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Volume 14
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J. Sens. Actuator Netw., Volume 14, Issue 5 (October 2025) – 16 articles

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30 pages, 27154 KB  
Article
The Modeling and Detection of Vascular Stenosis Based on Molecular Communication in the Internet of Things
by Zitong Shao, Pengfei Zhang, Xiaofang Wang and Pengfei Lu
J. Sens. Actuator Netw. 2025, 14(5), 101; https://doi.org/10.3390/jsan14050101 - 10 Oct 2025
Viewed by 38
Abstract
Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which [...] Read more.
Molecular communication (MC) has emerged as a promising paradigm for nanoscale information exchange in Internet of Bio-Nano Things (IoBNT) environments, offering intrinsic biocompatibility and potential for real-time in vivo monitoring. This study proposes a cascaded MC channel framework for vascular stenosis detection, which integrates non-Newtonian blood rheology, bell-shaped constriction geometry, and adsorption–desorption dynamics. Path delay and path loss are introduced as quantitative metrics to characterize how structural narrowing and molecular interactions jointly affect signal propagation. On this basis, a peak response time-based delay inversion method is developed to estimate both the location and severity of stenosis. COMSOL 6.2 simulations demonstrate high spatial resolution and resilience to measurement noise across diverse vascular configurations. By linking nanoscale transport dynamics with system-level detection, the approach establishes a tractable pathway for the early identification of vascular anomalies. Beyond theoretical modeling, the framework underscores the translational potential of MC-based diagnostics. It provides a foundation for non-invasive vascular health monitoring in IoT-enabled biomedical systems with direct relevance to continuous screening and preventive cardiovascular care. Future in vitro and in vivo studies will be essential to validate feasibility and support integration with implantable or wearable biosensing devices, enabling real-time, personalized health management. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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34 pages, 3231 KB  
Review
A Review of Smart Crop Technologies for Resource Constrained Environments: Leveraging Multimodal Data Fusion, Edge-to-Cloud Computing, and IoT Virtualization
by Damilola D. Olatinwo, Herman C. Myburgh, Allan De Freitas and Adnan M. Abu-Mahfouz
J. Sens. Actuator Netw. 2025, 14(5), 99; https://doi.org/10.3390/jsan14050099 - 9 Oct 2025
Viewed by 241
Abstract
Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent [...] Read more.
Smart crop technologies offer promising solutions for enhancing agricultural productivity and sustainability, particularly in the face of global challenges such as resource scarcity and climate variability. However, their deployment in infrastructure-limited regions, especially across Africa, faces persistent barriers, including unreliable power supply, intermittent internet connectivity, and limited access to technical expertise. This study presents a PRISMA-guided systematic review of literature published between 2015 and 2025, sourced from the Scopus database including indexed content from ScienceDirect and IEEE Xplore. It focuses on key technological components including multimodal sensing, data fusion, IoT resource management, edge-cloud integration, and adaptive network design. The analysis of these references reveals a clear trend of increasing research volume and a major shift in focus from foundational unimodal sensing and cloud computing to more complex solutions involving machine learning post-2019. This review identifies critical gaps in existing research, particularly the lack of integrated frameworks for effective multimodal sensing, data fusion, and real-time decision support in low-resource agricultural contexts. To address this, we categorize multimodal sensing approaches and then provide a structured taxonomy of multimodal data fusion approaches for real-time monitoring and decision support. The review also evaluates the role of IoT virtualization as a pathway to scalable, adaptive sensing systems, and analyzes strategies for overcoming infrastructure constraints. This study contributes a comprehensive overview of smart crop technologies suited to infrastructure-limited agricultural contexts and offers strategic recommendations for deploying resilient smart agriculture solutions under connectivity and power constraints. These findings provide actionable insights for researchers, technologists, and policymakers aiming to develop sustainable and context-aware agricultural innovations in underserved regions. Full article
(This article belongs to the Special Issue Remote Sensing and IoT Application for Smart Agriculture)
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36 pages, 1954 KB  
Article
VeMisNet: Enhanced Feature Engineering for Deep Learning-Based Misbehavior Detection in Vehicular Ad Hoc Networks
by Nayera Youness, Ahmad Mostafa, Mohamed A. Sobh, Ayman M. Bahaa and Khaled Nagaty
J. Sens. Actuator Netw. 2025, 14(5), 100; https://doi.org/10.3390/jsan14050100 - 9 Oct 2025
Viewed by 100
Abstract
Ensuring secure and reliable communication in Vehicular Ad hoc Networks (VANETs) is critical for safe transportation systems. This paper presents Vehicular Misbehavior Network (VeMisNet), a deep learning framework for detecting misbehaving vehicles, with primary contributions in systematic feature engineering and scalability analysis. VeMisNet [...] Read more.
Ensuring secure and reliable communication in Vehicular Ad hoc Networks (VANETs) is critical for safe transportation systems. This paper presents Vehicular Misbehavior Network (VeMisNet), a deep learning framework for detecting misbehaving vehicles, with primary contributions in systematic feature engineering and scalability analysis. VeMisNet introduces domain-informed spatiotemporal features—including DSRC neighborhood density, inter-message timing patterns, and communication frequency analysis—derived from the publicly available VeReMi Extension Dataset. The framework evaluates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM architectures across dataset scales from 100 K to 2 M samples, encompassing all 20 attack categories. To address severe class imbalance (59.6% legitimate vehicles), VeMisNet applies SMOTE post train–test split, preventing data leakage while enabling balanced evaluation. Bidirectional LSTM with engineered features achieves 99.81% accuracy and F1-score on 500 K samples, with remarkable scalability maintaining >99.5% accuracy at 2 M samples. Critical metrics include 0.19% missed attack rates, under 0.05% false alarms, and 41.76 ms inference latency. The study acknowledges important limitations, including reliance on simulated data, single-split evaluation, and potential adversarial vulnerability. Domain-informed feature engineering provides 27.5% relative improvement over dimensionality reduction and 22-fold better scalability than basic features. These results establish new VANET misbehavior detection benchmarks while providing honest assessment of deployment readiness and research constraints. Full article
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28 pages, 1927 KB  
Systematic Review
Drone Imaging and Sensors for Situational Awareness in Hazardous Environments: A Systematic Review
by Siripan Rattanaamporn, Asanka Perera, Andy Nguyen, Thanh Binh Ngo and Javaan Chahl
J. Sens. Actuator Netw. 2025, 14(5), 98; https://doi.org/10.3390/jsan14050098 - 29 Sep 2025
Viewed by 463
Abstract
Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This [...] Read more.
Situation awareness is essential for ensuring safety in hazardous environments, where timely and accurate information is critical for decision-making. Unmanned Aerial Vehicles (UAVs) have emerged as valuable tools in enhancing situation awareness by providing real-time data and monitoring capabilities in high-risk areas. This study explores the integration of advanced technologies, focusing on imaging and sensor technologies such as thermal, spectral, and multispectral cameras, deployed in critical zones. By merging these technologies into UAV platforms, responders gain access to essential real-time information while reducing human exposure to hazardous conditions. This study presents case studies and practical applications, highlighting the effectiveness of these technologies in a range of hazardous situations. Full article
(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)
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42 pages, 5827 KB  
Review
A Review of Reconfigurable Intelligent Surfaces in Underwater Wireless Communication: Challenges and Future Directions
by Tharuka Govinda Waduge, Yang Yang and Boon-Chong Seet
J. Sens. Actuator Netw. 2025, 14(5), 97; https://doi.org/10.3390/jsan14050097 - 26 Sep 2025
Viewed by 769
Abstract
Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater [...] Read more.
Underwater wireless communication (UWC) is an emerging technology crucial for automating marine industries, such as offshore aquaculture and energy production, and military applications. It is a key part of the 6G vision of creating a hyperconnected world for extending connectivity to the underwater environment. Of the three main practicable UWC technologies (acoustic, optical, and radiofrequency), acoustic methods are best for far-reaching links, while optical is best for high-bandwidth communication. Recently, utilizing reconfigurable intelligent surfaces (RISs) has become a hot topic in terrestrial applications, underscoring significant benefits for extending coverage, providing connectivity to blind spots, wireless power transmission, and more. However, the potential for further research works in underwater RIS is vast. Here, for the first time, we conduct an extensive survey of state-of-the-art of RIS and metasurfaces with a focus on underwater applications. Within a holistic perspective, this survey systematically evaluates acoustic, optical, and hybrid RIS, showing that environment-aware channel switching and joint communication architectures could deliver holistic gains over single-domain RIS in the distance–bandwidth trade-off, congestion mitigation, security, and energy efficiency. Additional focus is placed on the current challenges from research and realization perspectives. We discuss recent advances and suggest design considerations for coupling hybrid RIS with optical energy and piezoelectric acoustic energy harvesting, which along with distributed relaying, could realize self-sustainable underwater networks that are highly reliable, long-range, and high throughput. The most impactful future directions seem to be in applying RIS for enhancing underwater links in inhomogeneous environments and overcoming time-varying effects, realizing RIS hardware suitable for the underwater conditions, and achieving simultaneous transmission and reflection (STAR-RIS), and, particularly, in optical links—integrating the latest developments in metasurfaces. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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15 pages, 603 KB  
Article
A Hybrid CNN–GRU Deep Learning Model for IoT Network Intrusion Detection
by Kuburat Oyeranti Adefemi, Murimo Bethel Mutanga and Oyeniyi Akeem Alimi
J. Sens. Actuator Netw. 2025, 14(5), 96; https://doi.org/10.3390/jsan14050096 - 26 Sep 2025
Viewed by 600
Abstract
Internet of Things (IoT) networks are constantly exposed to various security challenges and vulnerabilities, including manipulative data injections and cyberattacks. Traditional security measures are often inadequate, overburdened, and unreliable in adapting to the heterogeneous yet diverse nature of IoT networks. This emphasizes the [...] Read more.
Internet of Things (IoT) networks are constantly exposed to various security challenges and vulnerabilities, including manipulative data injections and cyberattacks. Traditional security measures are often inadequate, overburdened, and unreliable in adapting to the heterogeneous yet diverse nature of IoT networks. This emphasizes the need for intelligent and effective methodologies. In recent times, deep learning models have been extensively used to monitor and detect intrusions in complex applications. The models can effectively learn and understand the dynamic characteristics of voluminous IoT datasets to prompt efficient decision-making predictions. This study proposes a hybrid Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) algorithm to enhance intrusion detection in IoT environments. The proposed CNN-GRU model is validated using two benchmark datasets: the IoTID20 and BoT-IoT intrusion detection datasets. The proposed model incorporates an effective technique to handle the class imbalance issues that are peculiar to voluminous datasets. The results demonstrate superior accuracy, precision, recall, F1-score, and area under the curve, with a reduced false positive rate compared to similar models in the literature. Specifically, the proposed CNN–GRU achieved up to 99.83% and 99.01% accuracy, surpassing baseline models by a margin of 2–3% across both datasets. These findings highlight the model’s potential for real-time cybersecurity applications in IoT networks and general industrial control systems. Full article
(This article belongs to the Special Issue Security and Smart Applications in IoT and Wireless Sensor and Actuator Networks)
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16 pages, 585 KB  
Article
T-Way Combinatorial Testing Strategy Using a Refined Evolutionary Heuristic
by Peng Lin, Jinzhao She and Xiang Chen
J. Sens. Actuator Netw. 2025, 14(5), 95; https://doi.org/10.3390/jsan14050095 - 25 Sep 2025
Viewed by 350
Abstract
In complex testing scenarios of large-scale information systems, communication networks, and the Internet of Things, exhaustive testing is always prohibitively expensive and time-consuming. T-way combinatorial testing has emerged as a cost-effective solution. To address the problem of generating test suites for t [...] Read more.
In complex testing scenarios of large-scale information systems, communication networks, and the Internet of Things, exhaustive testing is always prohibitively expensive and time-consuming. T-way combinatorial testing has emerged as a cost-effective solution. To address the problem of generating test suites for t-way combinatorial testing, a Logical Combination Index Table (LCIT) is proposed. Utilizing the LCIT, the t-way combinatorial coverage model (t-wCCM) is constructed to guide the test case generation process. Multi-start Construction Procedure (MsCP) algorithm is employed to generate an initial solution set, and then local optimization is performed using a low-complexity Balanced Local Search (BLS) algorithm. Further, Evolutionary Path Relinking combined with the BLS (EvPR + BLS) algorithm is proposed to accelerate the convergence process. Experiments show that the proposed Refined Evolutionary Heuristic (REH) algorithm performs best on 50% of the classic test instances, and performs superior to the average on 66% of the test instances, with a relative improvement in the maximum computation time of approximately 33.33%. Full article
(This article belongs to the Section Communications and Networking)
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42 pages, 12059 KB  
Review
A Survey of Three-Dimensional Wireless Sensor Networks Deployment Techniques
by Tingting Cao, Fan Yang, Chensiyu Fan, Ru Han, Xing Yang and Lei Shu
J. Sens. Actuator Netw. 2025, 14(5), 94; https://doi.org/10.3390/jsan14050094 - 24 Sep 2025
Viewed by 459
Abstract
Three-dimensional (3D) wireless sensor networks (WSNs) are gaining increasing significance in applications across complex environments, including underwater monitoring, mountainous terrains, and smart cities. Compared to two-dimensional (2D) WSNs, 3D WSNs introduce unique challenges in coverage, connectivity, map construction, and blind area detection. This [...] Read more.
Three-dimensional (3D) wireless sensor networks (WSNs) are gaining increasing significance in applications across complex environments, including underwater monitoring, mountainous terrains, and smart cities. Compared to two-dimensional (2D) WSNs, 3D WSNs introduce unique challenges in coverage, connectivity, map construction, and blind area detection. This paper provides a comprehensive survey of node deployment strategies in 3D WSNs. We summarize several key design aspects: sensing models, occlusion detection, coverage and connectivity, sensor mobility, signal and protocol effects, and simulation map construction. Deployment algorithms are categorized into six main types: classical algorithms, computational geometry algorithms, virtual force algorithms, evolutionary algorithms, swarm intelligence algorithms, and approximation algorithms. For each category, we review representative works, analyze their design principles, and evaluate their advantages and limitations. Comparative summaries are included to facilitate algorithm selection based on specific deployment requirements. Recent advancements in these strategies have led to significant improvements in network performance, with some algorithms achieving up to 12.5% lower cost and 30% higher coverage compared to earlier methods, and even reaching 100% coverage in certain cases. Thus, this survey aims to present the current research status and highlight practical improvements, offering a reference for understanding existing approaches and selecting appropriate algorithms for diverse deployment scenarios. Full article
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20 pages, 7858 KB  
Article
Optimizing CO2 Monitoring: Evaluating a Sensor Network Design
by Kenia Elizabeth Sabando-Bravo, Marlon Navia and Jorge Luis Zambrano-Martinez
J. Sens. Actuator Netw. 2025, 14(5), 93; https://doi.org/10.3390/jsan14050093 - 19 Sep 2025
Viewed by 508
Abstract
In the present work, a sensor network design for monitoring carbon dioxide (CO2) pollution in Portoviejo City, Ecuador, is evaluated through a methodology that combines simulation and physical implementation. This methodology involves the development and evaluation of two scenarios: an initial [...] Read more.
In the present work, a sensor network design for monitoring carbon dioxide (CO2) pollution in Portoviejo City, Ecuador, is evaluated through a methodology that combines simulation and physical implementation. This methodology involves the development and evaluation of two scenarios: an initial scenario (A), developed through both physical implementation and simulation, and another simulation scenario (B). Both simulated scenarios are created using CupCarbon version 6.51 software. In these scenarios, the functionality of Wireless Sensor Networks (WSNs) is analyzed by implementing the LoRaWAN communication technology. Furthermore, the MQ-135 sensor is used to obtaining data on the PPM of (CO2) in order to examine the areas that concentrate the most significant amount of this atmospheric pollutant. The proposed networks are evaluated using the packet loss metric during data transmission. After implementation, analysis, and respective evaluation, it can be concluded that the network simulated in Scenario B is suitable for monitoring (CO2) and other pollutants that can be analyzed within the urban environment. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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16 pages, 15073 KB  
Article
A Bidirectional, Full-Duplex, Implantable Wireless CMOS System for Prosthetic Control
by Riccardo Collu, Cinzia Salis, Elena Ferrazzano and Massimo Barbaro
J. Sens. Actuator Netw. 2025, 14(5), 92; https://doi.org/10.3390/jsan14050092 - 10 Sep 2025
Viewed by 645
Abstract
Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s [...] Read more.
Implantable medical devices present several technological challenges, one of the most critical being how to provide power supply and communication capabilities to a device hermetically sealed within the body. Using a battery as a power source represents a potential harm for the individual’s health because of possible toxic chemical release or overheating, and it requires periodic surgery for replacement. This paper proposes a batteryless implantable device powered by an inductive link and equipped with bidirectional wireless communication channels. The device, designed in a 180 nm CMOS process, is based on two different pairs of mutually coupled inductors that provide, respectively, power and a low-bitrate bidirectional communication link and a separate, high-bitrate, one-directional upstream connection. The main link is based on a 13.56 MHz carrier and allows power transmission and a half-duplex two-way communication at 106 kbps (downlink) and 30 kbps (uplink). The secondary link is based on a 27 MHz carrier, which provides one-way communication at 2.25 Mbps only in uplink. The low-bitrate links are needed to send commands and monitor the implanted system, while the high-bitrate link is required to receive a continuous stream of information from the implanted sensing devices. The microchip acts as a hub for power and data wireless transmission capable of managing up to four different neural recording and stimulation front ends, making the device employable in a complex, distributed, bidirectional neural prosthetic system. Full article
(This article belongs to the Topic Wireless Energy Harvesting and Power Transfer for Communications and Networks)
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24 pages, 3485 KB  
Article
Impact Evaluation of Sound Dataset Augmentation and Synthetic Generation upon Classification Accuracy
by Eleni Tsalera, Andreas Papadakis, Gerasimos Pagiatakis and Maria Samarakou
J. Sens. Actuator Netw. 2025, 14(5), 91; https://doi.org/10.3390/jsan14050091 - 9 Sep 2025
Viewed by 730
Abstract
We investigate the impact of dataset augmentation and synthetic generation techniques on the accuracy of supervised audio classification based on state-of-the-art neural networks used as classifiers. Dataset augmentation techniques are applied upon the raw sound and its transformed image format. Specifically, sound augmentation [...] Read more.
We investigate the impact of dataset augmentation and synthetic generation techniques on the accuracy of supervised audio classification based on state-of-the-art neural networks used as classifiers. Dataset augmentation techniques are applied upon the raw sound and its transformed image format. Specifically, sound augmentation techniques are applied prior to spectral-based transformation and include time stretching, pitch shifting, noise addition, volume controlling, and time shifting. Image augmentation techniques are applied after the transformation of the sound into a scalogram, involving scaling, shearing, rotation, and translation. Synthetic sound generation is based on the AudioGen generative model, triggered through a series of customized prompts. Augmentation and synthetic generation are applied to three sound categories: (a) human sounds, (b) animal sounds, and (c) sounds of things, with each category containing ten sound classes with 20 samples retrieved from the ESC-50 dataset. Sound- and image-orientated neural network classifiers have been used to classify the augmented datasets and their synthetic additions. VGGish and YAMNet (sound classifiers) employ spectrograms, while ResNet50 and DarkNet53 (image classifiers) employ scalograms. The streamlined AI-based process of augmentation and synthetic generation, enhanced classifier fine-tuning and inference allowed for a consistent, multicriteria-comparison of the impact. Classification accuracy has increased for all augmentation and synthetic generation scenarios; however, the increase has not been uniform among the techniques, the sound types, and the percentage of the training set population increase. The average increase in classification accuracy ranged from 2.05% for ResNet50 to 9.05% for VGGish. Our findings reinforce the benefit of audio augmentation and synthetic generation, providing guidelines to avoid accuracy degradation due to overuse and distortion of key audio features. Full article
(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)
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28 pages, 9195 KB  
Article
DAR-MDE: Depth-Attention Refinement for Multi-Scale Monocular Depth Estimation
by Saddam Abdulwahab, Hatem A. Rashwan, Moumen T. El-Melegy and Domenec Puig
J. Sens. Actuator Netw. 2025, 14(5), 90; https://doi.org/10.3390/jsan14050090 - 1 Sep 2025
Viewed by 870
Abstract
Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose [...] Read more.
Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and robustness across diverse environments remains difficult. In this paper, we propose DAR-MDE, a novel framework that combines an autoencoder backbone with a Multi-Scale Feature Aggregation (MSFA) module and a Refining Attention Network (RAN). The MSFA module enables the model to capture geometric details across multiple resolutions, while the RAN enhances depth predictions by attending to structurally important regions guided by depth-feature similarity. We also introduce a multi-scale loss based on curvilinear saliency to improve edge-aware supervision and depth continuity. The proposed model achieves robust and accurate depth estimation across varying object scales, cluttered scenes, and weak-texture regions. We evaluated DAR-MDE on the NYU Depth v2, SUN RGB-D, and Make3D datasets, demonstrating competitive accuracy and real-time inference speeds (19 ms per image) without relying on auxiliary sensors. Our method achieves a δ < 1.25 accuracy of 87.25% and a relative error of 0.113 on NYU Depth v2, outperforming several recent state-of-the-art models. Our approach highlights the potential of lightweight RGB-only depth estimation models for real-world deployment in robotics and scene understanding. Full article
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26 pages, 17668 KB  
Article
Real-Time Damage Detection and Localization on Aerospace Structures Using Graph Neural Networks
by Emiliano Del Priore and Luca Lampani
J. Sens. Actuator Netw. 2025, 14(5), 89; https://doi.org/10.3390/jsan14050089 - 29 Aug 2025
Viewed by 922
Abstract
This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the [...] Read more.
This work presents a novel Graph Neural Network (GNN) based framework for structural damage detection and localization in composite aerospace structures. The sensor network is modeled as a graph whose nodes correspond to the strain measurement points placed on the system, while the edges capture spatial and structural relationships among sensors. Strain mode shapes, extracted via Automated Operational Modal Analysis (AOMA), are used as input features for the GNN. Two architectures are developed: one for binary damage detection and another for damage localization, the latter outputting a spatial probability distribution of damage over the structure. Both networks are trained and validated on synthetic datasets generated from high-fidelity finite element transient simulations performed on a composite wing equipped with 40 strain sensors. The obtained results show strong effectiveness in both detection and localization tasks, thus highlighting the potential of leveraging GNNs for topology-aware Structural Health Monitoring applications. In particular, the proposed framework achieves an AUC of 0.97 for damage detection and a mean localization error of approximately 3% of the wingspan on the synthetic dataset. The performance of the GNN is also compared with a fully connected and a convolutional neural network, demonstrating significant improvements in the localization accuracy. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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15 pages, 4071 KB  
Article
Electrostatic MEMS Phase Shifter for SiN Photonic Integrated Circuits
by Seyedfakhreddin Nabavi, Michaël Ménard and Frederic Nabki
J. Sens. Actuator Netw. 2025, 14(5), 88; https://doi.org/10.3390/jsan14050088 - 29 Aug 2025
Viewed by 2204
Abstract
Optical phase modulation is essential for a wide range of silicon photonic integrated circuits used in communication applications. In this study, an optical phase shifter utilizing photo-elastic effects is proposed, where mechanical stress is induced by electrostatic micro-electro-mechanical systems (MEMS) with actuators arranged [...] Read more.
Optical phase modulation is essential for a wide range of silicon photonic integrated circuits used in communication applications. In this study, an optical phase shifter utilizing photo-elastic effects is proposed, where mechanical stress is induced by electrostatic micro-electro-mechanical systems (MEMS) with actuators arranged in a comb drive configuration. The design incorporates suspended serpentine silicon nitride (SiN) optical waveguides. Through extensive numerical simulations, it is shown that the change in the effective refractive index (neff) of the optical waveguide is a function of the voltage applied to the electrostatic actuators and that such neff tuning can be achieved for a broad range of wavelengths. Implemented within one arm of an unbalanced Mach–Zehnder interferometer (MZI), the phase shifter achieves a phase change of π when the stressed optical path measures 4.7 mm, and the actuators are supplied with 80 V DC and consume almost no power. This results in a half-wave voltage-length product (VπL) of 37.6 V·cm. Comparative analysis with contemporary optical phase shifters highlights the proposed design’s superior power efficiency, compact footprint, and simplified fabrication process, making it a highly efficient component for reconfigurable MEMS-based silicon nitride photonic integrated circuits. Full article
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36 pages, 1564 KB  
Review
Hybrid Path Planning Algorithm for Autonomous Mobile Robots: A Comprehensive Review
by Mithun Shanmugaraja, Mohanraj Thangamuthu and Sivasankar Ganesan
J. Sens. Actuator Netw. 2025, 14(5), 87; https://doi.org/10.3390/jsan14050087 - 28 Aug 2025
Viewed by 1737
Abstract
Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms, have limitations in computational [...] Read more.
Path planning is a complex task in robotics, requiring an efficient and adaptive algorithm to find the shortest path in a dynamic environment. The traditional path planning methods, such as graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms, have limitations in computational efficiency, real-time adaptability, and obstacle avoidance. To address these challenges, hybrid path planning algorithms combine the strengths of multiple techniques to enhance performance. This paper includes a comprehensive review of hybrid approaches based on graph-based algorithms, sampling-based algorithms, reaction-based algorithms, and optimization-based algorithms. Also, this article discusses the advantages and limitations, supported by a comparative evaluation of computational complexity, path optimization, and finding the shortest path in a dynamic environment. Finally, we propose an AI-driven adaptive path planning approach to solve the difficulties. Full article
(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)
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11 pages, 650 KB  
Article
Efficient and Low-Cost Modular Polynomial Multiplier for WSN Security
by Fariha Haroon and Hua Li
J. Sens. Actuator Netw. 2025, 14(5), 86; https://doi.org/10.3390/jsan14050086 - 25 Aug 2025
Viewed by 595
Abstract
Wireless Sensor Network (WSN) technology has constrained computing resources that require efficient and low-cost cryptographic hardware to provide security services, particularly when dealing with large modular polynomial multiplication in cryptography. In this paper, a cost-efficient reconfigurable Karatsuba modular polynomial multiplier is proposed for [...] Read more.
Wireless Sensor Network (WSN) technology has constrained computing resources that require efficient and low-cost cryptographic hardware to provide security services, particularly when dealing with large modular polynomial multiplication in cryptography. In this paper, a cost-efficient reconfigurable Karatsuba modular polynomial multiplier is proposed for general modulus polynomials. The modulus polynomial can be changed easily depending on the application. The proposed modular polynomial multiplier is synthesized and simulated by the AMD Vivado Design Tool. The design’s performance on ADP (Area Delay Product) has been improved compared to previous designs. It can be applied in ECC encryption to speed up the security services in WSN. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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