Smoothing Sensor Data in a Controlled IoT Framework with Moving Averages

• Published in: Proceedings - 2023 IEEE Regional Symposium on Micro and Nanoelectronics, RSM 2023
• Main Author: Zulhakim A.M.; Fazlida Hanim Abdullah W.; Abdul Halim I.S.; Binti Haji Mamat R.; Alif Muslan M.I.; Zaki Abu Bakar A.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179853505&doi=10.1109%2fRSM59033.2023.10326949&partnerID=40&md5=a540d8a0b7396070fe1e5ed23368b7ce

This research presents a study on the effect of incorporating two moving averaging algorithms to an ESP32 microcontroller which was connected to an Extended Gate Field Effect Transistor (EGFET) sensor and an IoT framework via Wi-Fi. The collected data parameters are shown on an analytical platform which was installed in the cloud server and saved inside the server's database. The Simple Moving Average (SMA) algorithm was used to remove noise in the data by taking an average of values over a period, but this method is prone to having large delays especially with larger sampling rates. Therefore, the study proposes an enhanced version of the SMA algorithm called Enhanced Simple Moving Average (ESMA) which incorporates a second period to perform the averaging twice. The results show that ESMA significantly reduces the initialization period while keeping the sensor values stable. © 2023 IEEE.