Adaptive algorithm for microsensor in sustainable environmental monitoring

TL;DR

This paper introduces an adaptive Fourier-based algorithm for microsensors that reduces power consumption by activating only during events, while maintaining data integrity for environmental and structural defect detection.

Contribution

It presents a novel harmonic analysis algorithm that enables sensors to efficiently identify events and conserve energy in sustainable environmental monitoring.

Findings

Reduces sensor power consumption by activating only during events

Maintains accuracy in defect detection and structural analysis

Efficient data processing with Fourier transform and harmonic analysis

Abstract

Traditional data collection from sensors produce a lot of data, which lead to constant power consumption and require more storage space. This study proposes an algorithm for a data acquisition and processing method based on Fourier transform (DFT), which extracts dominant frequency components using harmonic analysis (HA) to identify frequency peaks. This algorithm allows sensors to activate only when an event occurs, while preserving critical information for detecting defects, such as those in the surface structures of buildings and ensuring accuracy for further predictions.