mobile sensor network noise reduction and recalibration using a bayesian network
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2016
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Abstract
People are becoming increasingly interested in mobile air quality sensor
network applications. By eliminating the inaccuracies caused by spatial and
temporal heterogeneity of pollutant distributions, this method shows great
potential for atmospheric research. However, systems based on low-cost air
quality sensors often suffer from sensor noise and drift. For the sensing
systems to operate stably and reliably in real-world applications, those
problems must be addressed. In this work, we exploit the correlation of
different types of sensors caused by cross sensitivity to help identify and
correct the outlier readings. By employing a Bayesian network based system,
we are able to recover the erroneous readings and recalibrate the drifted
sensors simultaneously. Our method improves upon the state-of-art Bayesian
belief network techniques by incorporating the virtual evidence and adjusting
the sensor calibration functions recursively.
Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
Specifically, we have (1) designed a system based on the Bayesian belief network to detect and recover the abnormal readings, (2) developed methods to update the sensor calibration functions infield without requirement of ground truth, and (3) extended the Bayesian network with virtual evidence for infield sensor recalibration. To validate our technique, we have tested our technique with metal oxide sensors measuring NO2, CO, and O3 in a real-world deployment. Compared with the existing Bayesian belief network techniques, results based on our experiment setup demonstrate that our system can reduce error by 34.1 % and recover 4 times more data on average.
| Reference Key |
xiang2016atmosphericmobile
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|---|---|
| Authors | ;Y. Xiang;Y. Tang;W. Zhu |
| Journal | bioorganic & medicinal chemistry |
| Year | 2016 |
| DOI |
10.5194/amt-9-347-2016
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| URL | |
| Keywords |
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