Author: Metia, Santanu; Nguyen, Huynh A. D.; Ha, Quang Phuc
Title: IoT-Enabled Wireless Sensor Networks for Air Pollution Monitoring with Extended Fractional-Order Kalman Filtering Cord-id: dd0hvtzh Document date: 2021_8_6
ID: dd0hvtzh
Snippet: 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 effectiven
Document: 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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