Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning

Markus-Philipp Gherman, Yun Cheng, Andres Gomez, Olga Saukh

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

Abstract

Popular low-cost air quality sensors embedded into IoT and mobile devices are based on metal oxides (MOX) that change their electrical resistance in response to ambient pollutants emitted as gases. Operating MOX sensors continuously is expensive, since it requires to heat up and maintain a hotplate at several hundred degrees. To save energy, sensors are commonly duty cycled with short on-times and long off-times. However, doing so adversely affects the sensor's chemical reactions, which have slower transients as the off-time increases. As a result, sensor sensitivity to various gases deviates from a continuously powered sensor. In this paper, we show that it is possible to recover accurate continuous-sensor measurements from transient responses obtained from a duty cycled sensor and compensate for an altered multi-gas cross-sensitivity profile using machine learning methods. On a test set, we achieve a mean absolute error (MAE) of 24ppb between continuous ground-truth measurements and obtained model predictions of tVOC. This results in estimating 86.6% of Indoor Air Quality (IAQ) levels correctly compared to 68.1% if no correction is used. Our models are invariant to minor baseline shifts and work for both tVOC and CO2-eq signals provided by the sensor. Thanks to our models, 98.5% of the energy consumption can be reduced while maintaining high accuracy. This optimization enables energy-harvesting-based operation of IAQ sensors in indoor IoT scenarios
Originalspracheenglisch
Titel2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021
Seitenumfang9
ISBN (elektronisch)9781665441087
DOIs
PublikationsstatusVeröffentlicht - 2021
Veranstaltung2021 IEEE International Conference on Sensing, Communication and Networking: IEEE SECON 2021 - Virtuell
Dauer: 6 Juli 20219 Juli 2021
https://secon2021.ieee-secon.org

Konferenz

Konferenz2021 IEEE International Conference on Sensing, Communication and Networking
KurztitelIEEE SECON 2021
OrtVirtuell
Zeitraum6/07/219/07/21
Internetadresse

ASJC Scopus subject areas

  • Elektrotechnik und Elektronik
  • Hardware und Architektur
  • Computernetzwerke und -kommunikation

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  • Best Paper Award

    Gherman, M. (Empfänger/-in), Cheng, Y. (Empfänger/-in), Gomez, A. (Empfänger/-in) & Saukh, Olga (Empfänger/-in), 8 Juli 2021

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