IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering

Metia, S; Nguyen, HAD; Ha, QP

IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering

Metia, S

Nguyen, HAD Ha, QP

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Publisher:: MDPI AG
Publication Type:: Journal Article
Citation:: Sensors, 21, (16), pp. 5313-5313

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Field	Value	Language
dc.contributor.author	Metia, S https://orcid.org/0000-0002-5676-1146
dc.contributor.author	Nguyen, HAD
dc.contributor.author	Ha, QP
dc.date.accessioned	2021-08-12T04:13:46Z
dc.date.available	2021-08-12T04:13:46Z
dc.identifier.citation	Sensors, 21, (16), pp. 5313-5313
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10453/150107
dc.description.abstract	<jats:p>This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.</jats:p>
dc.language	en
dc.publisher	MDPI AG
dc.relation.ispartof	Sensors
dc.relation.isbasedon	10.3390/s21165313
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0301 Analytical Chemistry, 0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 0502 Environmental Science and Management, 0602 Ecology
dc.subject.classification	Analytical Chemistry
dc.title	IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering
dc.type	Journal Article
utslib.citation.volume	21
utslib.for	0301 Analytical Chemistry
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0502 Environmental Science and Management
utslib.for	0602 Ecology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.date.updated	2021-08-12T04:13:33Z
pubs.issue	16
pubs.publication-status	Published online
pubs.volume	21
utslib.citation.issue	16

Abstract:

This paper presents the development of high-performance wireless sensor networks for local monitoring of air pollution. The proposed system, enabled by the Internet of Things (IoT), is based on low-cost sensors collocated in a redundant configuration for collecting and transferring air quality data. Reliability and accuracy of the monitoring system are enhanced by using extended fractional-order Kalman filtering (EFKF) for data assimilation and recovery of the missing information. Its effectiveness is verified through monitoring particulate matters at a suburban site during the wildfire season 2019–2020 and the Coronavirus disease 2019 (COVID-19) lockdown period. The proposed approach is of interest to achieve microclimate responsiveness in a local area.

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http://hdl.handle.net/10453/150107