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dc.contributor.author
Gherman, Markus-Philipp
dc.contributor.author
Cheng, Yun
dc.contributor.author
Gomez, Andres
dc.contributor.author
Saukh, Olga
dc.date.accessioned
2021-08-13T06:41:05Z
dc.date.available
2021-08-11T04:49:09Z
dc.date.available
2021-08-11T13:08:26Z
dc.date.available
2021-08-13T06:41:05Z
dc.date.issued
2021
dc.identifier.isbn
978-1-6654-4108-7
en_US
dc.identifier.isbn
978-1-6654-3111-8
en_US
dc.identifier.other
10.1109/SECON52354.2021.9491586
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/500383
dc.description.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.
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning
en_US
dc.type
Conference Paper
dc.date.published
2021-07-26
ethz.book.title
2021 18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)
en_US
ethz.pages.start
9491586
en_US
ethz.size
9 p.
en_US
ethz.event
18th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON 2021)
en_US
ethz.event.location
Online
en_US
ethz.event.date
July 6-9, 2021
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02640 - Inst. f. Technische Informatik und Komm. / Computer Eng. and Networks Lab.::03429 - Thiele, Lothar (emeritus) / Thiele, Lothar (emeritus)
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02640 - Inst. f. Technische Informatik und Komm. / Computer Eng. and Networks Lab.::03429 - Thiele, Lothar (emeritus) / Thiele, Lothar (emeritus)
ethz.date.deposited
2021-08-11T04:49:12Z
ethz.source
SCOPUS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-08-13T06:41:11Z
ethz.rosetta.lastUpdated
2023-02-06T22:20:28Z
ethz.rosetta.versionExported
true
ethz.COinS
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