IDLab, Ghent University-imec, 9052 Ghent, Belgium.
Sensors (Basel). 2023 Feb 23;23(5):2459. doi: 10.3390/s23052459.
A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment.
健康安全的室内环境是遏制 2019 年冠状病毒病(COVID-19)大流行的重要组成部分。因此,本工作提出了一个实时物联网(IoT)软件架构,用于自动计算和可视化 COVID-19 气溶胶传播风险估计。这种风险估计基于室内气候传感器数据,如二氧化碳(CO)和温度,这些数据被输入到语义流处理平台 Streaming MASSIF 中进行计算。结果在动态仪表板上可视化,该仪表板根据数据的语义自动建议适当的可视化。为了评估完整的架构,分析了 2020 年 1 月(COVID-19 前)和 2021 年 1 月(COVID-19 中)学生考试期间的室内气候。相互比较后,我们观察到 2021 年的 COVID-19 措施导致室内环境更安全。