Projekte pro Jahr
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
Originalsprache | englisch |
---|---|
Titel | 2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021 |
Seitenumfang | 9 |
ISBN (elektronisch) | 9781665441087 |
DOIs | |
Publikationsstatus | Veröffentlicht - 2021 |
Veranstaltung | 2021 IEEE International Conference on Sensing, Communication and Networking: IEEE SECON 2021 - Virtuell Dauer: 6 Juli 2021 → 9 Juli 2021 https://secon2021.ieee-secon.org |
Konferenz
Konferenz | 2021 IEEE International Conference on Sensing, Communication and Networking |
---|---|
Kurztitel | IEEE SECON 2021 |
Ort | Virtuell |
Zeitraum | 6/07/21 → 9/07/21 |
Internetadresse |
ASJC Scopus subject areas
- Elektrotechnik und Elektronik
- Hardware und Architektur
- Computernetzwerke und -kommunikation
Fingerprint
Untersuchen Sie die Forschungsthemen von „Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning“. Zusammen bilden sie einen einzigartigen Fingerprint.Projekte
- 1 Abgeschlossen
-
LocSense - Zuverlässige Lokalisierungs- und Erkennungsdienste für kognitive Produkte
Römer, K. U., Diwold, K., Cao, N. & Saukh, O.
15/12/20 → 31/03/21
Projekt: Forschungsprojekt
Aktivitäten
- 1 Invited talk bei Konferenz oder Fachtagung
-
Mixed Signals at Large Scales: IoT Sensors for Air Quality Monitoring and Forecasting
Olga Saukh (Redner/in)
14 Jan. 2022Aktivität: Vortrag oder Präsentation › Invited talk bei Konferenz oder Fachtagung › Science to science
Auszeichnungen
-
Best Paper Award
Gherman, Markus-Philipp (Empfänger/-in), Cheng, Yun (Empfänger/-in), Gomez, Andres (Empfänger/-in) & Saukh, Olga (Empfänger/-in), 8 Juli 2021
Auszeichnung: Preise / Medaillen / Ehrungen