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

Santanu Metia; Huynh A D Nguyen; Quang Phuc Ha

doi:10.3390/s21165313

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

Sensors (Basel). 2021 Aug 6;21(16):5313. doi: 10.3390/s21165313.

Authors

Santanu Metia¹, Huynh A D Nguyen^{1

2}, Quang Phuc Ha¹

Affiliations

¹ Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.
² College of Engineering Technology, Can Tho University, Can Tho 900000, Vietnam.

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.

Keywords: air quality; extended fractional-order kalman filter; internet of things.

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19*
Communicable Disease Control
Environmental Monitoring
Humans
Internet of Things*
Reproducibility of Results
SARS-CoV-2

Substances

Air Pollutants