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Low-Power Embedded Sensor Node for Real-Time Environmental Monitoring with On-Board Machine-Learning Inference.

Manuel J C S Reis1

  • 1Engineering Department, Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal.

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Summary

This study introduces a low-power sensor node for real-time environmental monitoring using on-board machine learning. The design optimizes energy efficiency and reduces data transmission for smart city applications.

Keywords:
LoRaWANedge artificial intelligenceembedded sensor systemsenvironmental monitoringlow-power designmicrocontroller architectureson-device inferencequantised neural networks

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Area of Science:

  • Embedded Systems Engineering
  • Environmental Monitoring Technology
  • Machine Learning on the Edge

Background:

  • Real-time environmental monitoring requires efficient data acquisition and processing.
  • Existing sensor nodes often face power constraints and high data transmission costs.
  • On-board machine learning offers potential for localized data analysis and reduced network load.

Purpose of the Study:

  • To design and optimize a low-power embedded sensor-node architecture.
  • To integrate heterogeneous sensing for air quality and ambient parameters.
  • To implement on-board machine learning for real-time event/anomaly detection.

Main Methods:

  • Development of a modular embedded platform with a low-power microcontroller and neural inference accelerator.
  • End-to-end energy optimization using adaptive duty-cycling, hierarchical power domains, and edge-level data reduction.
  • On-device multi-class classification using quantised neural models and MATLAB Simulink simulations for system analysis.

Main Results:

  • Achieved 94% inference accuracy with 0.87 ms latency.
  • Demonstrated average power consumption of approximately 2.9 mWh for energy-autonomous operation.
  • Adaptive LoRaWAN communication reduced data transmissions by ~88% compared to periodic reporting.

Conclusions:

  • The proposed architecture enables energy-efficient environmental sensing with on-device inference.
  • On-device inference effectively reduces network traffic while maintaining reliable event detection.
  • The system supports sustainable smart-city and climate-monitoring deployments.