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Intelligent Sensor Calibration: Techniques, Devices and Methodologies
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Intelligent Sensor Calibration: Techniques, Devices and Methodologies

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 7134

Special Issue Editor

Dr. Yan Liu

E-Mail Website
Guest Editor
School of Mechano-Electronic Engineering, Xidian University, Xi’an 710071, China
Interests: MEMS; piezoresistive sensors; piezoelectric devices; flexible sensors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Sensor technology's evolution is a pivotal catalyst in the relentless march of the intelligent age. Within sensor development, calibration is a critical process that can establish correlations between sensor outputs and the measured quantities, thereby elucidating the sensor's response characteristics. As the trend in intelligence continues to expand in sensor research, an increasing number of available technologies, devices, and methods also innovate sensor calibration techniques.

This Special Issue focuses on new technologies, methods, and devices in sensor calibration, showing the latest achievements in intelligent development within this domain. Contributions about relevant theories, hardware platforms, and algorithms are warmly encouraged. Furthermore, topics that can inspire new ideas for future research are also appropriate.

The original research papers and comprehensive reviewing articles are all acceptable, but they must conform to the academic standard of this journal.

Dr. Yan Liu
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent sensor calibration
  • calibration theory
  • calibration device
  • methodology and algorithm

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Published Papers (2 papers)

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Research

21 pages, 2788 KB  
Article
Gaussian Process-Based Multi-Fidelity Bayesian Optimization for Optimal Calibration Point Selection
by Hua Zhuo, Jungang Ma, Mei Yang, Yikun Zhao, Lifang Yao, Yan Xu and Kun Yang
Sensors 2025, 25(22), 7030; https://doi.org/10.3390/s25227030 - 18 Nov 2025
Viewed by 657
Abstract
Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range [...] Read more.
Temperature and humidity calibration chambers, which provide controlled environments for instrument testing and validation, are widely applied in the aerospace and biomedicine fields. However, traditional fixed calibration points fail to adapt to complex operational requirements and exhibit problems including a limited coverage range and low efficiency. To address these challenges, this study develops a Gaussian Process-based Multi-Fidelity Bayesian Optimization (GP-MFBO) framework for optimal selection of temperature and humidity calibration points. The framework integrates the following three key components: (1) a three-layer progressive multi-fidelity modeling system comprising physical analytical models, computational fluid dynamics (CFD) numerical simulations, and experimental verification; (2) a systematic uncertainty quantification system covering model uncertainty, parameter uncertainty, and observation uncertainty; and (3) an adaptive acquisition function that balances uncertainty penalty mechanisms and multi-fidelity information gain evaluation. The experimental results demonstrate that the proposed GP-MFBO method achieves optimal calibration point combinations with a temperature uniformity score of 0.149 and humidity uniformity score of 2.38, approaching theoretical optimal solutions within 4.5% and 3.6%, respectively. Compared to standard Gaussian process, Co-Kriging, two-stage optimization, polynomial regression, and traditional single-fidelity methods, GP-MFBO achieves uniformity score improvements of up to 81.7% and 76.3% for temperature and humidity, respectively. The prediction confidence interval coverage reaches 94.2%, outperforming all comparative methods. This research provides a rigorous theoretical foundation and technical solution for the scientific design and reliable operation of large-space temperature and humidity calibration systems. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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21 pages, 7991 KB  
Article
Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors
by Mehmet Taştan
Sensors 2025, 25(10), 3183; https://doi.org/10.3390/s25103183 - 19 May 2025
Cited by 3 | Viewed by 3756
Abstract
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. In this study, an Internet of [...] Read more.
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. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM2.5), carbon dioxide (CO2), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO2 sensor calibration, GB achieved R2 = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM2.5 sensor, kNN delivered the most successful results, with R2 = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R2 = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article
(This article belongs to the Special Issue Intelligent Sensor Calibration: Techniques, Devices and Methodologies)
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