Beyond SMA and EMA: Demonstrating the Strength of the Kalman Filter for Embedded Sensor Smoothing
Reda ADJAR, Fathallah RERHRHAYE, Ahmed ELAKKARY, Jihane CHTIOUI, Badr RERHRHAYE, Ilyas LAHLOUH, Nacer SEFIANI
Abstract. The study offers an overall comparison between the simple moving average (SMA) algorithm, the exponential moving average (EMA) algorithm, and the Kalman filter algorithm used as a filter to smooth out the temperatures. The overall comparison has been done using the simulated and actual measures taken by the DHT22 sensors when implemented on resource-limited microcontrollers used by the Internet of Things (IoT) environment. The results reveal that the MAE value is 0.3592, the measure of the level of instability is 8.66%, and the value of robustness is 88.19% when the Kalman filter algorithm is used.
Keywords
IoT Sensor Data and Processing, Embedded Systems, Signal Filtering-SMA and EMA, The Kalman Filter, Noise Reduction and How to Handle Outliers, Applications of Microcontrollers, DHT22 Temperature Sensor
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Reda ADJAR, Fathallah RERHRHAYE, Ahmed ELAKKARY, Jihane CHTIOUI, Badr RERHRHAYE, Ilyas LAHLOUH, Nacer SEFIANI, Beyond SMA and EMA: Demonstrating the Strength of the Kalman Filter for Embedded Sensor Smoothing, Materials Research Proceedings, Vol. 64, pp 371-378, 2026
DOI: https://doi.org/10.21741/9781644904091-46
The article was published as article 46 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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