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Related Experiment Video

Updated: Dec 28, 2025

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

Published on: April 20, 2016

An Adaptive Genetic Algorithm of Adjusting Sensor Acquisition Frequency.

Feng Chen¹, Shouzhi Xu¹, Ying Zhao¹

¹College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China.

Sensors (Basel, Switzerland)

|February 16, 2020

Summary

This study introduces an adaptive genetic algorithm to reduce sensor data acquisition frequency for wireless sensor nodes. This method conserves power in portable meteorological stations without significant data loss.

Keywords:

data acquisition frequency regulation low power consumption wireless sensor

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Area of Science:

Environmental Science
Computer Science
Engineering

Background:

Portable meteorological stations require efficient power management for long-term environmental monitoring.
Wireless sensor nodes face power limitations, necessitating reduced energy consumption for sustained operation.

Purpose of the Study:

To develop an adaptive algorithm for optimizing sensor data acquisition frequency in environmental monitoring.
To reduce the power consumption of wireless sensor nodes by dynamically adjusting sampling rates.

Main Methods:

Analysis of historical environmental data to identify patterns and regularity.
Development of an adaptive genetic algorithm to model data acquisition strategies.
Dynamic adjustment of sensor sampling frequency based on historical data characteristics.

Main Results:

The proposed algorithm significantly reduces the data acquisition frequency.
Energy consumption of sensors is substantially decreased.
Environmental monitoring data curves are obtained with minimal error compared to fixed-frequency methods.

Conclusions:

Adaptive genetic algorithms can effectively optimize sampling frequencies for power conservation in environmental monitoring.
Dynamic adjustment of data acquisition is a viable strategy for extending the operational life of wireless sensor nodes.
The approach balances energy efficiency with data accuracy for portable meteorological stations.