Sensors

15 pages, 1190 KiB

Open AccessArticle

A Highly Portable Smartphone-Based Capillary Electrophoresis with Capacitively Coupled Contactless Conductivity Detection

by Zhimin Tao, Qiang Zhang, Yiren Cao, Xunjie Duan, Yuyang Wu, Liuyin Fan, Chengxi Cao and Weiwen Liu

Sensors 2025, 25(7), 2303; https://doi.org/10.3390/s25072303 (registering DOI) - 4 Apr 2025

Abstract

Work has rarely been reported on a highly portable smartphone-based capillary electrophoresis (CE) with capacitively coupled contactless conductivity detection (C⁴D). Herein, a highly portable phone-based CE (130 mm × 190 × 70 mm, 1.4 kg) with C⁴D and Bluetooth [...] Read more.

Work has rarely been reported on a highly portable smartphone-based capillary electrophoresis (CE) with capacitively coupled contactless conductivity detection (C⁴D). Herein, a highly portable phone-based CE (130 mm × 190 × 70 mm, 1.4 kg) with C⁴D and Bluetooth communication, as well as user-interface software, was developed for portable analysis. To demonstrate the device, six metal ions were selected as model analytes for verification and successfully applied to the detection of ions in tap water. The analytical performance highlighted that the runs and data analysis of the CE-C⁴D device could be controlled via the user interface based on smartphones. Furthermore, the experiments showed that (i) the linear ranges of six metal ions were between 6 and 1500 μmol/L with a correlation coefficient of more than 0.9934; (ii) the limit of detection (LOD) values were within 1.84–4.33 μmol/L; (iii) the intra-day deviations of migration time and peak area were 2.40–5.24% and 0.75–2.82% (n = 5), respectively. Although the LOD is not the most optimal among current portable devices, the results still indicated the satisfactory analytical performance and potential of the developed device, software, and method for portable separation and quantitation of analytes from various fields. Full article

(This article belongs to the Special Issue Sensors from Miniaturization of Analytical Instruments (2nd Edition))

30 pages, 11088 KiB

Open AccessArticle

Space Surveillance with High-Frequency Radar

by Brendan Hennessy, Heath Yardley, Rob Debnam, Tristan A. Camilleri, Nicholas K. Spencer, David A. Holdsworth, Goeff Warne, Brian Cheung and Sergey Kharabash

Sensors 2025, 25(7), 2302; https://doi.org/10.3390/s25072302 (registering DOI) - 4 Apr 2025

Abstract

High-Frequency (HF) radar is well suited to the surveillance of low-earth-orbit space. For large targets, a small deployable HF radar is able to match the detection performance of much larger space surveillance radar systems operating at higher frequencies. However, there are some unique [...] Read more.

High-Frequency (HF) radar is well suited to the surveillance of low-earth-orbit space. For large targets, a small deployable HF radar is able to match the detection performance of much larger space surveillance radar systems operating at higher frequencies. However, there are some unique challenges associated with the use of HF, including the range–Doppler coupling bias, coarse detection-level localisation, and the presence of meteor returns and other unwanted signals. This paper details the use of HF radar for space surveillance, including signal processing and radar product formation, tracking, ionospheric correction, and orbit determination. It is shown that by fusing measurements from multiple passes, accurate orbital estimates can be obtained. Included are results from recent SpaceFest trials of the Defence Science and Technology Group’s HF space surveillance radar, achieving real-time wide-area surveillance in tracking, orbit determination, and cueing of other space surveillance sensors. Full article

(This article belongs to the Special Issue Sensors for Space Situational Awareness and Object Tracking)

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15 pages, 4638 KiB

Open AccessArticle

Invisible CMOS Camera Dazzling for Conducting Adversarial Attacks on Deep Neural Networks

by Zvi Stein, Adir Hazan and Adrian Stern

Sensors 2025, 25(7), 2301; https://doi.org/10.3390/s25072301 (registering DOI) - 4 Apr 2025

Abstract

Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling [...] Read more.

Despite the outstanding performance of deep neural networks, they remain vulnerable to adversarial attacks. While digital domain adversarial attacks are well-documented, most physical-world attacks are typically visible to the human eye. Here, we present a novel invisible optical-based physical adversarial attack via dazzling a CMOS camera. This attack involves using a designed light pulse sequence spatially transformed within the acquired image due to the camera’s shutter mechanism. We provide a detailed analysis of the photopic conditions required to keep the attacking light source invisible to human observers while effectively disrupting the image, thereby deceiving the DNN. The results indicate that the light source duty cycle controls the tradeoff between the attack’s success rate and the degree of concealment needed. Full article

(This article belongs to the Special Issue Deep Learning for Perception and Recognition: Method and Applications)

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25 pages, 27458 KiB

Open AccessArticle

A Comparative Study and Optimization of Camera-Based BEV Segmentation for Real-Time Autonomous Driving

by Woomin Jun and Sungjin Lee

Sensors 2025, 25(7), 2300; https://doi.org/10.3390/s25072300 (registering DOI) - 4 Apr 2025

Abstract

This study addresses the optimization of a camera-based bird’s eye view (BEV) segmentation technique that operates in real-time within an embedded system environment while maintaining high accuracy despite limited computational resources. Specifically, it examines three technical approaches for BEV segmentation in autonomous driving: [...] Read more.

This study addresses the optimization of a camera-based bird’s eye view (BEV) segmentation technique that operates in real-time within an embedded system environment while maintaining high accuracy despite limited computational resources. Specifically, it examines three technical approaches for BEV segmentation in autonomous driving: depth-based methods, MLP-based methods, and transformer-based methods, focusing on key techniques such as lift–splat–shoot, HDMapNet, and BEVFormer. A mathematical analysis of these methods is conducted, followed by a comparative performance evaluation using the nuScenes dataset. The optimization process was carried out in three stages: accuracy improvement, latency reduction, and model size optimization. In the first stage of the process, the three modules for BEV segmentation (encoder, view transformation, and decoder) were selected with the goal of maximizing mIoU performance. In the second stage, environmental variable optimization was performed through input resolution adaptation and data augmentation to improve accuracy. Finally, in the third stage, model compression was applied to minimize model size and latency for efficient deployment on embedded systems. Experimental results from the first stage show that the lift–splat–shoot view transformation model, based on the InternImage-B encoder and EfficientNet-B0 decoder, achieved the highest performance with 54.9 mIoU at an input image size of

448 \times 800

. Notably, the lift–splat–shoot view transformation model with the InternImage-T encoder and EfficientNet-B0 decoder demonstrated performance of 53.1 mIoU while achieving high efficiency (51.7 ms and 159.5 MB, respectively). The application of the second stage revealed that increasing the input resolution does not always lead to improved accuracy, and there is an optimal resolution size depending on the model. In this study, the best performance was achieved with an input image size of

448 \times 800

. During the third stage, FP16 quantization enabled a 50% reduction in memory size and decreased latency while maintaining similar or identical mIoU performance. When deployed on the NVIDIA AGX Orin device, which operates under power constraints, energy efficiency improved, although it resulted in higher latency under certain power supply conditions. As a result, the InternImage encoder-based lift–splat–shoot technique was shown to achieve the highest accuracy performance relative to latency and model size. This approach outperformed the original method by achieving a 29.2% higher mIoU while maintaining similar latency performance and reducing memory size by 32.2%. Full article

(This article belongs to the Special Issue AI-Driving for Autonomous Vehicles)

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18 pages, 1379 KiB

Open AccessArticle

An Algorithm for Mining the Living Habits of Elderly People Living Alone Based on AIoT

by Jiaxuan Wu, Yuxin Lu and Yueqiu Jiang

Sensors 2025, 25(7), 2299; https://doi.org/10.3390/s25072299 (registering DOI) - 4 Apr 2025

Abstract

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates [...] Read more.

With the global aging population on the rise, the health and safety of elderly individuals living alone have become increasingly critical. This study introduces a novel AIoT-based habit mining algorithm designed to enhance activity monitoring in smart home environments. The proposed method integrates a one-dimensional U-Net neural network for accurate behavioral classification and an FP-Growth-based temporal association rule analysis for uncovering meaningful living patterns. By leveraging environmental sensor data, the algorithm first classifies daily activities and then uses timestamps to detect time-sensitive dependencies in behavior sequences, identifying the long-term habits of the elderly. Experimental validation on CASAS datasets (ARUBA and MILAN) demonstrates superior performance, achieving a precision of 84.77%. Compared to traditional techniques, this approach excels in behavior recognition and habit mining, offering a precise and adaptive framework for AIoT-driven smart home safety and health monitoring systems. The results highlight its potential to improve the quality of life and safety for elderly individuals living alone. Full article

(This article belongs to the Special Issue Integrating Smart Sensors and Artificial Intelligence Technologies for Human Healthcare Applications)

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22 pages, 10948 KiB

Open AccessArticle

Method of Forearm Muscles 3D Modeling Using Robotic Ultrasound Scanning

by Vladislava Kapravchuk, Albert Ishkildin, Andrey Briko, Anna Borde, Maria Kodenko, Anastasia Nasibullina and Sergey Shchukin

Sensors 2025, 25(7), 2298; https://doi.org/10.3390/s25072298 (registering DOI) - 4 Apr 2025

Abstract

The accurate assessment of muscle morphology and function is crucial for medical diagnostics, rehabilitation, and biomechanical research. This study presents a novel methodology for constructing volumetric models of forearm muscles based on three-dimensional ultrasound imaging integrated with a robotic system to ensure precise [...] Read more.

The accurate assessment of muscle morphology and function is crucial for medical diagnostics, rehabilitation, and biomechanical research. This study presents a novel methodology for constructing volumetric models of forearm muscles based on three-dimensional ultrasound imaging integrated with a robotic system to ensure precise probe positioning and controlled pressure application. The proposed ultrasound scanning approach combined with a collaborative six-degrees-of-freedom robotic manipulator enabled reproducible and high-resolution imaging of muscle structures in both relaxed and contracted states. A custom-built phantom, acoustically similar to biological tissues, was developed to validate the method. The cross-sectional area of the muscles and the coordinates of the center of mass of the sections, as well as the volume and center of gravity of each muscle, were calculated for each cross-section of the reconstructed forearm muscle models at contraction. The method’s feasibility was confirmed by comparing the reconstructed volumes with anatomical data and phantom measurements. This study highlights the advantages of robotic-assisted ultrasound imaging for non-invasive muscle assessment and suggests its potential applications in neuromuscular diagnostics, prosthetics design, and rehabilitation monitoring. Full article

(This article belongs to the Special Issue 3D Sensing and Imaging for Biomedical Investigations: Second Edition)

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26 pages, 2436 KiB

Open AccessArticle

A Score-Based Game Approach Considering Resource Heterogeneity and Social Dynamics for Traffic Optimization in Social IoT Networks

by Muhammad Muneer Umar, Ali F. Almutairi and Shafiullah Khan

Sensors 2025, 25(7), 2297; https://doi.org/10.3390/s25072297 (registering DOI) - 4 Apr 2025

Abstract

The incorporation of human-like social concepts into the Internet of Things (IoT) has given rise to the paradigm of Social IoT (SIoT). In these networks, objects autonomously form social relationships to enhance network scalability in information and service discovery, focusing on their own [...] Read more.

The incorporation of human-like social concepts into the Internet of Things (IoT) has given rise to the paradigm of Social IoT (SIoT). In these networks, objects autonomously form social relationships to enhance network scalability in information and service discovery, focusing on their own benefits. However, social likeness or dislikeness among nodes can result in selfish behavior, adversely affecting network performance. Existing node stimulation mechanisms primarily focus on ad hoc and IoT networks, emphasizing topological structures and traffic patterns, while overlooking the social and behavioral factors crucial to the SIoT. This work proposes a novel node stimulation scheme for the SIoT that incorporates both social and behavioral characteristics and network topology. The mechanism employs a virtual currency-based game to incentivize cooperation by considering parameters such as proximity, energy levels, buffer size, correlated relays, and data quality. Additionally, social factors—including social preference, node importance, interaction history, and the probability of vital data transfer—are integrated into the decision-making process. Simulation results demonstrate that the proposed mechanism outperforms existing approaches in terms of energy efficiency, throughput, packet delivery ratio, and end-to-end delay, making it a robust solution for improving cooperation and performance in SIoT networks. Full article

(This article belongs to the Section Sensor Networks)

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21 pages, 5289 KiB

Open AccessArticle

Research on the Transformer Failure Diagnosis Method Based on Fluorescence Spectroscopy Analysis and SBOA Optimized BPNN

by Xueqing Chen, Dacheng Li and Anjing Wang

Sensors 2025, 25(7), 2296; https://doi.org/10.3390/s25072296 - 4 Apr 2025

Abstract

The representative dissolved gases analysis (DGA) method for transformer fault detection faces many shortcomings in early fault diagnosis, which restricts the application and development of fault detection technology in the field of transformers. In order to diagnose early failure in time, fluorescence analysis [...] Read more.

The representative dissolved gases analysis (DGA) method for transformer fault detection faces many shortcomings in early fault diagnosis, which restricts the application and development of fault detection technology in the field of transformers. In order to diagnose early failure in time, fluorescence analysis technology has recently been used for the research of transformer failure diagnosis, which makes up for the shortcomings of DGA. However, most of the existing fluorescence analyses of insulating oil studies combined with intelligent algorithms are a qualitative diagnosis of fault types; the quantitative fault diagnosis of the same oil sample has not been reported. In this study, a typical fault simulation experiment of the interval discharge of insulating oil was carried out with the new Xinjiang Karamay oil, and the fluorescence spectroscopy data of insulating oil under different discharge durations were collected. In order to eliminate the influence of noise factors on the spectral analysis and boost the accuracy of the diagnosis, a variety of spectral preprocessing algorithms, such as Savitzky–Golay (SG), moving median, moving mean, gaussian, locally weighted linear regression smoothing (Lowess), locally weighted quadratic regression smoothing (Loess), and robust (RLowess) and (Rloess), are used to smooth denoise the collected spectral data. Then, the dimensionality reduction techniques of principal component analysis (PCA), kernel principal component analysis (KPCA), and multi-dimensional scale (MDS) are used for further processing. Based on various preprocessed and dimensionally reduced data, transformer failure diagnosis models based on the particle swarm optimization algorithm (PSO) and the secretary bird optimization algorithm (SBOA) optimized BPNN are established to quantitatively analyze the state of insulating oil and predict the durations of transformer failure. By using the mathematical evaluation methods to comprehensively evaluate and compare the effects of various algorithm models, it was found that the Loess-MDS-SBOA-BP model has the best performance, with its determination coefficient (R²) increasing to 99.711%, the root mean square error (RMSE) being only 0.27144, and the other evaluation indicators also being optimal. The experimental results show that the failure diagnosis model finally proposed in this paper can perform an accurate diagnosis of the failure time; the predicted time is closest to the true value, which lays a foundation for the further development of the field of transformer failure diagnosis. Full article

(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)

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18 pages, 9015 KiB

Open AccessArticle

An End-to-End General Language Model (GLM)-4-Based Milling Cutter Fault Diagnosis Framework for Intelligent Manufacturing

by Jigang He, Xuan Liu, Yuncong Lei, Ao Cao and Jie Xiong

Sensors 2025, 25(7), 2295; https://doi.org/10.3390/s25072295 - 4 Apr 2025

Abstract

CNC machine and cutting tools are an indispensable part of the cutting process. Their life default diagnosis is related to the efficiency of the entire production process, which ultimately impacts economic performance. Many methods provided by deep learning articles have been verified for [...] Read more.

CNC machine and cutting tools are an indispensable part of the cutting process. Their life default diagnosis is related to the efficiency of the entire production process, which ultimately impacts economic performance. Many methods provided by deep learning articles have been verified for use on large cutting datasets and can help in diagnosing tools’ lifetime well; however, on small samples, the challenge of learning difficulties still emerges. The rise in large language models (LLMs) has brought changes to tool life diagnosis. This study proposes a fault diagnosis algorithm based on GLM-4, and the experimental validation on the PHM 2010 dataset and a proprietary milling cutter dataset demonstrates the superiority of the proposed model, achieving diagnostic accuracies of 93.8% and 93.3%, respectively, outperforming traditional models (SVM, CNN, RNN) and baseline LLMs (ChatGLM2-6B variants). Further robustness and noise-resistance analyses confirm its stability under varying SNR levels (10 dB to −10 dB) and limited training samples. This work highlights the potential of integrating domain-specific feature engineering with LLMs to advance intelligent manufacturing diagnostics. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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24 pages, 2317 KiB

Open AccessArticle

Transparent and Privacy-Preserving Mobile Crowd-Sensing System with Truth Discovery

by Ruijuan Jia, Juan Ma, Ziyin You and Mingyue Zhang

Sensors 2025, 25(7), 2294; https://doi.org/10.3390/s25072294 - 4 Apr 2025

Abstract

The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth [...] Read more.

The proliferation of numerous portable mobile devices has made mobile crowd-sensing (MCS) systems a promising new trend. Traditional MCS systems typically outsource sensing tasks to the data aggregator (e.g., cloud server). They collect and analyze the provided sensing data through an appropriate truth discovery (TD) method to identify valuable data sets. However, existing privacy-preserving MCS systems lack transparency, enabling data aggregators to deviate from the specified protocols and allowing malicious users to provide false or invalid sensing data, thereby contaminating the resulting data sets. The lack of transparency and public verifiability in MCS systems undermines widespread adoption by preventing data requesters from confidently verifying data integrity and accuracy. To address this issue, we propose a transparent and privacy-preserving mobile crowd-sensing system with truth discovery (TP-MCS) constructed using zero-knowledge proof (ZKP) and the Merkle commitment tree. This scheme enables data requesters to effectively verify the correctness of the truth discovery service while ensuring data privacy. Furthermore, theoretical analysis and extensive experiments demonstrate that this scheme is secure and efficient. Full article

(This article belongs to the Special Issue Advanced Mobile Edge Computing in 5G Networks)

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22 pages, 3666 KiB

Open AccessArticle

Resource-Constrained Specific Emitter Identification Based on Efficient Design and Network Compression

by Mengtao Wang, Shengliang Fang, Youchen Fan and Shunhu Hou

Sensors 2025, 25(7), 2293; https://doi.org/10.3390/s25072293 - 4 Apr 2025

Abstract

Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) [...] Read more.

Specific emitter identification (SEI) methods based on deep learning (DL) have effectively addressed complex, multi-dimensional signal recognition tasks by leveraging deep neural networks. However, this advancement introduces challenges such as model parameter redundancy and high feature dimensionality, which pose limitations for resource-constrained (RC) edge devices, especially in Internet of Things (IoT) applications. To tackle these problems, we propose an RC-SEI method based on efficient design and model compression. Specifically, for efficient design, we have developed a lightweight convolution network (LCNet) that aims to balance performance and complexity. Regarding model compression, we introduce sparse regularization techniques in the fully connected (FC) layer, achieving over 99% feature dimensionality reduction. Furthermore, we have comprehensively evaluated the proposed method on public automatic-dependent surveillance-broadcast (ADS-B) and Wi-Fi datasets. Simulation results demonstrate that our proposed method exhibits superior performance in terms of both recognition accuracy and model complexity. Specifically, LCNet achieved accuracies of 99.40% and 99.90% on the ADS-B and Wi-Fi datasets, respectively, with only 33,510 and 33,544 parameters. These results highlight the feasibility and potential of our proposed RC-SEI method for RC scenarios. Full article

(This article belongs to the Special Issue Advances in Remote Sensing and Electromagnetic Spectrum Sensing: Data Acquisition and Signal Processing)

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50 pages, 7835 KiB

Open AccessArticle

Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System

by Marilena Ianculescu, Victor-Ștefan Constantin, Andreea-Maria Gușatu, Mihail-Cristian Petrache, Alina-Georgiana Mihăescu, Ovidiu Bica and Adriana Alexandru

Sensors 2025, 25(7), 2292; https://doi.org/10.3390/s25072292 - 4 Apr 2025

Abstract

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, [...] Read more.

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article

(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)

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25 pages, 2217 KiB

Open AccessSystematic Review

IoT Sensing for Advanced Irrigation Management: A Systematic Review of Trends, Challenges, and Future Prospects

by Ahmed A. Abdelmoneim, Hilda N. Kimaita, Christa M. Al Kalaany, Bilal Derardja, Giovanna Dragonetti and Roula Khadra

Sensors 2025, 25(7), 2291; https://doi.org/10.3390/s25072291 - 4 Apr 2025

Abstract

Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management [...] Read more.

Efficient water management is crucial for sustainable agriculture, and the integration of Internet of Things (IoT) technologies in irrigation systems offers innovative solutions to optimize resource use. In this systematic review, the current landscape of Internet of Things (IoT) applications in irrigation management was investigated. The study aimed to identify key research trends and technological developments in the field. Using VOSviewer (CWTS, Leiden, The Netherlands) for bibliometric mapping, the influential research clusters were identified. The analysis revealed a significant rise in scholarly interest, with peak activity between 2020 and 2022, and a shift towards interdisciplinary and applied research. Additionally, the content analysis revealed prevalent agricultural applications, frequently employed microcontroller units (MCUs), widely used sensors, and trends in communication technologies such as the increasing adoption of low-power, scalable communication protocols for real-time data acquisition. This study not only offers a comprehensive overview of the current status of IoT integration in smart irrigation but also highlights the technological advancements. Future research directions include integrating IoT with emerging technologies such as artificial intelligence, edge computing, and blockchain to enhance decision-support systems and predictive irrigation strategies. By examining the transformative potential of IoT, this study provides valuable insights for researchers and practitioners seeking to enhance agricultural productivity, optimize resource use, and improve sustainability. Full article

(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)

29 pages, 3929 KiB

Open AccessArticle

Beyond Signatures: Leveraging Sensor Fusion for Contextual Handwriting Recognition

by Alen Salkanovic, Diego Sušanj, Luka Batistić and Sandi Ljubic

Sensors 2025, 25(7), 2290; https://doi.org/10.3390/s25072290 - 4 Apr 2025

Abstract

This paper deals with biometric identification based on unique patterns and characteristics of an individual’s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only [...] Read more.

This paper deals with biometric identification based on unique patterns and characteristics of an individual’s handwriting, focusing on the dynamic writing process on a touchscreen device. Related work in this domain indicates the dominance of specific research approaches. Namely, in most cases, only the signature is analyzed, verification methods are more prevalent than recognition methods, and the provided solutions are mainly based on using a particular device or specific sensor for collecting biometric data. In this context, our work aims to fill the identified research gap by introducing a new handwriting-based user recognition technique. The proposed approach implements the concept of sensor fusion and does not rely exclusively on signatures for recognition but also includes other forms of handwriting, such as short sentences, words, or individual letters. Additionally, two different ways of handwriting input, using a stylus and a finger, are introduced into the analysis. In order to collect data on the dynamics of handwriting and signing, a specially designed apparatus was used with various sensors integrated into common smart devices, along with additional external sensors and accessories. A total of 60 participants took part in a controlled experiment to form a handwriting biometrics dataset for further analysis. To classify participants’ handwriting, custom architecture CNN models were utilized for feature extraction and classification tasks. The obtained results showed that the proposed handwriting recognition system achieves accuracies of 0.982, 0.927, 0.884, and 0.661 for signatures, words, short sentences, and individual letters, respectively. We further investigated the main effects of the input modality and the train set’s size on the system’s accuracy. Finally, an ablation study was carried out to analyze the impact of individual sensors within the fusion-based setup. Full article

(This article belongs to the Special Issue Biometrics-Based Authentication: Advancements and Real-World Implementations)

25 pages, 1764 KiB

Open AccessReview

Fiber Bragg Grating Sensors: Design, Applications, and Comparison with Other Sensing Technologies

by Alaa N. D. Alhussein, Mohammed R. T. M. Qaid, Timur Agliullin, Bulat Valeev, Oleg Morozov and Airat Sakhabutdinov

Sensors 2025, 25(7), 2289; https://doi.org/10.3390/s25072289 - 4 Apr 2025

Abstract

Fiber Bragg grating (FBG) sensors have emerged as advanced tools for monitoring a wide range of physical parameters in various fields, including structural health, aerospace, biochemical, and environmental applications. This review provides a comprehensive overview of FBG sensor technology, focusing on their operating [...] Read more.

Fiber Bragg grating (FBG) sensors have emerged as advanced tools for monitoring a wide range of physical parameters in various fields, including structural health, aerospace, biochemical, and environmental applications. This review provides a comprehensive overview of FBG sensor technology, focusing on their operating principles, key advantages such as high sensitivity and immunity to electromagnetic interference, and common challenges like temperature-strain cross-sensitivity and the high cost of interrogation systems. Additionally, this review compares FBG sensors with other sensing technologies and highlights recent innovations in design, packaging, and implementation techniques. Finally, future research directions are discussed to enhance the performance, scalability, and long-term reliability of FBG-based sensing systems. Full article

(This article belongs to the Special Issue Feature Review Papers in Physical Sensors)

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26 pages, 3796 KiB

Open AccessArticle

An Explainable LSTM-Based Intrusion Detection System Optimized by Firefly Algorithm for IoT Networks

by Taiwo Blessing Ogunseyi and Gogulakrishan Thiyagarajan

Sensors 2025, 25(7), 2288; https://doi.org/10.3390/s25072288 - 4 Apr 2025

Abstract

As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed [...] Read more.

As more IoT devices become connected to the Internet, the attack surface for cybercrimes expands, leading to significant security concerns for these devices. Existing intrusion detection systems (IDSs) designed to address these concerns often suffer from high rates of false positives and missed threats due to the presence of redundant and irrelevant information for the IDSs. Furthermore, recent IDSs that utilize artificial intelligence are often presented as black boxes, offering no explanation of their internal operations. In this study, we develop a solution to the identified challenges by presenting a deep learning-based model that adapts to new attacks by selecting only the relevant information as inputs and providing transparent internal operations for easy understanding and adoption by cybersecurity personnel. Specifically, we employ a hybrid approach using statistical methods and a metaheuristic algorithm for feature selection to identify the most relevant features and limit the overall feature set while building an LSTM-based model for intrusion detection. To this end, we utilize two publicly available datasets, NF-BoT-IoT-v2 and IoTID20, for training and testing. The results demonstrate an accuracy of 98.42% and 89.54% for the NF-BoT-IoT-v2 and IoTID20 datasets, respectively. The performance of the proposed model is compared with that of other machine learning models and existing state-of-the-art models, demonstrating superior accuracy. To explain the proposed model’s predictions and increase trust in its outcomes, we applied two explainable artificial intelligence (XAI) tools: Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP), providing valuable insights into the model’s behavior. Full article

(This article belongs to the Special Issue Sensor Data Privacy and Intrusion Detection for IoT Networks)

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11 pages, 2992 KiB

Open AccessArticle

Fully Printed and Scalable Current and Voltage Sensors for Smart Grid Transmission Line Monitoring

by Yanyun Fan, Lei Zhang, Chi Zhang, Zhengang An, Bo Li and Dachao Li

Sensors 2025, 25(7), 2287; https://doi.org/10.3390/s25072287 - 4 Apr 2025

Abstract

In the construction and operation of smart grids, real-time monitoring of electrical signals is crucial for achieving efficient and stable power transmission, so it is necessary to develop current and voltage sensors with high stability, mass manufacturing and light weight. This study presents [...] Read more.

In the construction and operation of smart grids, real-time monitoring of electrical signals is crucial for achieving efficient and stable power transmission, so it is necessary to develop current and voltage sensors with high stability, mass manufacturing and light weight. This study presents a current and voltage sensor based on fully printed technology for electrical signal monitoring of transmission lines. The current sensor is supported and insulated by polyimide, and successfully fabricates the 3D induction coil through screen printing and high-precision inkjet printing processes, achieving a sensitivity of 0.00823 mV/A and a linearity of 0.999 in 0–60 A. The voltage sensor is made of polyimide film as the substrate, and a pair of silver sensing electrodes are prepared by screen printing process, achieving a sensitivity of 0.00369 μA/V and a linearity of 0.999 in 0–1200 V, with stable output over a continuous operation of 24 h. The overall size of the current and voltage sensor is 1.5 cm × 2 cm, the weight is 1.8 g, the cost is about USD 0.462, and it has the advantages of low cost, lightweight, good linearity, high stability, simple structure, and scalable preparation. This work provides a new sensor fabrication method for current and voltage monitoring in transmission lines. Full article

(This article belongs to the Section Physical Sensors)

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27 pages, 10003 KiB

Open AccessArticle

Fault Diagnosis Method of Rolling Bearing Based on 1D Multi-Channel Improved Convolutional Neural Network in Noisy Environment

by Huijuan Guo, Dongzhi Ping, Lijun Wang, Weijie Zhang, Junfeng Wu, Xiao Ma, Qiang Xu and Zhongyu Lu

Sensors 2025, 25(7), 2286; https://doi.org/10.3390/s25072286 - 4 Apr 2025

Abstract

The vibration signal of mechanical equipment in operating environments is the key to describing fault characteristics, but due to thez influence of equipment density and environmental interference, the accuracy of fault diagnosis is often affected by noise. In this paper, a fault diagnosis [...] Read more.

The vibration signal of mechanical equipment in operating environments is the key to describing fault characteristics, but due to thez influence of equipment density and environmental interference, the accuracy of fault diagnosis is often affected by noise. In this paper, a fault diagnosis method based on a 1D Multi-Channel Improved Convolutional Neural Network (1DMCICNN) is proposed. By introducing BiLSTM, an attention mechanism and a local sparse structure of a two-channel Convolutional Neural Network, the feature information of the noisy timing signal is fully extracted at different scales while reducing the computational parameters. The model is verified through experiments under different signal-to-noise ratios and loads. The results show that the accuracy of 1DMCICNN is 98.67%, 99.71%, 99.04%, and 99.71% on different load and speed datasets. Meanwhile, compared with the unoptimized two-channel Convolutional Neural Network, the training parameters are reduced by 55.58%. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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13 pages, 3705 KiB

Open AccessArticle

Investigating the Influence of Laser Polarization on Filamentation Thresholds in Transparent Media via Supercontinuum Coherence

by Yun Zhang, Yu Xia, Canneng Liang, Yuyao Xiong, Jingyuan Zhang, Shuang Lin, Suyu Li and Mingxing Jin

Sensors 2025, 25(7), 2285; https://doi.org/10.3390/s25072285 - 4 Apr 2025

Abstract

In this work, we experimentally investigate the characteristics of supercontinuum (SC) generation induced by femtosecond laser pulses with different polarization states in transparent medium. We employ a Mach–Zehnder Interferometer (MZI) to capture interference patterns during the filamentation process. The relative filamentation threshold, P [...] Read more.

In this work, we experimentally investigate the characteristics of supercontinuum (SC) generation induced by femtosecond laser pulses with different polarization states in transparent medium. We employ a Mach–Zehnder Interferometer (MZI) to capture interference patterns during the filamentation process. The relative filamentation threshold, P_th, is measured for femtosecond laser pulses with different polarization states. The results demonstrate that the intensity of SC is strongly correlated with the polarization state of the incident laser pulses. At the same pulse energy, circularly polarized (CP) pulses suppress SC generation compared to linearly polarized (LP) pulses. Compared with weak external focusing, short-focal-length focusing significantly broadens the spectral range of SC. As the focal length of the focusing lens increases, the measured P_th values also increase. The P_th of the CP pulses is consistently higher than that of LP pulses. The experimental measurements of P_th for femtosecond lasers with different polarization states provide basic data support for the research on nonlinear characteristics. Full article

(This article belongs to the Section Optical Sensors)

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