 11, 2026

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
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Lightweight Signal Processing and Edge AI for Real-Time Anomaly Detection in IoT Sensor Networks.

Manuel J C S Reis1

  • 1Engineering Departement and IEETA, University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal.

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|November 13, 2025
PubMed
Summary

This study introduces a lightweight framework for real-time anomaly detection in Internet of Things (IoT) sensor networks. It efficiently processes time-series data at the edge, reducing latency and energy use for critical applications.

Keywords:
Internet of Things (IoT)anomaly detectionedge computingmachine learningsignal processing

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Area of Science:

  • Signal Processing
  • Machine Learning
  • Internet of Things (IoT)

Background:

  • IoT devices generate vast amounts of time-series data requiring efficient processing.
  • Real-time signal analysis is critical for applications like predictive maintenance and environmental sensing.
  • Edge computing offers solutions to reduce latency and communication overhead in IoT networks.

Purpose of the Study:

  • To propose a lightweight framework for edge-based anomaly detection in IoT sensor networks.
  • To combine classical signal processing with edge machine learning for efficient data analysis.
  • To evaluate the performance and resource efficiency of different models for anomaly detection.

Main Methods:

  • Feature extraction using Fourier and Wavelet transforms.
  • Deployment of machine learning models (Shallow Neural Network, Quantized TinyML, Decision Trees) on edge devices.
  • Testing with synthetic vibration, acoustic, and environmental time-series datasets.

Main Results:

  • Shallow Neural Network achieved high detection performance (F1-score ≈ 0.94).
  • Quantized TinyML model provided a good balance of performance (F1-score ≈ 0.92) with significantly reduced memory and energy usage.
  • Decision Trees offered low latency suitable for highly constrained devices.

Conclusions:

  • The proposed framework enables accurate and resource-efficient anomaly detection directly on IoT edge devices.
  • Edge-based processing is feasible for large-scale IoT sensor networks, improving operational efficiency.
  • The approach demonstrates robustness against common data imperfections and variations.