Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Intelligent Sensor Calibration: Techniques, Devices and Methodologies
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 August 2025 | Viewed by 536

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 100 words) can be sent to the Editorial Office for announcement on this website.

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 7991 KiB  
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
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop