Improving Pandemic Preparedness with Low-cost Smart Lighting System Compatible BLE and CO2 Grid Sensing

The COVID-19 pandemic once again reminds us of the need for better pandemic preparedness, particularly in densely populated urban environments where citizens tend to spend most of their time in crowded indoor building environments. Unfortunately, the complex and often aging building infrastructure i...

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Hauptverfasser: Lai, Chun Yin, Downie, Marc, Schuldenfrei, Eric, Xu, Haozhou, Gao, Trinity, Huang, Howard, So, Hayden
Format: Dataset
Sprache:eng
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Zusammenfassung:The COVID-19 pandemic once again reminds us of the need for better pandemic preparedness, particularly in densely populated urban environments where citizens tend to spend most of their time in crowded indoor building environments. Unfortunately, the complex and often aging building infrastructure in most urban cities makes technologies necessary for improving pandemic preparedness, including contact tracing, indoor air quality control, as well as airborne infectious diseases surveillance, difficult to be deployed at scale. To address this need we have designed a practical environment sensing system based on spatial Bluetooth low power (BLE) packet sniffing augmented with carbon dioxide (CO2) monitoring. By analyzing the spatio-temporal correlation of the received BLE advertising packets and CO2 sensor values, our system can effectively reveal the movements of an individual occupant in the room, their CO2 exposure level, as well as indoor airflow and air quality, which when combined provide vital information to assess the risk of airborne infectious disease. Importantly, the BLE monitoring components of our design can be deployed through a simple over-the-air (OTA) firmware upgrade to currently-deployed smart light bulbs, paving the way for wide-scale deployment in real-world settings to improve a city's pandemic preparedness. This dataset was gathered in a university architecture studio. Result of our research paper can by reproduced by running the Jupyter notebooks.
DOI:10.17632/czphkwkmn8