Sensors

23 pages, 1190 KiB

Open AccessArticle

Task Planning of Multiple Unmanned Aerial Vehicles Based on Minimum Cost and Maximum Flow

by Xiaodong Shi, Xiangping Zhai, Rui Wang, Yi Le, Shuang Fu and Ningzhong Liu

Sensors 2025, 25(5), 1605; https://doi.org/10.3390/s25051605 - 5 Mar 2025

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to [...] Read more.

With the rapid development of UAV technology, UAV delivery has gained attention for its potential to reduce labor costs. However, limitations in load capacity and energy restrict UAVs’ distribution capabilities. This paper proposes a cooperative delivery scheme combining traditional trucks and UAVs to extend UAV coverage and improve delivery completion rates. For densely distributed depots in wide-area regions, we develop algorithms for task allocation and path planning in a truck-independent UAV system. Specifically, a minimum-cost, maximum-flow model is constructed to obtain sub-paths covering all delivery tasks, and resource tree-based algorithms are used to construct global paths for UAVs and trucks. Simulation results show that our algorithms reduce total energy consumption by 11.53% and 9.15% under different task points, which suggests that our proposed method can significantly enhance delivery efficiency, offering a promising solution for future logistics operations. Full article

(This article belongs to the Special Issue AI-IoT for New Challenges in Smart Cities)

13 pages, 1269 KiB

Open AccessArticle

Innovative Binocular Vision Testing for Phoria and Vergence Ranges Using Automatic Dual Rotational Risley Prisms

by Hui-Rong Su, Yu-Jung Chen, Yun-Shao Hu, Chi-Hung Lee, Shang-Min Yeh and Shuan-Yu Huang

Sensors 2025, 25(5), 1604; https://doi.org/10.3390/s25051604 - 5 Mar 2025

Abstract

This study evaluated binocular visual function using automatic dual rotational Risley prisms (ADRRPs) to measure phoria and vergence ranges. Thirty-nine (mean age: 21.82 ± 1.10 years; age range: 20–24 years) healthy adults with normal binocular vision participated. Each underwent baseline refraction exams followed [...] Read more.

This study evaluated binocular visual function using automatic dual rotational Risley prisms (ADRRPs) to measure phoria and vergence ranges. Thirty-nine (mean age: 21.82 ± 1.10 years; age range: 20–24 years) healthy adults with normal binocular vision participated. Each underwent baseline refraction exams followed by phoria and vergence tests conducted using both a phoropter with Maddox rods and the ADRRPs. The results revealed a strong positive correlation between the two instruments for distance phoria (r = 0.959, p < 0.001) and near-phoria measurements (r = 0.968, p < 0.001). For vergence testing, positive fusional vergence (PFV) at distance showed a moderate-to-strong correlation for break points (r = 0.758, p < 0.001) and a moderate correlation for recovery points (r = 0.452, p < 0.001). Negative fusional vergence (NFV) at distance demonstrated a strong correlation for break points (r = 0.863, p < 0.001) and a moderate correlation for recovery points (r = 0.458, p < 0.01). Near-vergence testing showed moderate-to-strong correlations for break points (r = 0.777, p < 0.001) and recovery points (r = 0.623, p < 0.001). The inclusion of Bland–Altman analysis provides a more comprehensive evaluation of agreement between ADRRPs and the phoropter. While strong correlations were observed, systematic bias and LoA indicate that these methods are not perfectly interchangeable. The ADRRPs demonstrated potential for binocular vision assessment but require further validation for clinical application. Full article

(This article belongs to the Special Issue Advances in Optical Sensing, Instrumentation and Systems: 2nd Edition)

13 pages, 1098 KiB

Open AccessArticle

Fault Detection and Identification in the Doubled Attitude and Heading Reference System (AHRS)

by Grzegorz Kopecki, Bogusław Dołęga and Paweł Rzucidło

Sensors 2025, 25(5), 1603; https://doi.org/10.3390/s25051603 - 5 Mar 2025

Abstract

This article presents a diagnostic system for two redundant AHRS units. The proposed system enables fault detection and identification, facilitating the design of more efficient flight control systems, particularly in low-cost applications. First, the principles of AHRS operation are first introduced, followed by [...] Read more.

This article presents a diagnostic system for two redundant AHRS units. The proposed system enables fault detection and identification, facilitating the design of more efficient flight control systems, particularly in low-cost applications. First, the principles of AHRS operation are first introduced, followed by a detailed description of the diagnostic system and the demonstration of the system’s properties. Simulation results presented in this article confirm the system’s effectiveness in fault detection and identification. The proposed solution can be applied in aeronautical control systems, particularly in UAV applications. Full article

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

24 pages, 587 KiB

Open AccessArticle

Performance Performance Evaluation of a Mesh-Topology LoRa Network

by Thomas Gerhardus Durand and Marthinus Johannes Booysen

Sensors 2025, 25(5), 1602; https://doi.org/10.3390/s25051602 - 5 Mar 2025

Abstract

Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN’s robust physical layer, Long-Range (LoRa), modulation scheme, [...] Read more.

Research into, and the usage of, Low-Power Wide-Area Networks (LPWANs) has increased significantly to support the ever-expanding requirements set by IoT applications. Specifically, the usage of Long-Range Wide-Area Networks (LoRaWANs) has increased, due to the LPWAN’s robust physical layer, Long-Range (LoRa), modulation scheme, which enables scalable, low-power consumption, long-range communication to IoT devices. The LoRaWAN Medium Access Control (MAC) protocol is currently limited to only support single-hop communication. This limits the coverage of a single gateway and increases the power consumption of devices which are located at the edge of a gateway’s coverage range. There is currently no standardised and commercialised multi-hop LoRa-based network, and the field is experiencing ongoing research. In this work, we propose a complementary network to LoRaWAN, which integrates mesh networking. An ns-3 simulation model has been developed, and the proposed LoRaMesh network is simulated for a varying number of scenarios. This research focuses on the design decisions needed to design a LoRa-based mesh network which maintains the low-power consumption advantages that LoRaWAN offers while ensuring that data packets are routed successfully to the gateway. The results highlighted a significant increase in the packet delivery ratio in nodes located far from a centralised gateway in a dense network. Nodes located further than 5.8 km from a gateway’s packet delivery ratio were increased from an average of 40.2% to 73.78%. The findings in this article validate the concept of a mesh-type LPWAN network based on the LoRa physical layer and highlight the potential for future optimisation Full article

(This article belongs to the Special Issue LoRa Communication Technology for IoT Applications)

24 pages, 953 KiB

Open AccessArticle

Sequential Clustering Phases for Environmental Noise Level Monitoring on a Mobile Crowd Sourcing/Sensing Platform

by Fawaz Alhazemi

Sensors 2025, 25(5), 1601; https://doi.org/10.3390/s25051601 - 5 Mar 2025

Abstract

Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm [...] Read more.

Using mobile crowd sourcing/sensing (MCS) noise monitoring can lead to false sound level reporting. The methods used for recruiting mobile phones in an area of interest vary from selecting full populations to randomly selecting a single phone. Other methods apply a clustering algorithm based on spatial or noise parameters to recruit mobile phones to MCS platforms. However, statistical t tests have revealed dissimilarities between these selection methods. In this paper, we assign these dissimilarities to (1) acoustic characteristics and (2) outlier mobile phones affecting the noise level. We propose two clustering phases for noise level monitoring in MCS platforms. The approach starts by applying spatial clustering to form focused clusters and removing spatial outliers. Then, noise level clustering is applied to eliminate noise level outliers. This creates subsets of mobile phones that are used to calculate the noise level. We conducted a real-world experiment with 25 mobile phones and performed a statistical t test evaluation of the selection methodologies. The statistical values indicated dissimilarities. Then, we compared our proposed method with the noise level clustering method in terms of properly detecting and eliminating outliers. Our method offers

4 %

to

12 %

higher performance than the noise clustering method. Full article

(This article belongs to the Special Issue Mobile Sensing for Smart Cities)

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28 pages, 3875 KiB

Open AccessArticle

Machine Learning-Based Measurement and Prediction of Ground Settlement Induced by Shield Tunneling Undercrossing Existing Tunnels in Composite Strata

by Mei Dong, Mingzhe Guan, Kuihua Wang, Yeyao Wu and Yuhan Fu

Sensors 2025, 25(5), 1600; https://doi.org/10.3390/s25051600 - 5 Mar 2025

Abstract

To address the issue of insufficient accuracy in traditional settlement prediction methods for shield tunneling undercrossing in composite strata in Hangzhou, this paper proposes a particle swarm optimization (PSO)-based Bidirectional Long Short-Term Memory neural network (Bi-LSTM) prediction model for high-precision dynamic prediction of [...] Read more.

To address the issue of insufficient accuracy in traditional settlement prediction methods for shield tunneling undercrossing in composite strata in Hangzhou, this paper proposes a particle swarm optimization (PSO)-based Bidirectional Long Short-Term Memory neural network (Bi-LSTM) prediction model for high-precision dynamic prediction of ground settlement under small-sample conditions. Shield tunneling is a key method for urban tunnel construction. This paper presents the measurement and prediction of ground settlement caused by shield tunneling undercrossing existing tunnels in composite strata in Hangzhou. The longitudinal ground settlement curve resulting from shield tunnel excavation was analyzed using measured data, and the measured lateral ground settlement was compared with the Peck empirical formula. Using PSO, the performance of three machine learning models in predicting the maximum ground settlement at monitoring points was compared: Long Short-Term Memory neural network (LSTM), Gated Recurrent Unit neural network (GRU), and Bi-LSTM. The linear relationships between different input parameters and between input parameters and the output parameter were analyzed using the Pearson correlation coefficient. Based on this analysis, the model was optimized, and its prediction performance before and after optimization was compared. The results show that the Bi-LSTM model optimized with the PSO algorithm demonstrates superior performance, achieving both accuracy and stability. Full article

(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)

14 pages, 2045 KiB

Open AccessArticle

Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study

by Carlos Hernando-Ramiro, Óscar Gamallo-Palomares, Javier Junquera-Sánchez and José Antonio Gómez-Sánchez

Sensors 2025, 25(5), 1599; https://doi.org/10.3390/s25051599 - 5 Mar 2025

Abstract

The time to first fix (TTFF) measures the time elapsed by a global navigation satellite system (GNSS) receiver from switch-on to provision of a navigation solution. This parameter is crucial for applications where a position, within an acceptable error, is needed as soon [...] Read more.

The time to first fix (TTFF) measures the time elapsed by a global navigation satellite system (GNSS) receiver from switch-on to provision of a navigation solution. This parameter is crucial for applications where a position, within an acceptable error, is needed as soon as possible after turning the device on. The quality of the TTFF depends mainly on the receiver, the environment, and the GNSS satellites employed. Although all four available GNSSs (BeiDou, Galileo, GLONASS, and GPS) are complementary, their constellations and signals differ, providing different TTFF performances. This becomes even more relevant in hostile environments, where the TTFF degrades from nominal results. In this work, the robustness of the signals of the four GNSSs against different levels of harshness and its influence on the TTFF performance are evaluated in a comparative way. For this purpose, a typical scenario for mass-market GNSS applications, involving cold-start conditions, single-frequency signals, and a low-cost receiver, is considered. The results indicate that GPS provides the most robust TTFF, followed by GLONASS (although at the expense of positioning accuracy), BeiDou, and Galileo, in that order. Full article

(This article belongs to the Special Issue Advances in GNSS Signal Processing and Navigation)

20 pages, 1254 KiB

Open AccessArticle

An Analog Sensor Signal Processing Method Susceptible to Anthropogenic Noise Based on Improved Adaptive Singular Spectrum Analysis

by Zhengyang Gao, Shuangchao Ge, Jie Li, Wentao Huang, Kaiqiang Feng, Chenming Zhang, Chunxing Zhang and Jiaxin Sun

Sensors 2025, 25(5), 1598; https://doi.org/10.3390/s25051598 - 5 Mar 2025

Abstract

Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of decomposition layers, requiring iterative adjustments that reduce precision and [...] Read more.

Sensor measurements are often affected by complex ambient noise and complicating signal processing tasks. The singular spectrum decomposition (SSA) algorithm, while widely used, faces challenges such as the difficulty of determining the number of decomposition layers, requiring iterative adjustments that reduce precision and increase processing time. This paper proposes an improved adaptive singular spectrum analysis (ASSA) algorithm that integrates a deep residual network (Res-Net) for automatic recognition. A comprehensive interference signal database was constructed to train the Deep Res-Net, and common interferences were restored through the combination of different signals, enabling greater frequency resolution performance. Meanwhile, a novel correlation detection reconstruction method based on a clustering algorithm for adaptive signal classification was developed to suppress background noise and extract meaningful signals. ASSA addresses the challenge of determining the optimal number of decomposition layers, eliminating the parameter adjusting process and enhancing the measurement efficiency of sensor systems. Through experiments, magnetotelluric (MT) observation data with complex interferences were applied to demonstrate the performance of ASSA, and promising results with an RMSE of 0.2 were obtained. The experiments also showed that the accuracy of ASSA was improved by 14% compared to other signal extraction algorithms, proving that ASSA can achieve excellent results when applied to other data processing fields. Full article

(This article belongs to the Special Issue Signal Processing and Machine Learning for Sensor Systems)

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9 pages, 2609 KiB

Open AccessArticle

Photoacoustic Imaging for Image-Guided Gastric Tube Placement: Ex Vivo Characterization

by Samuel John, Yeidi Yuja Vaquiz, Nikhila Nyayapathi, Loay Kabbani, Anoop Nilam, Jonathan F. Lovell, Nicole A. Wilson, Yan Yan and Mohammad Mehrmohammadi

Sensors 2025, 25(5), 1597; https://doi.org/10.3390/s25051597 - 5 Mar 2025

Abstract

Over 250,000 gastrostomy tubes (G-tubes) are placed annually in the United States. Percutaneous endoscopic gastrostomy (PEG) is the most widely used clinical method for placing G-tubes within the stomach. However, endoscope detectability is limited due to the scattering of light by tissues. Poor [...] Read more.

Over 250,000 gastrostomy tubes (G-tubes) are placed annually in the United States. Percutaneous endoscopic gastrostomy (PEG) is the most widely used clinical method for placing G-tubes within the stomach. However, endoscope detectability is limited due to the scattering of light by tissues. Poor organ visibility and low sensitivity of the palpation techniques cause blind needle insertions, which cause colon/liver perforations, abdominal bleeding, and gastric resections. Additionally, imaging artifacts and the poor distinguishability between water-filled tissues make ultrasound (US) imaging-based techniques incompatible with G-tube placement. The risk of ionizing radiation exposure and the confinement of fluoroscopy to radiology suites limits its bedside utility in patients. Considering these limitations, we propose to design a safe, point-of-care integrated US and photoacoustic (PA) imaging system for accurate G-tube placement procedures, for a broad spectrum of patients, and to characterize the system’s effectiveness. Our proposed technology utilizes a clinically safe contrast agent and a dual-wavelength approach for precise procedures. Our ex vivo tissue studies indicated that PA imaging accurately differentiates the different organs at specific wavelengths. Our characterization studies revealed that PA imaging could detect lower concentrations of Indocyanine Green (ICG) dye coating the colon wall, minimizing the risk of ICG dye-related toxicity and providing safer G-tube placements. Full article

(This article belongs to the Special Issue Advances in Photoacoustic Resonators and Sensors)

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17 pages, 6231 KiB

Open AccessArticle

Enhancing Image Reconstruction Method in High-Frequency Electric Field Visualization Systems Using a Polarized Light Image Sensor

by Kiyotaka Sasagawa, Ryoma Okada, Maya Mizuno, Hironari Takehara, Makito Haruta, Hiroyuki Tashiro and Jun Ohta

Sensors 2025, 25(5), 1596; https://doi.org/10.3390/s25051596 - 5 Mar 2025

Abstract

This paper introduces an image processing method, used to achieve uniform sensitivity across the imaging plane in a high-frequency electric field imaging system, that employs an electro-optical crystal and a polarization image sensor. The polarization pixels have two polarization directions, 0° and 90°, [...] Read more.

This paper introduces an image processing method, used to achieve uniform sensitivity across the imaging plane in a high-frequency electric field imaging system, that employs an electro-optical crystal and a polarization image sensor. The polarization pixels have two polarization directions, 0° and 90°, in pairs, and, conventionally, their difference is computed first. In contrast, this study proposes a method to separate each polarization image, perform pixel completion, and subsequently perform intensity correction. The proposed method was demonstrated to improve field distribution images acquired using 36 GHz and 30 GHz input signals for a microstrip line and patch antenna, respectively. From the measurement results of the microstrip line, the application of the proposed method reduced the electric field fluctuations on the line from 3.1 dB to 1.5 dB. This image-processing method can be applied sequentially during image acquisition, making it suitable for the real-time imaging of electric fields. Full article

(This article belongs to the Section Sensing and Imaging)

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22 pages, 6298 KiB

Open AccessArticle

BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection

by Hua Zhao, Chao Xu, Jiaxing Chen, Zhexian Zhang and Xiang Wang

Sensors 2025, 25(5), 1595; https://doi.org/10.3390/s25051595 - 5 Mar 2025

Abstract

Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort [...] Read more.

Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. Finally, to prevent the detection accuracy impact due to excessive lightweighting, the lightweight shared head (LSH) is constructed. The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB. Full article

(This article belongs to the Topic Applications and Development of Underwater Robotics and Underwater Vision Technology)

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29 pages, 9831 KiB

Open AccessArticle

Quality of Experience (QoE) in Cloud Gaming: A Comparative Analysis of Deep Learning Techniques via Facial Emotions in a Virtual Reality Environment

by Awais Khan Jumani, Jinglun Shi, Asif Ali Laghari, Muhammad Ahmad Amin, Aftab ul Nabi, Kamlesh Narwani and Yi Zhang

Sensors 2025, 25(5), 1594; https://doi.org/10.3390/s25051594 - 5 Mar 2025

Abstract

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods [...] Read more.

Cloud gaming has rapidly transformed the gaming industry, allowing users to play games on demand from anywhere without the need for powerful hardware. Cloud service providers are striving to enhance user Quality of Experience (QoE) using traditional assessment methods. However, these traditional methods often fail to capture the actual user QoE because some users are not serious about providing feedback regarding cloud services. Additionally, some players, even after receiving services as per the Service Level Agreement (SLA), claim that they are not receiving services as promised. This poses a significant challenge for cloud service providers in accurately identifying QoE and improving actual services. In this paper, we have compared our previous proposed novel technique that utilizes a deep learning (DL) model to assess QoE through players’ facial expressions during cloud gaming sessions in a virtual reality (VR) environment. The EmotionNET model technique is based on a convolutional neural network (CNN) architecture. Later, we have compared the EmotionNET technique with three other DL techniques, namely ConvoNEXT, EfficientNET, and Vision Transformer (ViT). We trained the EmotionNET, ConvoNEXT, EfficientNET, and ViT model techniques on our custom-developed dataset, achieving 98.9% training accuracy and 87.8% validation accuracy with the EmotionNET model technique. Based on the training and comparison results, it is evident that the EmotionNET model technique predicts and performs better than the other model techniques. At the end, we have compared the EmotionNET results on two network (WiFi and mobile data) datasets. Our findings indicate that facial expressions are strongly correlated with QoE. Full article

(This article belongs to the Special Issue Deep Learning Applications for Pose Estimation and Human Action Recognition—2nd Edition)

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17 pages, 8074 KiB

Open AccessArticle

Automated Segmentation of Breast Cancer Focal Lesions on Ultrasound Images

by Dmitry Pasynkov, Ivan Egoshin, Alexey Kolchev, Ivan Kliouchkin, Olga Pasynkova, Zahraa Saad, Anis Daou and Esam Mohamed Abuzenar

Sensors 2025, 25(5), 1593; https://doi.org/10.3390/s25051593 - 5 Mar 2025

Abstract

Ultrasound (US) remains the main modality for the differential diagnosis of changes revealed by mammography. However, the US images themselves are subject to various types of noise and artifacts from reflections, which can worsen the quality of their analysis. Deep learning methods have [...] Read more.

Ultrasound (US) remains the main modality for the differential diagnosis of changes revealed by mammography. However, the US images themselves are subject to various types of noise and artifacts from reflections, which can worsen the quality of their analysis. Deep learning methods have a number of disadvantages, including the often insufficient substantiation of the model, and the complexity of collecting a representative training database. Therefore, it is necessary to develop effective algorithms for the segmentation, classification, and analysis of US images. The aim of the work is to develop a method for the automated detection of pathological lesions in breast US images and their segmentation. A method is proposed that includes two stages of video image processing: (1) searching for a region of interest using a random forest classifier, which classifies normal tissues, (2) selecting the contour of the lesion based on the difference in brightness of image pixels. The test set included 52 ultrasound videos which contained histologically proven suspicious lesions. The average frequency of lesion detection per frame was 91.89%, and the average accuracy of contour selection according to the IoU metric was 0.871. The proposed method can be used to segment a suspicious lesion. Full article

(This article belongs to the Section Sensing and Imaging)

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21 pages, 1959 KiB

Open AccessArticle

Coverage Path Planning Using Actor–Critic Deep Reinforcement Learning

by Sergio Isahí Garrido-Castañeda, Juan Irving Vasquez and Mayra Antonio-Cruz

Sensors 2025, 25(5), 1592; https://doi.org/10.3390/s25051592 - 5 Mar 2025

Abstract

One of the main capabilities a mobile robot must demonstrate is the ability to explore its environment. The core challenge in exploration lies in planning the route to fully cover the environment. Despite recent advances, this problem remains unsolved. This study proposes an [...] Read more.

One of the main capabilities a mobile robot must demonstrate is the ability to explore its environment. The core challenge in exploration lies in planning the route to fully cover the environment. Despite recent advances, this problem remains unsolved. This study proposes an approach to address the coverage path planning problem, where the mobile robot is tasked with exploring and completely covering a terrain using a deep reinforcement learning framework. The environment is divided into cells, with obstacles designated as prohibited areas. The robot is trained using two state-of-the-art reinforcement learning algorithms based on actor–critic methods: Advantage Actor–Critic (A2C) and Proximal Policy Optimization (PPO). By defining a set of observations, states, and a reward function tailored to characteristics of the environment and the desired behavior of the robot, the training process is conducted, resulting in optimized policies for each algorithm. Then, these policies are evaluated to determine the most effective approach to accomplish the proposed task. Our findings demonstrate that actor–critic methods can produce policies capable of guiding a robot to efficiently explore and cover new environments. Full article

(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)

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34 pages, 2273 KiB

Open AccessArticle

SimulatorOrchestrator: A 6G-Ready Simulator for the Cell-Free/Osmotic Infrastructure

by Rohin Gillgallon, Reham Almutairi, Giacomo Bergami and Graham Morgan

Sensors 2025, 25(5), 1591; https://doi.org/10.3390/s25051591 - 5 Mar 2025

Abstract

To the best of our knowledge, we offer the first IoT-Osmotic simulator supporting 6G and Cloud infrastructures, leveraging the similarities in Software-Defined Wide Area Network (SD-WAN) architectures when used in Osmotic architectures and User-Centric Cell-Free mMIMO (massive multiple-input multiple-output) architectures. Our simulator acts [...] Read more.

To the best of our knowledge, we offer the first IoT-Osmotic simulator supporting 6G and Cloud infrastructures, leveraging the similarities in Software-Defined Wide Area Network (SD-WAN) architectures when used in Osmotic architectures and User-Centric Cell-Free mMIMO (massive multiple-input multiple-output) architectures. Our simulator acts as a simulator orchestrator, supporting the interaction with a patient digital twin generating patient healthcare data (vital signs and emergency alerts) and a VANET simulator (SUMO), both leading to IoT data streams towards the cloud through pre-initiated MQTT protocols. This contextualises our approach within the healthcare domain while showcasing the possibility of orchestrating different simulators at the same time. The combined provision of these two aspects, joined with the addition of a ring network connecting all the first-mile edge nodes (i.e., access points), enables the definition of new packet routing algorithms, streamlining previous solutions from SD-WAN architectures, thus showing the benefit of 6G architectures in achieving better network load balancing, as well as showcasing the limitations of previous approaches. The simulated 6G architecture, combined with the optimal routing algorithm and MEL (Microelements software components) allocation policy, was able to reduce the time required to route all communications from IoT devices to the cloud by up to 50.4% compared to analogous routing algorithms used within 5G architectures. Full article

(This article belongs to the Special Issue e-Health Systems and Technologies)

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19 pages, 875 KiB

Open AccessArticle

Highly Accurate Adaptive Federated Forests Based on Resistance to Adversarial Attacks in Wireless Traffic Prediction

by Lingyao Wang , Chenyue Pan , Haitao Zhao , Mingyi Ji , Xinren Wang , Junchen Yuan , Miao Liu and Donglai Jiao

Sensors 2025, 25(5), 1590; https://doi.org/10.3390/s25051590 - 5 Mar 2025

Abstract

Current 5G communication services have limitations, prompting the development of the Beyond 5G (B5G) network. B5G aims to extend the scope of communication to encompass land, sea, air, and space while enhancing communication intelligence and evolving into an omnipresent converged information network. This [...] Read more.

Current 5G communication services have limitations, prompting the development of the Beyond 5G (B5G) network. B5G aims to extend the scope of communication to encompass land, sea, air, and space while enhancing communication intelligence and evolving into an omnipresent converged information network. This expansion demands higher standards for communication rates and intelligent processing across multiple devices. Furthermore, traffic prediction is crucial for the intelligent and efficient planning and management of communication networks, optimizing resource allocation, and enhancing network performance and communication speeds and is an important part of B5G’s performance. Federated learning addresses privacy and transmission cost issues in model training, making it widely applicable in traffic prediction. However, traditional federated learning models are susceptible to adversarial attacks that can compromise model outcomes. To safeguard traffic prediction from such attacks and ensure the reliability of the prediction system, this paper introduces the Adaptive Threshold Modified Federated Forest (ATMFF). ATMFF employs adaptive threshold modification, utilizing a confusion matrix rate-based screening-weighted aggregation of weak classifiers to adjust the decision threshold. This approach enhances the accuracy of recognizing adversarial samples, thereby ensuring the reliability of the traffic prediction model. Our experiments, based on real 5G traffic data, demonstrate that ATMFF’s adversarial sample recognition accuracy surpasses that of traditional multiboost models and models without adaptive threshold modified. This improvement bolsters the security and reliability of intelligent traffic classification services. Full article

(This article belongs to the Section Sensor Networks)

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26 pages, 7114 KiB

Open AccessArticle

Fault Diagnosis Method for Centrifugal Pumps in Nuclear Power Plants Based on a Multi-Scale Convolutional Self-Attention Network

by Chen Li, Xinkai Liu, Hang Wang and Minjun Peng

Sensors 2025, 25(5), 1589; https://doi.org/10.3390/s25051589 - 5 Mar 2025

Abstract

The health status of rotating machinery equipment in nuclear power plants is of paramount importance for ensuring the overall normal operation of the power plant system. In particular, significant failures in large rotating machinery equipment, such as main pumps, pose critical safety hazards [...] Read more.

The health status of rotating machinery equipment in nuclear power plants is of paramount importance for ensuring the overall normal operation of the power plant system. In particular, significant failures in large rotating machinery equipment, such as main pumps, pose critical safety hazards to the system. Therefore, this paper takes pump equipment as a representative of rotating machinery in nuclear power plants and proposes a fault diagnosis method based on a multi-scale convolutional self-attention network for three types of faults: outer ring fracture, inner ring fracture, and rolling element pitting corrosion. Within the multi-scale convolutional self-attention network, a multi-scale hybrid feature complementarity mechanism is introduced. This mechanism leverages an adaptive encoder to capture deep feature information from the acoustic signals of rolling bearings and constructs a hybrid-scale feature set based on deep features and original signal characteristics in the time–frequency domain. This approach enriches the fault information present in the feature set and establishes a nonlinear mapping relationship between fault features and rolling bearing faults. The results demonstrate that, without significantly increasing model complexity or the volume of feature data, this method achieves a substantial increase in fault diagnosis accuracy, exceeding 99.5% under both vibration signal and acoustic signal conditions. Full article

(This article belongs to the Section Physical Sensors)

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18 pages, 4938 KiB

Open AccessStudy Protocol

Optimization of Control Point Layout for Orthophoto Generation of Indoor Murals

by Dingfei Yan and Yongming Yang

Sensors 2025, 25(5), 1588; https://doi.org/10.3390/s25051588 - 5 Mar 2025

Abstract

This study focuses on the preservation of indoor murals, which can be supported by combining RTK and total station technology to explore the optimization of image geometric accuracy based on a control points layout. The study involves placing varying numbers of control points [...] Read more.

This study focuses on the preservation of indoor murals, which can be supported by combining RTK and total station technology to explore the optimization of image geometric accuracy based on a control points layout. The study involves placing varying numbers of control points on the mural surface and processing the collected data using a spatial coordinate transformation model to assess the impact of different layouts on image accuracy. Some control points are used to ensure the spatial positioning accuracy of the images, while others serve as check points to validate the geometric precision of the images. After data processing, high-precision digital orthophotos are generated using Agisoft PhotoScan2.0.1 software, with accuracy verified by the check points. The experimental results show that as the number of control points increases, image accuracy improves gradually. When the number of control points reaches 24, the geometric accuracy of the images stabilizes, and further increases in the number of control points have a limited effect on improving accuracy. Therefore, the study proposes an optimal layout scheme: 24 control points for every 16 square meters. This scheme not only meets millimeter-level precision requirements but also effectively optimizes resource allocation and reduces time costs. The research provides reliable data support for the high-precision preservation and restoration of murals and offers important references for similar cultural heritage preservation projects. Full article

(This article belongs to the Special Issue Innovative Approaches in 3D Sensing and Imaging Technologies for Cultural Heritage)

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12 pages, 4491 KiB

Open AccessCommunication

Label-Free Extended Gate Field-Effect Transistor for Sensing Microcystin-LR in Freshwater Samples

by Sondavid Nandanwar, Songyi Lee, Myeongkee Park and Hak Jun Kim

Sensors 2025, 25(5), 1587; https://doi.org/10.3390/s25051587 - 5 Mar 2025

Abstract

In this study, we developed a label-free biosensor based on aptamer-modified multi-walled carbon nanotube extended gate field-effect transistor (MWCNT-EG-FET) for easy and selective detection of microcystin-LR (MC-LR), a prominent cyanotoxin associated with liver damage, bleeding, and necrosis. EG-FET had two parts: a MOSFET [...] Read more.

In this study, we developed a label-free biosensor based on aptamer-modified multi-walled carbon nanotube extended gate field-effect transistor (MWCNT-EG-FET) for easy and selective detection of microcystin-LR (MC-LR), a prominent cyanotoxin associated with liver damage, bleeding, and necrosis. EG-FET had two parts: a MOSFET and an extended-gate Au/SiO₂ electrode, which serves as the sensitive membrane. A custom-designed DNA oligonucleotide (5-NH₂-C₆-AN6) was used as MC-LR-targeting aptamer (MCTA). MWCNTs were functionalized with MCTA and then stably fixed on the sensitive membrane. The detection of MC-LR in freshwater was effectively achieved within 5 min by assessing the variations in electrical resistance that occur due to the selective interactions between MC-LR and MCTA. The detection limit and analytical sensitivity of the biosensor for MC-LR were found to be 0.134 ng/mL and 0.024 ng/mL, respectively. The sensitive membrane could be readily discarded if damaged, eliminating the need to replace the main transducer MOSFET. The developed sensor exhibits features such as straightforward preparation, swift response, potential for miniaturization, and ease of use, making it an attractive candidate for future integrated lab-on-chip systems for MC-LR detection in freshwater environments. Full article

(This article belongs to the Collection Sensors and Biosensors for Environmental and Food Applications)

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