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

Santanu Metia1, Huynh A D Nguyen1,2, Quang Phuc Ha1

  • 1Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia.

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|August 28, 2021
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Summary

This study introduces a reliable wireless sensor network for local air pollution monitoring using the Internet of Things (IoT). Extended fractional-order Kalman filtering (EFKF) enhances data accuracy, crucial for understanding air quality during events like wildfires and lockdowns.

Keywords:
air qualityextended fractional-order kalman filterinternet of things

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Area of Science:

  • Environmental Science
  • Sensor Technology
  • Data Science

Background:

  • Local air pollution monitoring is essential for public health and environmental management.
  • Existing wireless sensor networks (WSNs) often face challenges with data reliability and accuracy.
  • The Internet of Things (IoT) offers a platform for developing advanced environmental monitoring systems.

Purpose of the Study:

  • To develop a high-performance wireless sensor network (WSN) for local air pollution monitoring.
  • To enhance the reliability and accuracy of air quality data collection and transfer.
  • To validate the proposed system's effectiveness in real-world scenarios, including wildfire seasons and lockdown periods.

Main Methods:

  • Development of an IoT-enabled WSN utilizing low-cost, collocated sensors in a redundant configuration.
  • Implementation of extended fractional-order Kalman filtering (EFKF) for robust data assimilation and recovery of missing information.
  • Field deployment and testing of the WSN for monitoring particulate matter at a suburban site.

Main Results:

  • The proposed WSN demonstrated high performance in collecting and transferring air quality data.
  • EFKF significantly improved the reliability and accuracy of the collected air quality data.
  • The system effectively monitored particulate matter levels during critical periods like the 2019-2020 wildfire season and the 2020 COVID-19 lockdown.

Conclusions:

  • The developed IoT-based WSN provides a reliable and accurate solution for local air pollution monitoring.
  • EFKF is an effective technique for enhancing data quality in WSNs for environmental sensing.
  • The system offers potential for achieving microclimate responsiveness in local areas.