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Sensors in 2025
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Sensors in 2025

A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 March 2025) | Viewed by 2111

Special Issue Editors

Dr. Luigi Patrono

E-Mail Website1 Website2
Guest Editor
Department of Innovation Engineering, University of Salento, Via Monteroni, 73100 Lecce, Italy
Interests: Internet of Things; computer networks; cloud networks; RFID and BLE technologies; localization; smart environments; AAL systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Leopoldo Angrisani

E-Mail Website
Guest Editor
Department of Information Technology and Electrical Engineering, University of Napoli Federico II, Naples, Italy
Interests: measurement in the IoT field and, more generally, in the Industry 4.0 and Health 4.0 fields; cyber-physical measurement systems; measurement of ICT systems sustainability and sustainability of measurements; operation and performance assessment of communication systems, equipment, and networks; measurement uncertainty; impact of quantum technologies on measurements; metrological characterization of advanced human-to-machine interfaces
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vittorio M. N. Passaro

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Guest Editor
Dipartimento di Ingegneria Elettrica e dell'Informazione (Department of Electrical and Information Engineering), Politecnico di Bari, Via Edoardo Orabona n. 4, 70125 Bari, Italy
Interests: optoelectronic technologies; photonic devices and sensors; nanophotonic integrated sensors; non linear integrated optics; microelectronic and nanoelectronic technologies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue, entitled “Sensors in 2025”, which is part of the MDPI journal New Year Special Issue Series. This Special Issue will be a collection of high-quality reviews and original research articles from Advisory Board Members, Editors-in-Chief, Editorial Board Members, Guest Editors, Topical Advisory Panel Members, Reviewer Board Members, Societies, Authors, and Reviewers from Sensors, in addition to excellent editorials from high-profile scholars in the sensors field. Submissions on all aspects of sensors and sensing technologies are welcome.

We welcome submissions from all authors, irrespective of gender.

Dr. Luigi Patrono
Prof. Dr. Leopoldo Angrisani
Prof. Dr. Vittorio M. N. Passaro
Guest Editors

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

  • physical sensors
  • chemical sensors
  • biosensors
  • biomedical sensors
  • lab-on-a-chip
  • remote sensors
  • sensor networks

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

Related Special Issue

Published Papers (4 papers)

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Research

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23 pages, 16173 KiB  
Article
Enhanced Prediction of Soil Carbon via Encoder-Decoder Neural Networks for a Boreal Study Area in Northern Ontario
by Rory Pittman and Baoxin Hu
Sensors 2025, 25(8), 2583; https://doi.org/10.3390/s25082583 - 19 Apr 2025
Viewed by 58
Abstract
Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study [...] Read more.
Addressing the impacts of carbon in connection with land cover conversion and climate change is of predominant interest for boreal realms. Consequently, boosting accuracy for the prediction of total carbon (C) with soil mapping is a crucial objective, particularly for a boreal study area under risk of land cover transition in northern Ontario, Canada. To enhance the prediction of soil modeling, integrated approaches combining encoder-decoder (ED) with dense neural network (DNN) and convolutional neural network (CNN) formulations suitable for smaller target data sets were developed. These methods were able to effectively extract dominant features within predictor data and augment modeling accuracy. The obtained results were compared with those attained from structural equation modeling (SEM) and random forest (RF), as well as basic DNN and CNN models. A model ensemble based on all approaches was also considered, from which standard deviations were calculated to gauge the prediction uncertainty. Quantile mappings with respect to deciles were also derived from the model ensemble to provide additional insights with prediction. Validation accuracies for the ED-CNN model attained a coefficient of determination (R2) of 0.59. The greatest deviations with predicting C contents corresponded to the wetlands. However, when quantified by decile mapping, forested localities within river valleys encountered the highest uncertainties with prediction, indicting a need for better modeling of sites with intermediate concentrations of soil C. Full article
(This article belongs to the Special Issue Sensors in 2025)
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14 pages, 5317 KiB  
Article
LITES-Based Sensitive CO2 Detection Using 2 μm Diode Laser and Self-Designed 9.5 kHz Quartz Tuning Fork
by Junjie Mu, Jinfeng Hou, Shaoqi Qiu, Shunda Qiao, Ying He and Yufei Ma
Sensors 2025, 25(7), 2099; https://doi.org/10.3390/s25072099 - 27 Mar 2025
Viewed by 180
Abstract
A carbon dioxide (CO2) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant [...] Read more.
A carbon dioxide (CO2) sensor based on light-induced thermoelastic spectroscopy (LITES) using a 2 μm diode laser and a self-designed low-frequency trapezoidal-head QTF is reported for the first time in this invited paper. The self-designed trapezoidal-head QTF with a low resonant frequency of 9464.18 Hz and a high quality factor (Q) of 12,133.56 can significantly increase the accumulation time and signal level of the CO2-LITES sensor. A continuous-wave (CW) distributed-feedback (DFB) diode laser is used as the light source, and the strongest absorption line of CO2 located at 2004.01 nm is chosen. A comparison between the standard commercial QTF with the resonant frequency of 32.768 kHz and the self-designed trapezoidal-head QTF is performed. The experimental results show that the CO2-LITES sensor with the self-designed trapezoidal-head QTF has an excellent linear response to CO2 concentration, and its minimum detection limit (MDL) can reach 46.08 ppm (parts per million). When the average time is increased to 100 s based on the Allan variance analysis, the MDL of the sensor can be improved to 3.59 ppm. Compared with the 16.85 ppm of the CO2-LITES sensor with the commercial QTF, the performance is improved by 4.7 times, demonstrating the superiority of the self-designed trapezoidal-head QTF. Full article
(This article belongs to the Special Issue Sensors in 2025)
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Review

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30 pages, 7595 KiB  
Review
Principle and Applications of Thermoelectric Generators: A Review
by Mohamad Ridwan, Manel Gasulla and Ferran Reverter
Sensors 2025, 25(8), 2484; https://doi.org/10.3390/s25082484 - 15 Apr 2025
Viewed by 273
Abstract
For an extensive and sustainable deployment of technological ecosystems such as the Internet of Things, it is a must to leverage the free energy available in the environment to power the autonomous sensors. Among the different alternatives, thermal energy harvesters based on thermoelectric [...] Read more.
For an extensive and sustainable deployment of technological ecosystems such as the Internet of Things, it is a must to leverage the free energy available in the environment to power the autonomous sensors. Among the different alternatives, thermal energy harvesters based on thermoelectric generators (TEGs) are an attractive solution for those scenarios in which a gradient of temperature is present. In such a context, this article reviews the operating principle of TEGs and then the applications proposed in the literature in the last years. These applications are subclassified into five categories: domestic, industrial, natural heat, wearable, and others. In each category, a comprehensive comparison is carried out, including the thermal, mechanical, and electrical information of each case. Finally, an identification of the challenges and opportunities of research in the field of TEGs applied to low-power sensor nodes is exposed. Full article
(This article belongs to the Special Issue Sensors in 2025)
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19 pages, 8454 KiB  
Review
A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives
by Xuan Li, Bingxue Zhu, Sijia Li, Lushi Liu, Kaishan Song and Jiping Liu
Sensors 2025, 25(8), 2345; https://doi.org/10.3390/s25082345 - 8 Apr 2025
Viewed by 247
Abstract
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, [...] Read more.
Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review discusses remote sensing technologies currently used for estimating vegetation chlorophyll content, focusing on four key aspects: the acquisition of reference datasets, the identification of optimal spectral variables, the selection of estimation models, and the analysis of application scenarios. The results indicate that spectral bands in the visible and red-edge regions (e.g., 530 nm, 670 nm, and 705 nm) provide high prediction accuracy. Machine learning methods, such as random forest and support vector regression, exhibit excellent performance, with determination coefficients (R2) typically exceeding 0.9, although overfitting remains an issue. Although radiative transfer models are slightly less accurate (R2 = 0.6–0.8), they provide greater interpretability. Hybrid models integrating machine learning and radiative transfer show strong potential to balance accuracy and generalizability. Future research should improve model generalizability for different vegetation types and environmental conditions and integrate multi-source remote sensing data to improve spatial and temporal resolution. Combining physical models with data processing methods, such as artificial intelligence, can improve scalability, cost-effectiveness, and real-time monitoring capabilities. Full article
(This article belongs to the Special Issue Sensors in 2025)
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