An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements
The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO<sub>2</sub> concentrations as a proxy for exhaled air can help...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/12/4377 |
| _version_ | 1827656761184616448 |
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| author | Alexander Rusch Thomas Rösgen |
| author_facet | Alexander Rusch Thomas Rösgen |
| author_sort | Alexander Rusch |
| collection | DOAJ |
| description | The COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO<sub>2</sub> concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min. |
| first_indexed | 2024-03-09T22:33:51Z |
| format | Article |
| id | doaj.art-6e6f4976db7242beb83c8d76596decde |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T22:33:51Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6e6f4976db7242beb83c8d76596decde2023-11-23T18:52:30ZengMDPI AGSensors1424-82202022-06-012212437710.3390/s22124377An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate MeasurementsAlexander Rusch0Thomas Rösgen1Institute of Fluid Dynamics, ETH Zurich, 8092 Zurich, SwitzerlandInstitute of Fluid Dynamics, ETH Zurich, 8092 Zurich, SwitzerlandThe COVID-19 pandemic has emphasized the need for infection risk analysis and assessment of ventilation systems in indoor environments based on air quality criteria. In this context, simulations and direct measurements of CO<sub>2</sub> concentrations as a proxy for exhaled air can help to shed light on potential aerosol pathways. While the former typically lack accurate boundary conditions as well as spatially and temporally resolved validation data, currently existing measurement systems often probe rooms in non-ideal, single locations. Addressing both of these issues, a large and flexible wireless array of 50 embedded sensor units is presented that provides indoor climate metrics with configurable spatial and temporal resolutions at a sensor response time of 20 s. Augmented by an anchorless self-localization capability, three-dimensional air quality maps are reconstructed up to a mean 3D Euclidean error of 0.21 m. Driven by resolution, ease of use, and fault tolerance requirements, the system has proven itself in day-to-day use at ETH Zurich, where topologically differing auditoria (at-grade, sloped) were investigated under real occupancy conditions. The corresponding results indicate significant spatial and temporal variations in the indoor climate rendering large sensor arrays essential for accurate room assessments. Even in well-ventilated auditoria, cleanout time constants exceeded 30 min.https://www.mdpi.com/1424-8220/22/12/4377COVID-19 pandemicCO<sub>2</sub> measurementairborne transmissionindoor climate sensinginternet of thingswireless sensor array |
| spellingShingle | Alexander Rusch Thomas Rösgen An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements Sensors COVID-19 pandemic CO<sub>2</sub> measurement airborne transmission indoor climate sensing internet of things wireless sensor array |
| title | An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements |
| title_full | An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements |
| title_fullStr | An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements |
| title_full_unstemmed | An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements |
| title_short | An Internet of Things Sensor Array for Spatially and Temporally Resolved Indoor Climate Measurements |
| title_sort | internet of things sensor array for spatially and temporally resolved indoor climate measurements |
| topic | COVID-19 pandemic CO<sub>2</sub> measurement airborne transmission indoor climate sensing internet of things wireless sensor array |
| url | https://www.mdpi.com/1424-8220/22/12/4377 |
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