Journal of Sensor and Actuator Networks

38 pages, 9214 KB

Open AccessArticle

Networked Predictive Control and Intelligent Diagnostics for Automated Mechatronic Manufacturing and Intralogistics Systems

by Sholpan Bekmukhanbetova, Elmira Zhatkanbayeva, Akmaral Sagybekova, Daniyar Mukashev, Meirambay Toilybayev, Tatyana Baratova, Gulbarshyn Smailova, Ayaulym Rakhmatulina and Kalmukhamed Tazhen

J. Sens. Actuator Netw. 2026, 15(4), 51; https://doi.org/10.3390/jsan15040051 (registering DOI) - 29 Jun 2026

Abstract

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The [...] Read more.

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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27 pages, 14867 KB

Open AccessArticle

Discrete Event Modeling, Supervisor Control, and Fault Diagnosis of the Chlorinated Water Station of Delfino Based on the Sensor and Actuator Interaction

by Dimitrios G. Fragkoulis, Fotis N. Koumboulis, Maria P. Tzamtzi, Nikolaos D. Kouvakas, Konstantinos S. Katsiavrias and Klimis K. Katsiavrias

J. Sens. Actuator Netw. 2026, 15(4), 50; https://doi.org/10.3390/jsan15040050 (registering DOI) - 29 Jun 2026

Abstract

A chlorinated water station in Delfino, Greece, was studied from the control and fault diagnosis point of view, using the interaction of the devices installed to the station as well as rules resulting from the physical characteristics of the station. The DES models [...] Read more.

A chlorinated water station in Delfino, Greece, was studied from the control and fault diagnosis point of view, using the interaction of the devices installed to the station as well as rules resulting from the physical characteristics of the station. The DES models of the station’s devices (pumps, level sensors, flow sensors, and pressure sensors) are presented. The models of the pumps include both the activation/deactivation functionality and the regulation of the output flow of the pump. The models of the devices were validated using field data extracted from the monitoring system of the station. Towards protecting the pump from dry running and the tanks from overflow, a set of safety requirements were realized in the form of supervisor automata. Using field data, the effect of the supervisors in the activation/deactivation of the pumps was tested. A modular fault diagnosis system, where the number of fault diagnosers is equal to the number of pumps, was implemented to diagnose the faulty case of pump having stuck open despite deactivation command. A fault diagnosis system for a flow sensor of the station was developed and tested using the field data of the sensors and the pumping system. Supervisors and diagnosers were tested using one-week field data. The structured language code for PLC implementation of the diagnosers is presented. Full article

(This article belongs to the Topic Fault Diagnosis and System Health Intelligent Management)

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16 pages, 2550 KB

Open AccessArticle

Enhancing Robustness to Device Heterogeneity in WiFi-Based Indoor Localization

by Adrián García, Jorge Beltrán, Noelia Hernández, Ignacio Parra and Euntai Kim

J. Sens. Actuator Netw. 2026, 15(4), 49; https://doi.org/10.3390/jsan15040049 (registering DOI) - 27 Jun 2026

Abstract

Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless [...] Read more.

Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless of the device used. Recent research has addressed this issue of device heterogeneity by building datasets that include data from a diverse set of devices. In this paper, we tackle this challenge by presenting a novel, multi-device, WiFi Received Signal Strength dataset collected along unconstrained trajectories using nine Android devices over a three-month period with precise ground truth positions obtained using Simultaneous Localization And Mapping. We then study the effect of heterogeneity in the localization performance using an LSTM-based neural network that leverages the temporal nature of sequential WiFi scans, and introduce two mitigation strategies: per-device Received Signal Strength normalization and the incorporation of temporal features as additional input. Our results show that these methods significantly improve cross-device performance with a mean average localization error reduction of 56% and enable generalization to previously unseen hardware with a mean average localization error 8% higher for the unseen devices. Full article

(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)

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40 pages, 27259 KB

Open AccessArticle

Monocular 3D Position Estimation of a Moving Vehicle Based on a Kalman-Goldschmidt Adaptive Filter

by Diana Kalita, Pavel Lyakhov, Valery Andreev and Denis Butusov

J. Sens. Actuator Netw. 2026, 15(3), 48; https://doi.org/10.3390/jsan15030048 - 18 Jun 2026

Abstract

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, [...] Read more.

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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22 pages, 1357 KB

Open AccessArticle

A Closed-Form Cooperative Avoidance Control for Multiple m-DOF Manipulators

by Wenxue Zhang, Ziyi Ma, Ning Zong and Dušan M. Stipanović

J. Sens. Actuator Netw. 2026, 15(3), 47; https://doi.org/10.3390/jsan15030047 - 18 Jun 2026

Abstract

Multi-manipulator cooperative systems are widely deployed in industrial assembly, intelligent manufacturing and other fields, but collision safety and efficient motion coordination during coordinated operation remain key challenges. In this paper, a novel cooperative control strategy based on relative velocity information is derived to [...] Read more.

Multi-manipulator cooperative systems are widely deployed in industrial assembly, intelligent manufacturing and other fields, but collision safety and efficient motion coordination during coordinated operation remain key challenges. In this paper, a novel cooperative control strategy based on relative velocity information is derived to guarantee collision-free maneuvers for multiple m-degree-of-freedom (m-DOF) manipulator systems with general Lagrangian dynamics. One key advantage is that it ensures reliable safety while achieving smoother avoidance maneuvers, reduced interference with objective tasks, lower energy consumption, and improved task efficiency; notably, the avoidance control depends not only on the relative distance between manipulators but also on their relative motion, making it less conservative as manipulators avoid unnecessary spreading during collision avoidance. Another is that it integrates collision avoidance, disturbance attenuation, and deadlock elimination into a unified closed-form control law, which yields a closed-form solution and is easy to implement in engineering practice. Theoretically, this paper adopts the generalized Lyapunov stability theory to rigorously prove the asymptotic convergence and persistent collision-free property. Finally, simulation results on a dual two-DOF manipulator system further verify the effectiveness and reliability of the proposed control strategy. Full article

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25 pages, 1799 KB

Open AccessArticle

Self-Supervised Transfer Learning for IMU-Based Upper-Limb Action Detection and Motion Quality Analysis in an Immersive VR Functional Task

by Zhao Liu, Daniele Soria, Chee Siang Ang and Sukhi Shergill

J. Sens. Actuator Netw. 2026, 15(3), 46; https://doi.org/10.3390/jsan15030046 - 12 Jun 2026

Abstract

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as [...] Read more.

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of

0.712 \pm 0.128

and RightHand–ACC achieving

0.679 \pm 0.118

. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost (

r = 0.636

,

p < 0.001

). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

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23 pages, 7293 KB

Open AccessArticle

Multi-Modal Data Processing in Digital Twins: Connecting Sensors and Actuators for Health Optimisation

by Alexandru-George Berciu, Dan Doru Micu and Eva-Henrietta Dulf

J. Sens. Actuator Netw. 2026, 15(3), 45; https://doi.org/10.3390/jsan15030045 - 10 Jun 2026

Abstract

The continuous monitoring of population health is a major focus in scientific literature, with numerous studies highlighting the critical role of sleep. However, to the best of the authors’ knowledge, the multi-modal data processing required to fully map the tripartite relationship between environmental [...] Read more.

The continuous monitoring of population health is a major focus in scientific literature, with numerous studies highlighting the critical role of sleep. However, to the best of the authors’ knowledge, the multi-modal data processing required to fully map the tripartite relationship between environmental stimuli, sleep, and health has not been achieved. This paper proposes a comprehensive data fusion strategy, integrating public databases to extract common features from historical sensor data. The present paper proposes a robust processing architecture by training four classes of algorithms (mathematical, machine learning, artificial intelligence, and ensemble models) to analyse how environmental inputs impact sleep quality and, consequently, physiological health. The resulting state-of-the-art model, a multi-modal architecture comprising 10 integrated models, was tested on a massive combined dataset of 139,950 rows and 8249 columns. The model achieved an R-squared of 0.958, demonstrating superior data processing and predictive accuracy. Alongside the integrated dataset, this research establishes the computational groundwork for human-centric Digital Twins, paving the way for closed-loop IoT environments where sensor-driven analytics inform automated actuator interventions to improve sleep and health. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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65 pages, 14780 KB

Open AccessReview

Computational Architectures for 6G Networks: Integrating Distributed Computing and Edge Artificial Intelligence

by Evelio Astaiza Hoyos, Héctor Fabio Bermúdez-Orozco and Nasly Cristina Rodríguez-Idrobo

J. Sens. Actuator Netw. 2026, 15(3), 44; https://doi.org/10.3390/jsan15030044 - 5 Jun 2026

Abstract

This paper investigates the integration of distributed computing and edge Artificial Intelligence (edge AI) as foundational enablers of sixth-generation (6G) mobile networks. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing over 200 peer-reviewed papers, [...] Read more.

This paper investigates the integration of distributed computing and edge Artificial Intelligence (edge AI) as foundational enablers of sixth-generation (6G) mobile networks. Through a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, encompassing over 200 peer-reviewed papers, architectural proposals, and standardization documents retrieved from IEEE Xplore, Scopus, Web of Science, MDPI, arXiv, ITU-R, 3GPP, and ETSI, this study provides a structured computational analysis of architectural approaches that integrate distributed computing paradigms and edge AI as core enablers of 6G. The analysis examines the evolution from cloud-centric to edge-centric computing, key edge AI techniques—including Federated Learning (FL), Split Learning (SL), and edge-adapted Large AI Models (LAMs)—and their role in enabling intelligent orchestration, resource optimization, and context-aware services. The comparative analysis demonstrates that edge computing architectures reduce end-to-end latency by 85–95% relative to cloud-centric deployments (under conditions of MEC servers within 1 km and 5G NR fronthaul), while federated learning with gradient compression achieves communication overhead reductions of up to 99% under IID data distributions and stable channel conditions. The results indicate that the tight integration of distributed computing and edge AI enhances network responsiveness, scalability, and adaptability, while also revealing persistent challenges related to orchestration complexity, resource constraints, security, and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realization of 6G networks and for guiding future research and standardization efforts. Full article

(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)

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25 pages, 5196 KB

Open AccessArticle

Automatic Fault Detection and Prediction of AGV Magnetic Track Using Machine Learning and Computer Vision

by Jules Bekoka Botomba, Akhlaqur Rahman, Daniel T. H. Lai and Vishal Sharma

J. Sens. Actuator Netw. 2026, 15(3), 43; https://doi.org/10.3390/jsan15030043 - 27 May 2026

Abstract

The rise of Industry 4.0 has accelerated the adoption of intelligent automation in high-throughput manufacturing environments. Automated guided vehicles (AGVs) rely heavily on magnetic guidance tracks, which are susceptible to wear, contamination, and structural degradation. These defects frequently cause AGV misalignment, emergency stops, [...] Read more.

The rise of Industry 4.0 has accelerated the adoption of intelligent automation in high-throughput manufacturing environments. Automated guided vehicles (AGVs) rely heavily on magnetic guidance tracks, which are susceptible to wear, contamination, and structural degradation. These defects frequently cause AGV misalignment, emergency stops, and production downtime. This paper presents a lightweight, embedded, vision-based framework for real-time monitoring of AGV magnetic tracks using Raspberry Pi 4 cameras and Python-based computer vision algorithms. The system integrates grayscale intensity modeling, histogram-based MeanShift tracking, contour continuity analysis, and machine learning-assisted classification to detect missing segments, wear, and foreign object interference. Experimental validation on a 30 m test track and five years of industrial data (>3000 samples) demonstrate robust tracking, reliable anomaly detection, and zero false positives under nominal conditions. The proposed hybrid deterministic, ML architecture supports predictive maintenance, reduces downtime risk, and contributes to resilient Industry 4.0 material-handling systems. Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

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17 pages, 3402 KB

Open AccessArticle

A Near-Field Communication (NFC) Multi-Sensor Node with Optimized Read Range and Adaptive Power Management for Remote Monitoring

by Rishin Patra, Hilary Scott Nkimbeng Cho and Jin W. Choi

J. Sens. Actuator Netw. 2026, 15(3), 42; https://doi.org/10.3390/jsan15030042 - 26 May 2026

Abstract

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design [...] Read more.

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned design performs on-demand data acquisition, logs measurements in the FRAM, and communicates the measured results through an ISO15693 compliant NFC link while powered entirely by the reader’s radio-frequency (RF) field. Since NFC provides only limited harvested power, efficient energy management is critical. The proposed scheme continuously monitors the storage capacitor voltage and activates each sensor only when sufficient energy is available. After every measurement, the system reassesses the stored charge before triggering the next acquisition, ensuring stable multi-sensor operation. A BMP390 temperature and pressure sensor and the on-chip temperature sensor demonstrate the platform’s capability. Experimental results show that the system harvests 1.064 mW (1.85 V, 560 µA), achieves a wireless operating range of up to 40 mm, and delivers a response time of 800 ms, demonstrating its suitability for low-power temperature and pressure sensing applications. Full article

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28 pages, 2970 KB

Open AccessArticle

UGV Path Optimization in UAV-Assisted Environments Using Visibility-Aware Path Simplification

by Isuru Munasinghe, Asanka Perera, Sreenatha Anavatti and Matt Garratt

J. Sens. Actuator Netw. 2026, 15(3), 41; https://doi.org/10.3390/jsan15030041 - 22 May 2026

Abstract

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is [...] Read more.

This study proposes a modular path optimization framework for uncrewed ground vehicles (UGVs) in uncrewed aerial vehicle (UAV)-assisted navigation environments to improve the efficiency, smoothness, and executability of paths generated by classical grid-based path planning algorithms. The principal innovation of this work is the Visibility and Line-of-Sight Path Simplification (VLoSPS) algorithm, an algorithm-independent post-processing method that removes redundant waypoints through long-range axis-aligned visibility analysis while preserving path feasibility. VLoSPS is integrated with the Direction-Aware Path Planning Approach (DAPPA) to reduce angular deviations and improve directional continuity. The proposed framework is applicable to standard algorithms, including A*, Dijkstra, Breadth-First Search (BFS), and Depth-First Search (DFS), without modifying their internal search mechanisms. The main academic contributions comprise the formulation of a generalized post-processing architecture for UAV-derived occupancy maps, the introduction of a visibility-aware waypoint reduction strategy, and extensive validation using two synthetic maze datasets and three UAV-derived semantically segmented real-world datasets. On the Göttingen Maze Dataset, the VLoSPS and DAPPA pipeline reduced the average path lengths of A*, Dijkstra, BFS, and DFS by 5.42%, 9.46%, 10.44%, and 86.00%, respectively. The consistent improvements across real-world datasets demonstrate the effectiveness, computational feasibility, and general applicability of the proposed framework for UAV-assisted UGV path planning. The implementation code and benchmark resources developed in this study are publicly released to promote reproducibility and facilitate future research. Full article

(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)

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34 pages, 14603 KB

Open AccessArticle

A Benchmark for Image Forgery Detection and Localization on Social Media Images

by Md. Mehedi Rahman Rana, Md. Anisur Rahman, Kamrul Hasan Talukder, Syed Md. Galib and Nazmul Siddique

J. Sens. Actuator Netw. 2026, 15(3), 40; https://doi.org/10.3390/jsan15030040 - 19 May 2026

Abstract

The widespread manipulation of digital images on social media has significantly undermined public trust in visual content and created major challenges for automated forgery detection. These challenges are further intensified by platform-induced degradations such as compression, resizing, and filtering, which often obscure forensic [...] Read more.

The widespread manipulation of digital images on social media has significantly undermined public trust in visual content and created major challenges for automated forgery detection. These challenges are further intensified by platform-induced degradations such as compression, resizing, and filtering, which often obscure forensic traces. This work develops FIDD-6000, a large-scale benchmark dataset for image forgery detection and localization, containing 6000 social media images, including 1000 authentic and 5000 manipulated samples, with pixel-level ground-truth masks annotated across three forgery categories, splicing, copy-move, and retouching, all created under realistic post-processing conditions. Each manipulated image is accompanied by a pixel-level ground-truth mask indicating the tampered regions. To assess the challenges posed by social media-based image manipulation, we evaluate 15 state-of-the-art image forgery localization methods on FIDD-6000, including approaches based on JPEG compression artifacts, sensor-noise analysis, and error level analysis. Experimental results show that these methods perform poorly on the proposed dataset, revealing their limited effectiveness in detecting forged images that have undergone social media-specific compression and transformation. This performance gap highlights the need for more robust and advanced machine learning and deep learning approaches capable of handling the complexity of modern image manipulations. Therefore, FIDD-6000 provides a valuable resource for researchers by offering a rigorous benchmark for developing, evaluating, and comparing next-generation forgery detection and localization methods. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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26 pages, 7091 KB

Open AccessArticle

Evaluation of the Effectiveness of Distributed Antenna Systems for Improving Indoor Wireless Network Coverage

by Kyrmyzy Taissariyeva, Zhuldyz Kalpeyeva, Yerlan Tashtay, Yermek Bekenov and Zhansaya Ayapbergen

J. Sens. Actuator Netw. 2026, 15(3), 39; https://doi.org/10.3390/jsan15030039 - 18 May 2026

Abstract

A pressing challenge of modern wireless networks is ensuring stable radio coverage inside buildings, where radio signal propagation is significantly complicated by the influence of building structures. Reinforced concrete walls, floor slabs, internal partitions, and energy-efficient windows with metallized coatings create substantial obstacles [...] Read more.

A pressing challenge of modern wireless networks is ensuring stable radio coverage inside buildings, where radio signal propagation is significantly complicated by the influence of building structures. Reinforced concrete walls, floor slabs, internal partitions, and energy-efficient windows with metallized coatings create substantial obstacles to the propagation of electromagnetic waves, causing reflection, absorption, and scattering. As a result, areas with weakened coverage are formed inside buildings, leading to deterioration in mobile communication quality and reduced data transmission rates. This study presents an experimental investigation of the received signal strength of mobile operators inside a multi-storey residential complex. An analysis was conducted to evaluate the impact of building height, architectural features, and construction materials on radio signal propagation. In addition, the frequency bands used in 4G LTE and 5G networks by mobile operators were examined. It was found that LTE networks mainly operate in the 1.8–2.1 GHz frequency range, whereas 5G networks operate in the n77 band (3.6–3.7 GHz), which provides higher data throughput but is characterized by greater signal attenuation when propagating inside buildings. To address this issue, a Distributed Antenna System (DAS) based on GPON technology was implemented in the studied building. The placement of antenna equipment on the roof enabled the efficient reception of the signal from the base station and its subsequent distribution inside the building through an internal antenna network. The measurement results demonstrated that the deployment of a GPON-based DAS significantly improves the received signal level and ensures more uniform radio coverage inside indoor environments. The obtained results confirm that the use of distributed antenna systems is an effective solution for compensating signal losses caused by the shielding effect of building structures and can significantly improve the quality of mobile communications in dense urban environments. The results show that the RSRP level in indoor environments without DAS decreases to approximately −100 to −110 dBm, while after deployment of the GPON-based DAS, it improves to −45 to −75 dBm. This corresponds to a signal gain of up to 40–50 dB, ensuring stable connectivity and significantly improved data transmission performance. Full article

(This article belongs to the Section Communications and Networking)

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29 pages, 1927 KB

Open AccessReview

Fiber Bragg Grating-Based Deformation Monitoring in Space Infrastructure: A Comprehensive Review

by Nurzhigit Smailov, Sauletbek Koshkinbayev, Kydyrali Yssyraiyl, Ainur Kuttybayeva, Gulbahar Yussupova, Askhat Batyrgaliyev and Akezhan Sabibolda

J. Sens. Actuator Netw. 2026, 15(3), 38; https://doi.org/10.3390/jsan15030038 - 15 May 2026

Abstract

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on [...] Read more.

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

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25 pages, 3457 KB

Open AccessArticle

Nonlinear Dynamics and Energy Harvesting Characteristics of Asymmetric Tristable Systems with an Elastic Magnifier

by Devarajan Kaliyannan, Kadhiravan M J, Shree Vignesh Khumar Alampalayam Tamilselvan, Kughan S A, Hari Krishnan Babu and Mohanraj Thangamuthu

J. Sens. Actuator Netw. 2026, 15(3), 37; https://doi.org/10.3390/jsan15030037 - 12 May 2026

Abstract

Vibration energy harvesting has emerged as a sustainable solution for powering low-energy devices such as wireless sensors and wearable electronics. However, conventional vibration energy harvesters often suffer from narrow operational bandwidth and limited output performance under ultra-low excitation conditions. To overcome these limitations, [...] Read more.

Vibration energy harvesting has emerged as a sustainable solution for powering low-energy devices such as wireless sensors and wearable electronics. However, conventional vibration energy harvesters often suffer from narrow operational bandwidth and limited output performance under ultra-low excitation conditions. To overcome these limitations, this study proposes an asymmetric tristable vibration energy harvester integrated with an elastic magnifier (EM), hereafter referred to as the asymmetric TVEH with EM, to enhance energy conversion efficiency under weak excitation. A nonlinear two-degree-of-freedom electromechanical model is developed to describe the coupled dynamics between the cantilever beam and the EM, incorporating nonlinear restoring forces and electromechanical coupling effects. The system performance is investigated using the harmonic balance method (HBM) and time-domain numerical simulations. In addition, parametric studies are conducted to examine the influence of the EM mass and stiffness ratios on the dynamic response and energy harvesting performance. The numerical results demonstrate that the inclusion of the EM significantly amplifies the system response under ultra-low excitation

(f = 0.055)

, enabling improved inter-well motion and enhancing energy conversion efficiency by up to 45%. To validate the analytical and numerical findings, an experimental prototype is fabricated and tested. The experimental results confirm the effectiveness of the proposed design, achieving a root mean square voltage of

V_{rms} = 5 V

across a load resistance of

R_{L} = 100 k Ω

under a base acceleration of

1.4 {m / s}^{2}

at 14 Hz, measured over a 30 s window with a low-pass filter cut-off frequency of 100 Hz. The proposed asymmetric TVEH with EM consistently outperforms both the symmetric TVEH with EM and the asymmetric configuration without EM. Overall, the results highlight the pivotal role of the elastic magnifier in enhancing the dynamic response and harvesting performance under weak excitations, demonstrating strong potential for powering low-power electronic devices in practical applications. Furthermore, this work supports the United Nations Sustainable Development Goal SDG 7 (Affordable and Clean Energy) by promoting decentralized and renewable vibration-based energy harvesting technologies. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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16 pages, 540 KB

Open AccessArticle

Utilizing AoA for Decision Gathering in Optical Wireless Sensor Networks

by Abdullah Alhasanat, Ahed Aleid, Abdelrahman Abushattal, Amal Alhasanat and Umar Raza

J. Sens. Actuator Netw. 2026, 15(3), 36; https://doi.org/10.3390/jsan15030036 - 8 May 2026

Abstract

Optical Wireless Sensor Networks (OWSNs) have emerged as a promising solution for energy-efficient and secure data collection in free-space optical (FSO) environments. A key challenge in such networks is minimizing the decision error rate (DER) during decision aggregation at the central entity (CE). [...] Read more.

Optical Wireless Sensor Networks (OWSNs) have emerged as a promising solution for energy-efficient and secure data collection in free-space optical (FSO) environments. A key challenge in such networks is minimizing the decision error rate (DER) during decision aggregation at the central entity (CE). Building on earlier Time-Difference-of-Arrival (TDoA) reporting methods, this paper introduces an Angle-of-Arrival (AoA) framework for decision gathering. In the proposed scheme, sensor nodes equipped with Corner Cube Retro-reflectors (CCRs) passively communicate their local decisions, while the CE identifies such decisions based on AoA estimation. A closed-form expression for the DER is derived, incorporating false-alarm and missed-detection probabilities, and is validated through Monte Carlo simulations. Comparative evaluation against TDoA, Single Wavelength Parallel (SWP), and Multiple Wavelength Series (MWS) schemes shows that the AoA-based approach achieves consistently lower DERs, particularly in high-SNR regimes and larger node counts, closely approaching the theoretical lower bound. These results highlight AoA as a practical and scalable alternative to conventional decision-gathering methods in OWSNs. Full article

(This article belongs to the Section Communications and Networking)

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23 pages, 3889 KB

Open AccessArticle

Clinical Correlation and Postoperative Findings of Thigh-Based Electrocardiography in Aortic Stenosis

by Aline dos Santos Silva, Miguel Velhote Correia, Andreia Gonçalves da Costa, Rui J. Cerqueira and Hugo Plácido da Silva

J. Sens. Actuator Netw. 2026, 15(3), 35; https://doi.org/10.3390/jsan15030035 - 28 Apr 2026

Abstract

Previous studies on healthy controls suggest the added value of thigh-based Electrocardiography (ECG), which collects data using sensors embedded in a toilet seat for unobtrusive signal acquisition. However, further evidence regarding its clinical feasibility is needed; with this work, we investigated three complementary [...] Read more.

Previous studies on healthy controls suggest the added value of thigh-based Electrocardiography (ECG), which collects data using sensors embedded in a toilet seat for unobtrusive signal acquisition. However, further evidence regarding its clinical feasibility is needed; with this work, we investigated three complementary aspects: signal quality, morphological correlation with standard ECG leads, and the system’s potential for heart rate variability (HRV) analysis in patients undergoing aortic valve replacement. This work was divided into two main phases. In the first, 32 healthy volunteers underwent simultaneous ECG recordings using both a standard 12-lead ECG system and the thigh-based system. Signal Quality Index (SQI) analysis revealed that 56.25% of the experimental signals were classified as excellent, and over 62.5% of recordings showed a strong correlation with Lead I of the clinical ECG. These findings extend the state of the art by further characterising the quality and relevance of the captured signals. In the second phase, two patients with severe aortic stenosis were monitored before and after surgical valve replacement. HRV metrics derived from the thigh-based ECG captured distinct autonomic responses: one patient showed significant postoperative improvement in global and parasympathetic modulation (increased SDNN, RMSSD, and Sample Entropy), while the other exhibited reduced variability and complexity, potentially indicating impaired autonomic recovery. These results highlight the feasibility of thigh-based ECG data acquisition for passive, longitudinal cardiac health monitoring in everyday environments and its applicability for pre- and postoperative autonomic assessment. Full article

(This article belongs to the Section Actuators, Sensors and Devices)

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16 pages, 2729 KB

Open AccessArticle

Introducing the Slowloris E-DoS Attack: A Threat Arising from Vulnerabilities in the FTP and SSH Protocols

by Nikola Gavric, Guru Bhandari and Andrii Shalaginov

J. Sens. Actuator Netw. 2026, 15(2), 34; https://doi.org/10.3390/jsan15020034 - 17 Apr 2026

Abstract

Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols [...] Read more.

Slowloris is a well-known application-layer Denial of Service (DoS) attack that is challenging to detect due to its low-rate nature, allowing it to blend with legitimate traffic and remain unnoticed. Our hypothesis is that deliberate prolongation of the pre-authentication stage in stateful protocols induces unnecessary CPU utilization. In this study, we repurpose Slowloris as an energy-oriented (E-DoS) attack that exploits pre-authentication statefulness of the most prevalent remote access protocols, the Secure Shell Protocol (SSH) and File Transfer Protocol (FTP). We employ a Raspberry Pi-based experimental setup with different software implementations of the mentioned protocols to validate our hypothesis. Our experiments confirm the susceptibility of SSH and FTP to Slowloris E-DoS attacks, and we quantify the consequential impact on power consumption. We find that the Slowloris E-DoS attack exhibits an asymmetrical nature, causing a disproportionate computational demand on victim systems compared to the resources invested by the attacker. The results of this study indicate that battery-powered single-board computers (SBCs) are critically affected by these attacks due to their limited power availability. This research demonstrates the importance of understanding and mitigating Slowloris E-DoS vulnerabilities in the SSH and FTP protocols, offering valuable insights for enhancing security measures. Our findings show that millions of SBCs worldwide may be at risk and highlight a deeper structural weakness: the stateful design of widely deployed protocols can turn service availability into an energy liability. This systemic risk extends beyond SSH and FTP, with implications for IoT devices and backends that depend on stateful communication protocols. Full article

(This article belongs to the Special Issue Feature Papers in the Section of Network Security and Privacy)

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25 pages, 5736 KB

Open AccessArticle

Photogrammetry–Polarimetry Fusion for 3D Structural Edge Extraction and Physics-Guided Classification

by Mohammad Saadatseresht, Hossein Arefi and Fatemeh Torkamandi

J. Sens. Actuator Netw. 2026, 15(2), 33; https://doi.org/10.3390/jsan15020033 - 16 Apr 2026

Abstract

The accurate interpretation of structural edges requires distinguishing geometry-driven discontinuities from reflectance- and illumination-induced variations. Conventional photogrammetric pipelines rely primarily on radiometric and geometric cues, which often lack physical interpretability under complex material and lighting conditions. This study proposes a photogrammetry–polarimetry fusion framework [...] Read more.

The accurate interpretation of structural edges requires distinguishing geometry-driven discontinuities from reflectance- and illumination-induced variations. Conventional photogrammetric pipelines rely primarily on radiometric and geometric cues, which often lack physical interpretability under complex material and lighting conditions. This study proposes a photogrammetry–polarimetry fusion framework for physics-guided semantic classification of 3D structural edges. Radiometric, geometric, and polarimetric features are integrated within a noise-normalized representation to enable modality-independent interpretation. A rule-based classification scheme is introduced to assign edges to physically meaningful categories, including geometric, material, specular, illumination, and polarization-driven phenomena. The method is evaluated on a calibrated geometric object and a cultural heritage statue. Results show that polarization provides complementary information that reduces ambiguity between geometry-driven and reflectance-driven edge responses while preserving the underlying reconstructed geometry. On the calibrated dataset, edge detection achieves 88.4% precision, 95.5% recall, and an F1-score of approximately 0.92. Multi-view integration further improves the completeness of geometry-dominant 3D edges. The proposed framework introduces a physics-guided semantic sensing layer for multi-modal 3D perception, enabling more robust and interpretable structural analysis in photogrammetric workflows. Full article

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