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

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....

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Published inSensors (Basel, Switzerland) Vol. 21; no. 16; p. 5313
Main Authors Metia, Santanu, Nguyen, Huynh A. D., Ha, Quang Phuc
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 06.08.2021
MDPI
Subjects
Accuracy
Air pollution
air quality
Batteries
Communication
Data assimilation
Data collection
Design
extended fractional-order kalman filter
Internet of Things
Kalman filters
Monitoring systems
Outdoor air quality
Pollutants
Sensors
Volatility
Wireless networks
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s21165313