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IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering.
Metia, Santanu; Nguyen, Huynh A D; Ha, Quang Phuc.
  • Metia S; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
  • Nguyen HAD; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
  • Ha QP; College of Engineering Technology, Can Tho University, Can Tho 900000, Vietnam.
Sensors (Basel) ; 21(16)2021 Aug 06.
Article in English | MEDLINE | ID: covidwho-1348686
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / Internet of Things / COVID-19 Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21165313

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Air Pollutants / Air Pollution / Internet of Things / COVID-19 Limits: Humans Language: English Year: 2021 Document Type: Article Affiliation country: S21165313