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

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

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review


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
Original languageEnglish
Title of host publication2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021
Number of pages9
ISBN (Electronic)9781665441087
Publication statusPublished - 2021
Event2021 IEEE International Conference on Sensing, Communication and Networking: IEEE SECON 2021 - Virtuell
Duration: 6 Jul 20219 Jul 2021


Conference2021 IEEE International Conference on Sensing, Communication and Networking
Abbreviated titleIEEE SECON 2021
Internet address

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Hardware and Architecture
  • Computer Networks and Communications


Dive into the research topics of 'Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning'. Together they form a unique fingerprint.
  • Best Paper Award

    Gherman, Markus-Philipp (Recipient), Cheng, Yun (Recipient), Gomez, Andres (Recipient) & Saukh, Olga (Recipient), 8 Jul 2021

    Prize: Prizes / Medals / Awards

Cite this