Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors
Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability....
Saved in:
| Main Author: | Mehmet Taştan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3183 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Monitoring of Indoor Air Quality in a Classroom Combining a Low-Cost Sensor System and Machine Learning
by: Ioannis D. Apostolopoulos, et al.
Published: (2025-04-01) -
Calibration of Integrated Low-Cost Environmental Sensors for Urban Air Temperature Based on Machine Learning
by: Fang Nan, et al.
Published: (2025-05-01) -
Performance of Low-Cost Air Temperature Sensors and Applied Calibration Techniques—A Systematic Review
by: Jabir Ali Abdinoor, et al.
Published: (2025-07-01) -
Optimizing Air Quality Monitoring: Comparative Analysis of Linear Regression and Machine Learning in Low-Cost Sensor Calibration
by: Runcheng Fang, et al.
Published: (2025-04-01) -
High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling
by: Slawomir Koziel, et al.
Published: (2024-10-01)