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

Item Type Book Section
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 24 ppb 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.

Authors Gherman, Markus-Philipp; Cheng, Yun; Gomez, Andres & Saukh, Olga
Language English
Subjects computer science
HSG Classification contribution to scientific community
Date 6 July 2021
Title of Book 2021 IEEE International Conference on Sensing, Communication and Networking
Depositing User Prof. Dr. Simon Mayer
Date Deposited 14 Jul 2021 12:56
Last Modified 14 Jul 2021 12:56
URI: https://www.alexandria.unisg.ch/publications/263476

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Gherman, Markus-Philipp; Cheng, Yun; Gomez, Andres & Saukh, Olga: Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning. In 2021 IEEE International Conference on Sensing, Communication and Networking. 2021,

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https://www.alexandria.unisg.ch/id/eprint/263476
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