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

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doi.org2025-01-21 收录
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http://doi.org/10.17632/czphkwkmn8.1
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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.

新冠疫情的爆发再次提醒我们提升应对突发公共卫生事件能力的必要性,尤其是在人口密集的城市环境中,市民大多在拥挤的室内建筑环境中度过大部分时间。遗憾的是,大多数城市复杂的、往往年代久远的建筑基础设施使得提高公共卫生事件应对能力的必要技术,包括接触者追踪、室内空气质量控制和空气传播传染病监测,难以大规模部署。为满足这一需求,我们设计了一种基于空间低功耗蓝牙(BLE)数据包嗅探及二氧化碳(CO2)监测的实用环境感知系统。通过分析接收到的BLE广播数据包与CO2传感器值的时空相关性,我们的系统能有效揭示室内单个占用者的移动轨迹、CO2暴露水平以及室内气流和空气质量,这些信息结合后为评估空气传播传染病的风险提供了至关重要的信息。重要的是,我们设计中的BLE监测组件可通过简单的空中传输(OTA)固件升级部署到目前部署的智能灯具上,为在城市环境中大规模部署以提升公共卫生事件应对能力铺平了道路。本数据集是在一所大学建筑工作室收集的。通过运行Jupyter笔记本,可以重现我们研究论文的结果。
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