Arduino-Based Moving Average Filter Implementation for Increasing Stability Measurement of Digital Temperature in Climate Monitoring Systems Micro
Abstract
This study evaluated the implementation of a Moving Average Filter (MAF) to improve the stability of digital temperature measurements in an Arduino-based microclimate monitoring system. Although low-cost digital sensors are widely used for real-time environmental monitoring, their readings may fluctuate because of short-term noise, electrical interference, and rapid environmental variation. This study aimed to determine whether a lightweight MAF algorithm could reduce temperature-data variability without substantially altering the overall temperature trend. A quantitative experimental design was employed using an Arduino Uno, a DHT22 digital temperature sensor, and a five-sample MAF window. Temperature data were acquired at one-second intervals under fixed sensor-position and normal environmental conditions. Raw and filtered readings were compared using descriptive statistical parameters, including mean, standard deviation, minimum value, maximum value, and data range. The results showed that the mean temperature remained comparable before and after filtering, decreasing only from 30.17 °C to 30.11 °C. However, the standard deviation decreased from 0.39 to 0.09, while the data range declined from 1.2 °C to 0.3 °C. The reduction in standard deviation indicated a 76.92% decrease in short-term measurement variability. Graphical comparisons also showed that filtered data followed a smoother pattern with fewer abrupt fluctuations than raw sensor readings. These findings indicate that the MAF can improve temperature-data stability in resource-limited embedded systems while maintaining the general environmental trend. Nevertheless, further studies should compare the method with Kalman, median, and adaptive filters under more dynamic environmental conditions and across multiple sensor types.
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