Projects per year
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
Original language | English |
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Title of host publication | 2021 18th IEEE International Conference on Sensing, Communication and Networking, SECON 2021 |
Number of pages | 9 |
ISBN (Electronic) | 9781665441087 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE International Conference on Sensing, Communication and Networking: IEEE SECON 2021 - Virtuell Duration: 6 Jul 2021 → 9 Jul 2021 https://secon2021.ieee-secon.org |
Conference
Conference | 2021 IEEE International Conference on Sensing, Communication and Networking |
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Abbreviated title | IEEE SECON 2021 |
City | Virtuell |
Period | 6/07/21 → 9/07/21 |
Internet address |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Hardware and Architecture
- Computer Networks and Communications
Fingerprint
Dive into the research topics of 'Compensating Altered Sensitivity of Duty-Cycled MOX Gas Sensors with Machine Learning'. Together they form a unique fingerprint.Projects
- 1 Finished
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LocSense - Dependable Localization and Sensing Services for cognitive products
Römer, K. U., Diwold, K., Cao, N. & Saukh, O.
15/12/20 → 31/03/21
Project: Research project
Activities
- 1 Invited talk at conference or symposium
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Mixed Signals at Large Scales: IoT Sensors for Air Quality Monitoring and Forecasting
Olga Saukh (Speaker)
14 Jan 2022Activity: Talk or presentation › Invited talk at conference or symposium › Science to science
Prizes
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Best Paper Award
Gherman, Markus-Philipp (Recipient), Cheng, Yun (Recipient), Gomez, Andres (Recipient) & Saukh, Olga (Recipient), 8 Jul 2021
Prize: Prizes / Medals / Awards