Commonalities and Differences in ML-Pipelines for Air Quality Systems

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.