Monitoring air quality in cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. This paper presents an innovative IoT approach for highly granular air quality mapping in cities relying on (1) a combination of cloud-calibrated fixed and mobile air quality sensors and (2) machine learning approaches to infer the collected spatiotemporal point measurements in both space and time. Within this work, we focus on validation of this IoT approach by presenting data quality improvements of the cloud calibration algorithm and performance metrics of two spatiotemporal inference models (AVGAE and GRF). The observed cloud calibration improvements and model inference results approaching current physical state-of-the-art models demonstrate the potential of our approach in achieving accurate highly granular air quality maps and ultimately better air quality assessments.
Hofman, J, Nikolaou, M, Do Huu, T, Qin, X , Rodrigo Bonet, E , Philips, W , Deligiannis, N & Panzica La Manna, V 2020, Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data . in IEEE SENSORS 2020. , 9278941, Proceedings of IEEE Sensors, vol. 2020-October, IEEE, pp. 1-4, IEEE SENSORS 2020, Rotterdam, Netherlands, 25/10/20 .
Hofman, J., Nikolaou, M., Do Huu, T., Qin, X. , Rodrigo Bonet, E. , Philips, W. , Deligiannis, N. , & Panzica La Manna, V. (2020). Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data . In IEEE SENSORS 2020 (pp. 1-4). [9278941] (Proceedings of IEEE Sensors Vol. 2020-October). IEEE.
@inproceedings{807b82cd6ca74d9d842e57c3293a7741,
title = " Mapping Air Quality in IoT Cities: Cloud Calibration and Air Quality Inference of Sensor Data " ,
abstract = " Monitoring air quality in cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. This paper presents an innovative IoT approach for highly granular air quality mapping in cities relying on (1) a combination of cloud-calibrated fixed and mobile air quality sensors and (2) machine learning approaches to infer the collected spatiotemporal point measurements in both space and time. Within this work, we focus on validation of this IoT approach by presenting data quality improvements of the cloud calibration algorithm and performance metrics of two spatiotemporal inference models (AVGAE and GRF). The observed cloud calibration improvements and model inference results approaching current physical state-of-the-art models demonstrate the potential of our approach in achieving accurate highly granular air quality maps and ultimately better air quality assessments. " ,
keywords = " IoT, urban, air quality, machine learning, calibration " ,
author = " Jelle Hofman and Mania Nikolaou and {Do Huu}, Tien and Xuening Qin and {Rodrigo Bonet}, Esther and Wilfried Philips and Nikolaos Deligiannis and {Panzica La Manna}, Valerio " ,
year = " 2020 " ,
month = oct,
day = " 25 " ,
doi = " 10.1109/SENSORS47125.2020.9278941 " ,
language = " English " ,
isbn = " 978-1-7281-6802-9 " ,
series = " Proceedings of IEEE Sensors " ,
publisher = " IEEE " ,
pages = " 14 " ,
booktitle = " IEEE SENSORS 2020 " ,
note = " null Conference date: 25-10-2020 Through 28-10-2020 " ,
url = " https://2020.ieee-sensorsconference.org/ " ,
}