Distributed multi-scale calibration of low-cost ozone sensors in wireless sensor networks
New advances in sensor technologies and communications in wireless sensor networks have favored the introduction of low-cost sensors for monitoring air quality applications. In this article, we present the results of the European project H2020 CAPTOR, where three testbeds with sensors were deployed to capture tropospheric ozone concentrations. One of the biggest challenges was the calibration of the sensors, as the manufacturer provides them without calibrating. Throughout the paper, we show how short-term calibration using multiple linear regression produces good calibrated data, but instead produces biases in the calculated long-term concentrations. To mitigate the bias, we propose a linear correction based on Kriging estimation of the mean and standard deviation of the long-term ozone concentrations, thus correcting the bias presented by the sensors. © 2019, MDPI AG. All rights reserved.
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | artículo biblioteca |
| Language: | English |
| Published: |
Multidisciplinary Digital Publishing Institute
2019-06-01
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| Subjects: | Air pollution sensors, Calibration, Error estimation, Low-cost sensors, Wireless sensor networks, |
| Online Access: | http://hdl.handle.net/10261/200321 http://dx.doi.org/10.13039/501100000780 http://dx.doi.org/10.13039/501100003329 |
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| Summary: | New advances in sensor technologies and communications in wireless sensor networks have favored the introduction of low-cost sensors for monitoring air quality applications. In this article, we present the results of the European project H2020 CAPTOR, where three testbeds with sensors were deployed to capture tropospheric ozone concentrations. One of the biggest challenges was the calibration of the sensors, as the manufacturer provides them without calibrating. Throughout the paper, we show how short-term calibration using multiple linear regression produces good calibrated data, but instead produces biases in the calculated long-term concentrations. To mitigate the bias, we propose a linear correction based on Kriging estimation of the mean and standard deviation of the long-term ozone concentrations, thus correcting the bias presented by the sensors. © 2019, MDPI AG. All rights reserved. |
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