Multisensor Data Fusion Calibration in IoT Air Pollution Platforms.
This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models.
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Language: | English |
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Institute of Electrical and Electronics Engineers
2020-01
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Subjects: | Air pollution, Multisensor Data Fusion Calibration, |
Online Access: | http://hdl.handle.net/10261/217105 http://dx.doi.org/10.13039/501100000780 |
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dig-idaea-es-10261-2171052022-12-19T10:56:35Z Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. Ferrer-Cid, Pau Barceló-Ordinas, José María García Vidal, Jorge Ripoll, Anna Viana, Mar European Commission Air pollution Multisensor Data Fusion Calibration This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models. National Spanish funding; Regional Project; EU H2020 CAPTOR Project; AGAUR SGR44; 10.13039/501100011033-Agencia Estatal de Investigación; Spanish Ministry of Economy, Industry and Competitiveness; Peer reviewed 2020-07-27T16:11:55Z 2020-07-27T16:11:55Z 2020-01 artículo http://purl.org/coar/resource_type/c_6501 IEEE Internet of Things Journal 7 (4): 3124-3132 (2020) http://hdl.handle.net/10261/217105 10.1109/JIOT.2020.2965283 http://dx.doi.org/10.13039/501100000780 en #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/H2020/688110 Postprint Barceló-Ordinas, José María; Ferrer-Cid, Pau; García Vidal, Jorge; Viana, Mar; Ripoll, Anna; 2021; H2020 project CAPTOR: raw data collected by low-cost MOX ozone sensors in a real air pollution monitoring network [Dataset]; Zenodo; Version 1; https://doi.org/ 10.5281/zenodo.4570449 10.1109/JIOT.2020.2965283 Sí open Institute of Electrical and Electronics Engineers |
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Air pollution Multisensor Data Fusion Calibration Air pollution Multisensor Data Fusion Calibration Ferrer-Cid, Pau Barceló-Ordinas, José María García Vidal, Jorge Ripoll, Anna Viana, Mar Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
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This article investigates the calibration of low-cost sensors for air pollution. The sensors were deployed on three Internet of Things (IoT) platforms in Spain, Austria, and Italy during the summers of 2017, 2018, and 2019. One of the biggest challenges in the operation of an IoT platform, which has a great impact on the quality of the reported pollution values, is the calibration of the sensors in an uncontrolled environment. This calibration is performed using arrays of sensors that measure cross sensitivities and therefore compensate for both interfering contaminants and environmental conditions. This article investigates how the fusion of data taken by sensor arrays can improve the calibration process. In particular, calibration with sensor arrays, multisensor data fusion calibration with weighted averages, and multisensor data fusion calibration with machine learning models are compared. Calibration is evaluated by combining data from various sensors with linear and nonlinear regression models. |
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European Commission |
author_facet |
European Commission Ferrer-Cid, Pau Barceló-Ordinas, José María García Vidal, Jorge Ripoll, Anna Viana, Mar |
format |
artículo |
topic_facet |
Air pollution Multisensor Data Fusion Calibration |
author |
Ferrer-Cid, Pau Barceló-Ordinas, José María García Vidal, Jorge Ripoll, Anna Viana, Mar |
author_sort |
Ferrer-Cid, Pau |
title |
Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
title_short |
Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
title_full |
Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
title_fullStr |
Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
title_full_unstemmed |
Multisensor Data Fusion Calibration in IoT Air Pollution Platforms. |
title_sort |
multisensor data fusion calibration in iot air pollution platforms. |
publisher |
Institute of Electrical and Electronics Engineers |
publishDate |
2020-01 |
url |
http://hdl.handle.net/10261/217105 http://dx.doi.org/10.13039/501100000780 |
work_keys_str_mv |
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