Smart Wireless Particulate Matter Sensor Node for IoT-Based Strategic Monitoring Tool of Indoor COVID-19 Infection Risk via Airborne Transmission

C. Bambang Dwi Kuncoro (), Cornelia Adristi and Moch Bilal Zaenal Asyikin
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C. Bambang Dwi Kuncoro: Department of Refrigeration, Air Conditioning and Energy Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan
Cornelia Adristi: Department of Electrical Engineering, Faculty of Engineering, Universitas Indonesia, Depok 16424, Indonesia
Moch Bilal Zaenal Asyikin: Department of Refrigeration, Air Conditioning and Energy Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan

Sustainability, 2022, vol. 14, issue 21, 1-23

Abstract: Indoor and outdoor air pollution are associated with particulate matter concentration of minute size that deeply penetrates the human body and leads to significant problems. These particles led to serious health problems and an increased spread of infection through airborne transmission, especially during the COVID-19 pandemic. Considering the role of particulate matter during the spread of COVID-19, this paper presents a smart wireless sensor node for measuring and monitoring particulate matter concentrations indoors. Data for these concentrations were obtained and used as a risk indicator for airborne COVID-19 transmission. The sensor node was designed to consider air quality monitoring device requirements for indoor applications, such as real-time, continuous, reliable, remote, compact-sized, low-cost, low-power, and accessible. Total energy consumption of the node during measurement and monitoring of particulate matter concentration was minimized using a low-power algorithm and a cloud storage system embedded during software development. Therefore, the sensor node consumed low energy for one cycle of the particulate matter measurement process. This low-power strategy was implemented as a preliminary design for the autonomous sensor node that enables it to integrate with an energy harvester element to harvest energy from ambient (light, heat, airflow) and store energy in the supercapacitor, which extends the sensor node life. Furthermore, the measurement data can be accessed using the Internet of Things and visualized graphically and numerically on a graphical user interface. The test and measurement results showed that the developed sensor node had very small measurement error, which was promising and appropriate for indoor particulate matter concentration measurement and monitoring, while data results were utilized as strategic tools to minimize the risk of airborne COVID-19 transmission.

Keywords: particulate matter; COVID-19; sensor node; Internet of Things; low power (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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