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Two-to-One Trigger Mechanism for Event-Based Environmental Sensing.

Nursultan Daupayev1, Christian Engel1, Sören Hirsch1

  • 1Department of Engineering, Brandenburg University of Applied Sciences, Magdeburger Str. 50, 14770 Brandenburg an der Havel, Germany.

Sensors (Basel, Switzerland)
|July 12, 2025
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Summary

This study introduces an event-driven sampling method for environmental monitoring, significantly reducing carbon dioxide (CO2) measurements by activating them only when temperature and humidity thresholds are met. This approach enhances energy efficiency and data management in smart systems.

Keywords:
data reductionenergy efficiencyevent-driven activationmultivariate sensorsstructural health monitoring

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Area of Science:

  • Environmental Science
  • Embedded Systems Engineering
  • Sensor Technology

Background:

  • Continuous environmental monitoring systems face challenges with high energy consumption, data overload, and large equipment size.
  • These limitations hinder the deployment of smart monitoring systems in space-constrained environments.
  • Efficient energy use, accurate data measurement, and compact installation remain critical unresolved issues.

Purpose of the Study:

  • To propose and evaluate a novel two-to-one threshold sampling approach for environmental monitoring.
  • To address the limitations of high energy consumption and data overload in existing systems.
  • To enable compact and efficient installation of multivariate sensors in resource-constrained settings.

Main Methods:

  • Implemented an event-driven sampling strategy activating CO2 measurements based on temperature (T) and relative humidity (RH) threshold changes.
  • Developed a low-power, small-form multivariate sensor using a PIC16LF19156 microcontroller.
  • Compared the proposed method against a commercial system and Arduino Uno-based sensors.

Main Results:

  • The two-to-one threshold sampling significantly reduced CO2 measurements by approximately 41.9% without losing essential signal characteristics.
  • Signal reconstruction demonstrated high accuracy, with a Mean Absolute Error (MAE) of 0.0089 and Root Mean Squared Error (RMSE) of 0.0117.
  • The event-driven method proved effective in minimizing power consumption and redundant data collection.

Conclusions:

  • The proposed event-driven sampling approach offers an efficient solution for energy use, data management, and compact installation in environmental monitoring.
  • The technique successfully reduces CO2 measurements while preserving signal integrity, making it suitable for resource-constrained embedded systems.
  • Future work will explore adaptive thresholds and context-dependent models to expand applicability in diverse environmental conditions.