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Title	Calibration Of Ground-Ozone Low-Cost Sensors Embedded In Iot Nodes By Applying Machine Learning Techniques
ID_Doc	13244
Authors	Montalban-Faet G.; Meneses-Albala E.; Felici-Castell S.; Solano J.J.P.; Garcia J.S.; Navarro-Camba E.
Year	2025
Published	Proceedings of the 12th Euro-American Conference on Telematics and Information Systems, EATIS 2024
DOI	http://dx.doi.org/10.1145/3685243.3685283
Abstract	Air Quality (AQ) is an important issue in our cities, as it has an impact on the health of citizens. Nowadays with advanced communications and a high degree of digitisation, smart cities can integrate AQ monitoring networks based on wireless Internet of Things (IoT) sensor nodes, which for large deployments are often built with low-cost and less accurate components. In this paper, we focus on the calibration process of ozone Low-Cost Sensors (LCS) in order to improve their accuracy by using machine learning ensemble techniques and reduce their error compared with official measurements of this parameter in a real deployment. We have tested and compared different techniques, reducing the error of the reading values of these LCSs by around 90%, with the best results given by the Gradient Boosting algorithm. © 2024 Owner/Author.
Author Keywords	adaptative boost; air quality; calibration; gradient boosting; IoT; low-cost sensors; machine learning; ozone; random forest; smart cities

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