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.
Highlights
According to the World Health Organization, air pollutants were responsible for approximately 58% of total deaths related to cardiovascular problems, with nearly one-fifth of the cases being from serious lung illnesses, and lung cancer constituted 6% of all mortalities in 2016 [1]
To implement data assimilation for the proposed low-cost wireless sensor networks (LWSN), the following standard extended Kalman filtering Algorithm 1 is applied for the data collected y∗k−1 from the LWSN and the concentrations of the air pollutants of interest yk in association with the emissions process described by Equations (3)–(5)
The results obtained indicate that the proposed LWSN with extended fractional-order Kalman filtering (EFKF) for monitoring of air quality data from low-cost sensors offers a great alternative to air quality monitoring in local suburbs, where State-run stations can hardly cover all locations or provide a comprehensive interpretation with visualization and dashboard
Summary
According to the World Health Organization, air pollutants were responsible for approximately 58% of total deaths related to cardiovascular problems, with nearly one-fifth of the cases being from serious lung illnesses (e.g., obstructive pulmonary or respiratory infections), and lung cancer constituted 6% of all mortalities in 2016 [1]. Due to the recent proliferation of the Internet of Things (IoT) technologies, low-cost wireless sensor networks (LWSN) have been designed and have developed rapidly as local monitoring systems in conjunction with the state-run observatories to enhance the spatio-temporal distribution resolution of environmental parameters [6,7]. As these sensors are often exposed to changes of weather patterns in their 24/7 operation, it is essential to develop reliable and resilient monitoring systems for performance improvement.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
