Commonalities and Differences in ML-Pipelines for Air Quality Systems

Cezary Orlowski (), Grit Behrens () and Kostas Karatzas ()
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Cezary Orlowski: WSB Merito University Gdansk
Grit Behrens: HSBI/Bielefeld University of Applied Sciences
Kostas Karatzas: Aristotle University of Thessaloniki

A chapter in Advances and New Trends in Environmental Informatics 2023, 2024, pp 21-37 from Springer

Abstract: Abstract This paper compares three ML-pipelines in Air Quality (AQ) Systems, namely a fog layer management model for IoT-systems, a low-cost AQ sensor system with sensor calibration and data fusion competences and a ML-method research based on low-cost OpenSensorMap. The three ML-pipelines are described, commonalities and differences worked out and the advantages of every technique are led over in an effort of a combined ML-pipeline which could be realised in a scientific cooperation of the three groups.

Keywords: ML-pipelines; Fog layer management model; IoT-system; Low-cost AQ sensor (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46902-2_2

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