Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References
There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliabilit...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9690889/ |
| _version_ | 1818894032513794048 |
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| author | Georgi Tancev Federico Grasso Toro |
| author_facet | Georgi Tancev Federico Grasso Toro |
| author_sort | Georgi Tancev |
| collection | DOAJ |
| description | There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO<sub>2</sub>, and 4 ppb for O<sub>3</sub> could be achievable with a few mobile references in urban environments. |
| first_indexed | 2024-12-19T18:22:03Z |
| format | Article |
| id | doaj.art-67230987fc554aa6a5f145bb6a03258c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T18:22:03Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-67230987fc554aa6a5f145bb6a03258c2022-12-21T20:10:57ZengIEEEIEEE Access2169-35362022-01-0110139011391010.1109/ACCESS.2022.31459459690889Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile ReferencesGeorgi Tancev0https://orcid.org/0000-0003-0012-1796Federico Grasso Toro1Swiss Federal Institute of Metrology, Wabern-Bern, SwitzerlandSwiss Federal Institute of Metrology, Wabern-Bern, SwitzerlandThere has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO<sub>2</sub>, and 4 ppb for O<sub>3</sub> could be achievable with a few mobile references in urban environments.https://ieeexplore.ieee.org/document/9690889/Air quality monitoringcalibrationgas sensorInternet of Thingslow-costonline learning |
| spellingShingle | Georgi Tancev Federico Grasso Toro Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References IEEE Access Air quality monitoring calibration gas sensor Internet of Things low-cost online learning |
| title | Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References |
| title_full | Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References |
| title_fullStr | Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References |
| title_full_unstemmed | Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References |
| title_short | Stochastic Online Calibration of Low-Cost Gas Sensor Networks With Mobile References |
| title_sort | stochastic online calibration of low cost gas sensor networks with mobile references |
| topic | Air quality monitoring calibration gas sensor Internet of Things low-cost online learning |
| url | https://ieeexplore.ieee.org/document/9690889/ |
| work_keys_str_mv | AT georgitancev stochasticonlinecalibrationoflowcostgassensornetworkswithmobilereferences AT federicograssotoro stochasticonlinecalibrationoflowcostgassensornetworkswithmobilereferences |