Abstract
Urban health and climate change mitigation are the most significant challenges related to digital transformation and data analytics in Smart Cities and IoT architectures. In this context, improving data quality is essential to assess impacts, and high spatial resolution networks are required to obtain reliable measurements using hyperlocal IoT system sensors. However, this approach is related to significant uncertainty, not meeting the standards defined in theDirective 2008/50/EC on Ambient Air Quality and Cleaner Air for Europe because of the lack of methodology regarding sensors systems. Therefore, a new standard has emerged, CEN/TS 17660-1:2021, to regulate the evaluation of sensor systems, providing a set of experiments and statics parameters to perform a quality classification of the sensor. In this work, we perform a sensitivity analysis using a Smart Spot sensor system- that meets the Class 1 requirements - to quantify the effect of sensor inter variability and the number of replicates on the CEN/TS 17660-1:2021 to obtain more profound knowledge of the processes defined in this norm. We provide the first study that goes beyond assessing the methodology to perform a certification according to CEN/TS 17660-1:2021. Our results show that these parameters could decide the result of the classification. We provide a set of good practices for users interested in certificate their sensors systems.
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Acknowledgements
This work has been supported by the fellowship 21300/FPI/19 founded by Fundación Séneca, the pre-doctoral research associate contracts within the University of Murcia’s Research Promotion Plan and co-founded by HOP Ubiquitous S.L. Región de Murcia (Spain). For this, we would especially like to thank these three institutions for their support, which has made this work possible.
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Illueca Fernández, E., Bernabé Mulero, N., Pujante Pérez, A., Merino García, J.M., Cuevas Martínez, I., Jara Valera, A.J. (2023). CEN/TS 17660 in Air Quality Systems for Data Quality Validation and Certification over Smart Spot Air Quality Systems. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_65
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DOI: https://doi.org/10.1007/978-3-031-21333-5_65
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