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Women’s Special Issue Series: Sensors

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

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 20905

Special Issue Editors

Dr. Nicoleta Cristina Gaitan

E-Mail Website
Guest Editor
Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, 720229 Suceava, Romania
Interests: Industrial Internet of Things; smart cities; data acquisition; distributed systems; embedded systems; FPGA systems; software architecture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Monica Wachowicz

E-Mail Website
Guest Editor
School of Science, RMIT University, VIC 3000, Australia
Interests: streaming analytics; mobility behaviour analytics; edge computing; EdgeAI; machine learning on graphs; TinyML; Internet of Things; visual analytics; geospatial intelligence
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Imene Yahyaoui

E-Mail Website
Guest Editor
Applied Mathematics, Materials Science and Engineering and Electronic Technology, King Juan Carlos University, Madrid, Spain
Interests: renewable energies; PV; PVTs; fuel cells; wind turbines; power control; PV panels efficiency; PV end use; electrical machines and power electronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

To celebrate and highlight the achievements of women in the research area of sensors, this Special Issue, entitled "Women’s Special Issue Series: Sensors", will present sensor-related work from leading female scientists. We also hope that this Special Issue will further encourage and promote the scientific contributions of female researchers in this field.

The topic of this Special Issue include, but are not limited to, the following:

  • 3D sensing.
  • Wearable sensors, devices, and electronics.
  • Lab-on-a-chip.
  • Sensor devices, technology, and applications.
  • Advanced materials for sensing.
  • Photonic sensors.
  • Nanophotonics.
  • Internet of things.
  • Distributed systems.
  • Real-time systems.
  • Industrial Internet of things.
  • Applications and use cases, such as Industry 4.0, smart cities, digital health, and smart and digital agriculture.
  • Intelligent IIoT management and networking services.
  • Sensor networks and smart computing.

Dr. Nicoleta Cristina Gaitan
Prof. Dr. Monica Wachowicz
Prof. Dr. Imene Yahyaoui
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 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.

Women’s Special Issue Series

This Special Issue is part of Sensors's Women’s Special Issue Series, hosted by women editors for women researchers. The Series advocates the advancement of women in science. We invite contributions to the Special Issue whose lead authors identify as women. The submission of articles with all-women authorship is especially encouraged. However, we do welcome articles from all authors, irrespective of gender.

Keywords

  • sensors and sensing
  • lab-on-a-chip
  • sensor devices

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 (4 papers)

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Research

27 pages, 14010 KB  
Article
A Novel Unsupervised Structural Damage Detection Method Based on TCN-GAT Autoencoder
by Yanchun Ni, Qiyuan Jin and Rui Hu
Sensors 2025, 25(21), 6724; https://doi.org/10.3390/s25216724 - 3 Nov 2025
Cited by 2 | Viewed by 1327
Abstract
Over the service life of several decades, structural damage detection is crucial for ensuring the safety and durability of engineering structures. However, existing methods often overlook the spatiotemporal coupling in multi-sensor data, hindering the full exploitation of structural dynamic evolution and spatial correlations. [...] Read more.
Over the service life of several decades, structural damage detection is crucial for ensuring the safety and durability of engineering structures. However, existing methods often overlook the spatiotemporal coupling in multi-sensor data, hindering the full exploitation of structural dynamic evolution and spatial correlations. This paper proposes an autoencoder model integrating Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT), termed TCNGAT-AE, to establish an unsupervised damage detection method. The model utilizes the TCN module to extract temporal dependencies and dynamic features from vibration signals, while leveraging the GAT module to explicitly capture the spatial topological relationships within the sensor network, thereby achieving deep fusion of spatiotemporal features. The proposed method adopts an “offline training-online detection” framework, requiring only data from the healthy state of the structure for training, and employs reconstruction error as the damage indicator. To validate the proposed method, two sets of experimentally measured data are utilized: one from the Z-24 concrete box-girder bridge under ambient excitation, and the other from the Old Ada Bridge under vehicle load excitation. Additionally, ablation studies are conducted to analyze the effectiveness of the spatiotemporal fusion mechanism. Results demonstrate that the proposed method achieves effective damage detection in both different structural types and excitation scenarios. Furthermore, the explicit modeling of spatiotemporal features significantly enhances detection performance, with the anomaly detection rate showing substantial improvement compared to baseline models utilizing only temporal or spatial modeling. Moreover, this end-to-end framework processes raw vibration signals directly, avoiding complex preprocessing. This makes it highly suitable for practical and near-real-time monitoring. The findings of this study demonstrate that the damage detection method based on TCNGAT-AE can be effectively applied to structural safety monitoring in complex engineering environments, and can be further integrated with real-time monitoring systems of critical structures for online analysis. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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20 pages, 10342 KB  
Article
Integrating Artificial Intelligence into an Automated Irrigation System
by Nicoleta Cristina Gaitan, Bianca Ioana Batinas, Calin Ursu and Filaret Niculai Crainiciuc
Sensors 2025, 25(4), 1199; https://doi.org/10.3390/s25041199 - 16 Feb 2025
Cited by 20 | Viewed by 12405
Abstract
Climate change in Eastern Europe requires introducing automated irrigation systems and monitoring agricultural and climatic parameters to ensure food security. The automation of irrigation, together with the generation of climate reports based on AI (artificial intelligence) using OpenAI models for Internet of Things [...] Read more.
Climate change in Eastern Europe requires introducing automated irrigation systems and monitoring agricultural and climatic parameters to ensure food security. The automation of irrigation, together with the generation of climate reports based on AI (artificial intelligence) using OpenAI models for Internet of Things (IoT) data processing, contributes to the optimization of resources by reducing excessive water and energy consumption, supporting plant health through proper irrigation and increasing sustainable agricultural productivity by providing suggestions and statistics to streamline the agricultural process. In this paper, the authors present a system that allows continuous data collection of parameters such as temperature, humidity, and soil moisture, providing detailed information and advanced analytics for each device and area monitored using AI to generate predictive recommendations. The data transmission is performed wirelessly via WebSocket to the central database. This system uses data from all devices connected to the application to assess current climate conditions at a national level, identifying trends and generating reports that aid in adapting to extreme events. The integration of artificial intelligence in the context of monitoring and irrigation of agricultural areas is a step forward in the development of sustainable agriculture and for the adaptation of agriculture to increasingly aggressive climate phenomena, providing a replicable framework for vulnerable regions. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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18 pages, 13770 KB  
Article
Optimizing Correction Factors on Color Differences for Automotive Painting Services
by Emilia Corina Corbu, Anne-Marie Nitescu and Eduard Edelhauser
Sensors 2024, 24(24), 8213; https://doi.org/10.3390/s24248213 - 23 Dec 2024
Cited by 2 | Viewed by 2799
Abstract
Currently, the automotive sector is showing increased demands regarding the color of cars in general, but especially the quality and the time of painting, in particular. Companies working in this industry, especially in specialized painting services, must perform work of impeccable quality in [...] Read more.
Currently, the automotive sector is showing increased demands regarding the color of cars in general, but especially the quality and the time of painting, in particular. Companies working in this industry, especially in specialized painting services, must perform work of impeccable quality in the shortest possible time in order to be efficient. Color differences that appear in different areas of the car result from the use of different formulas for obtaining color. These differences can be reduced by using correction factors that are established for the colors in the partial or total painting process of cars. There are several factors that lead to settings that are not verified by the real color and, therefore, contribute to incorrect color results and also to high and unnecessary repair costs. In this study, the authors aimed to optimize the values of the correction factors applicable in the automotive industry, based on a set of 135 measurements performed with a BYK Gardner spectrophotometer, in order to minimize color differences. Through this study, authors have also aimed to find out how the color-identification process can be streamlined with the smallest possible tolerances by optimally adjusting the correction factors and by identifying the factors that influence the color-reading and identification process. A total of 85 pairs of samples were used for the DS1 (data set) and 53 pairs of samples for the DS2 (data set); these samples were used in the visual experiments for testing the performance of two color-differentiation formulas. The first part of the research aimed to investigate the visual perception of the painted cars in terms of differences in brightness, chroma and hue, data that were used to optimize the formulas used for color differences. Finally, authors have estimated the closest color variant to the objective color by optimizing the correction factors and thus achieving the efficiency of the color-identification process and the whole painting-identification process. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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35 pages, 13487 KB  
Article
Sensory Navigation System for Indoor Localization and Orientation of Users with Cognitive Disabilities in Daily Tasks and Emergency Situations
by María Teresa García-Catalá, Estefanía Martín-Barroso, María Cristina Rodríguez-Sánchez, Marcos Delgado-Álvaro and Robert Novak
Sensors 2024, 24(22), 7154; https://doi.org/10.3390/s24227154 - 7 Nov 2024
Cited by 3 | Viewed by 3093
Abstract
This article presents SmartRoutes, (version 1) a sensory navigation system designed for the localization and guidance of individuals with cognitive disabilities in both indoor and outdoor environments. The platform facilitates route generation in both contexts and provides detailed instructions, enabling effective task execution [...] Read more.
This article presents SmartRoutes, (version 1) a sensory navigation system designed for the localization and guidance of individuals with cognitive disabilities in both indoor and outdoor environments. The platform facilitates route generation in both contexts and provides detailed instructions, enabling effective task execution and seamless integration into daily activities or high-stress situations, such as emergency evacuations. SmartRoutes aims to enhance users’ independence and quality of life by offering comprehensive support for navigation across various settings. The platform is specifically designed to manage routes in both indoor and outdoor environments, targeting individuals with cognitive disabilities that affect orientation and the ability to follow instructions. This solution seeks to improve route learning and navigation, facilitating the completion of routine tasks in work and social contexts. Additionally, in exceptional situations such as emergencies, SmartRoutes ensures that users do not become disoriented or blocked. The application effectively guides users to the most appropriate exit or evacuation point. This combination of route generation and detailed instructions underscores the platform’s commitment to inclusion and accessibility, ultimately contributing to the well-being and autonomy of individuals with cognitive disabilities. Full article
(This article belongs to the Special Issue Women’s Special Issue Series: Sensors)
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