Hydrogel-and-nanomaterial-integrated wearable biosensor networks are promising platforms for real-time biomedical monitoring because they combine soft biointerfaces, sensitive signal transduction, and wireless data connectivity. Hydrogels provide tissue-like softness, hydration, adhesion, permeability, and biocompatibility, whereas nanomaterials such as graphene, carbon nanotubes, MXenes, metallic nanoparticles, and conductive polymers enhance conductivity, electrochemical activity, optical responsiveness, mechanical durability, and signal amplification. This review summarizes recent advances in hydrogel-and-nanomaterial-integrated wearable biosensors, ranging from soft material interfaces and stand-alone sensing devices to wireless wearable nodes, IoT-connected platforms, and emerging closed-loop sensor–actuator systems. Because these platforms differ substantially in their level of integration and validation, this review distinguishes enabling material and device concepts from fully connected or closed-loop systems. The distinctive contribution of this review is a materials-to-systems, evidence-graded framework that links hydrogel and nanomaterial interface design with sensing mechanisms, wearable sensor-node integration, wireless and IoT connectivity, and closed-loop actuation while distinguishing device-level proof of concept from clinically validated performance. We discuss functional hydrogel design, nanomaterial-based conductive networks, hybrid hydrogel–nanomaterial structures, and key requirements for skin compatibility, adhesion, stretchability, and long-term stability. Major sensing mechanisms and biomedical targets are reviewed, including electrochemical and optical biosensing, mechanical and physiological signal sensing, and sweat biomarker monitoring. We further highlight system-level integration strategies involving wearable sensor nodes, wireless communication, smartphone and cloud connectivity, data processing, power management, security, and reliability. Representative biomedical applications are summarized, including sweat-based metabolic monitoring, smart wound monitoring, hydrogel-based wound dressings, cardiovascular and respiratory monitoring, and motion sensing. Finally, current technical and translational challenges are discussed with emphasis on the distinction between analytical sensing performance, physiological correlation, and clinical validation. Disease-management and closed-loop healthcare applications are discussed as emerging directions that require appropriate human studies, reference-method comparison, agreement analysis, long-term monitoring, and safety validation before clinical implementation.
In the industrial edge-to-cloud continuum, data is often privacy-sensitive and spans multiple organizations that do not fully trust one another, making central aggregation of raw data undesirable and often non-compliant with regulations such as the General Data Protection Regulation (GDPR). Federated Learning (FL) addresses this by sharing model updates rather than raw data, but conventional FL assumes a central coordinator, leaving it exposed to poisoning and inference attacks and to a single point of trust and failure. Decentralized Federated Learning (DFL) couples FL with Distributed Ledger Technologies (DLTs), removing the coordinator and enabling verifiable aggregation in trustless, cross-organizational environments. In this work, we assess the applicability of DFL to on-device spoken-command recognition—a representative edge audio task underpinning voice-driven industrial interfaces—by comparing decentralized and centralized training under idealized and adversarial conditions. Using a Convolutional Neural Network (CNN) replicated across edge nodes, we evaluate resilience to inter-node data imbalance, to poisoning attacks, and to a privacy-preserving noise-injection defense against inference attacks, together with model compression for resource-constrained edge devices. The system pairs this comparison with a validation-based poisoning defense in which each node scores its peers’ updates on its own held-out data, and an update is aggregated only if a majority of nodes report a weighted F1-score above a threshold—requiring neither a shared validation set nor a trusted validator. Our results indicate that the DFL system achieves accuracy comparable to centralized baselines in most scenarios (weighted F1-score 0.762 across nine nodes, against 0.896 centralized), and that a cross-node validation mechanism reliably excludes poisoned updates as long as fewer than half of the nodes are compromised (within 3.54% of the unpoisoned model). Noise-based inference defenses reduce accuracy substantially (44.7% on average at a noise standard deviation of 1.0), exposing a sharp privacy–utility trade-off, whereas model compression preserves performance (0.765 against 0.762 for pruning and format conversion, with 8-bit quantization costing up to a further 13.3%). These findings clarify both the promise and the current limitations of decentralized, privacy-preserving learning for the industrial edge-to-cloud continuum.
This paper proposes a priority-aware EP-ALOHA framework with predictive radio resource allocation for heterogeneous machine-to-machine (M2M) devices in 5G massive machine-type communication (mMTC) networks. The proposed framework extends conventional EP-ALOHA by introducing M2M priority classes, priority-dependent delay constraints, and Exploration Phase resource block (RB) allocation. The RB-allocation problem is formulated as an integer-constrained optimization problem, where the objective is to improve effective radio channel utilization while satisfying delay constraints for different priority classes. A Genetic Algorithm-based optimization procedure is used to generate optimization-derived RB-allocation targets under different traffic and system parameter settings. These targets are then used to train and evaluate predictive RB-allocation models, including Random Forest, Neural Network, Gradient Boosting, and Linear Regression. The simulation results show that the proposed priority-aware EP-ALOHA method achieves a higher successful access probability than the considered baseline schemes within the feasible operating region. For predictive RB allocation, the Neural Network achieved the best test-set performance, with MSE = 25.5002, RMSE = 5.0498 RBs, MAE = 3.2629 RBs, and R2 = 0.9810. A separate computational evaluation showed that Random Forest inference reduced the mean allocation-decision time from 213.54 ms for GA-based optimization to 15.20 ms, corresponding to a 14.05-fold speed-up on the evaluated platform. In addition, M2M device activity probability forecasting is evaluated using Bayesian estimation, LSTM, moving average, and exponential smoothing. LSTM achieves the lowest forecasting error, while exponential smoothing provides a close and computationally simpler alternative. The results indicate that the proposed framework can support proactive and priority-aware resource management for heterogeneous M2M traffic, while the learning-based components are used as approximation and forecasting tools rather than as universally superior solutions.
This paper presents the results of a low-cost environmental testing campaign performed on commercial off-the-shelf components intended for aerospace applications, specifically a housekeeping system designed for suborbital launchers. These tests encompass a broader range of thermal and mechanical testing procedures than is typically reported in the literature, providing a more comprehensive assessment of the system’s robustness. The housekeeping system is subjected to sine-equivalent dynamic loads representative of launch environments expected by vehicles such as Ariane 6, VEGA, and Falcon 9 using a shaker. In addition, thermal vacuum testing is conducted to evaluate system performance under temperature and pressure conditions representative of high-altitude flight. Following each test, the system’s functionality is assessed by comparing its performance against baseline laboratory conditions using telemetry data acquired by the system; most importantly, a critical failure on telemetry data acquisition is verified, which determines the survivability of the system. The successful completion of these environmental tests demonstrates the survivability of the housekeeping system, validating its reliability and suitability for operation in suborbital launcher missions.
Trust Management Systems (TMSs) have recently emerged as a behavioural complement to identity-based approaches in Industrial IoT (IIoT) cybersecurity by evaluating node trustworthiness. Deep Learning (DL)-based TMSs offer favourable detection over heuristic and Machine Learning (ML) models. The computational density of DL models introduces a trade-off between inference fidelity and deployment feasibility, particularly in Edge-Fog-Cloud (EFC) IIoT architectures where latency and resources are constrained. This work proposes an EFC architectural framework that relocates DL inference to the Fog layer, reducing Cloud communication latency and Edge resource exhaustion. A lightweight Long Short-Term Memory (LSTM)-based model derives continuous trust scores from header-derived, flow-aggregated features, with inference latency bounded through fixed-size sliding windows and stateless execution. The system is trained and evaluated on CIC-IoT-2023 across Denial-of-Service (DoS), Distributed DoS (DDoS), Mirai, and benign scenarios. System scalability is assessed through ns-3 network simulation under benign conditions, with full-system behaviour further evaluated under benign, DoS, and Mirai scenarios. Offline evaluation achieves F1-score 0.9996, accuracy 0.9997, ROC-AUC 0.9999, and PR-AUC 0.9997. Architectural evaluation yields a mean inference latency of 0.049 ms, a maximum enforcement latency of 0.120 ms, and a 302 kB deployment footprint. System simulation confirms a benign False Positive Rate (FPR) 0.07% and a maximum detection latency of 0.22 ms. DoS achieves recall 0.99999 and FPR 0.00186, and Mirai achieves recall 0.99997 with FPR 0. This demonstrates that DL-based trust inference is achievable on resource-constrained Fog nodes, establishing the work as a viable solution for trust evaluation in EFC IIoT deployments.
In DC microgrid (DC-MG) clusters, distributed generation units rely on electronic communication networks to exchange voltage measurements, current information, and coordination signals for voltage recovery and current sharing. As the degree of system clustering and communication coupling increases, nodal denial-of-service (DoS) attacks may interrupt the information exchange of leaders and followers, resulting in communication topology switching and degraded cooperative control performance. Accordingly, this paper proposes an event-triggered (ET) resilient control scheme with high communication efficiency for networked DC-MG clusters under nodal DoS attacks. First, a distributed secondary control model with a cross-layer communication mechanism is constructed in accordance with the requirements of the system’s overall power distribution, which incorporates the two-layer node architecture of leaders and followers in DC-MG clusters. Second, a statistical multimode nodal DoS attack model is developed to characterize heterogeneous communication interruptions through topology-dependent attack modes and their occurrence probabilities. Finally, an exponential threshold ET mechanism based on bus-voltage recovery errors is designed within the distributed secondary control framework to reduce redundant information transmission while preserving resilience against nodal communication attacks. Simulation results demonstrate that the proposed method can maintain accurate voltage recovery and current sharing in networked DC-MG clusters under large-scale DoS attacks, while improving communication efficiency through ET updates.
This paper introduces the Sensor Traffic Generator (STGen), a lightweight, pure-software testbed for evaluating IoT application- and transport-layer protocols at scale. Relative to existing software-based IoT evaluation platforms, STGen combines three design decisions that, to the best of our knowledge, no prior testbed offers together. Every emulated sensor node runs as an independent operating-system process using a real transport stack rather than a discrete-event model or a container. Sensor workloads are generated using physically grounded stochastic models calibrated against real deployment data. Experiments are specified in three independent tiers, IoT Protocols (N), Scenarios (M), and Networks(L), reducing configuration effort from a combinatorial N×M×L problem to a linear N+M+L workflow, with new protocols integrated by overriding a four-method abstract interface. STGen operates above OSI Layer 4 and therefore does not model PHY- or MAC-layer behavior, such as RF interference, CSMA/CA collision avoidance, or duty cycling. The sensor models are calibrated using 1,826,223 real-world readings from the Intel Berkeley Research Laboratory; for temperature, the synthetic stream matches the 37-day measurements of 54 Mica2Dot motes with a Kolmogorov–Smirnov D of 0.071 and a Jensen–Shannon divergence of 0.018, showing that STGen reproduces the statistical structure of real sensor data rather than only plausible values. By inverting these calibrated models, STGen also synthesizes labeled false-data-injection anomalies that are separable from normal traffic, with a receiver operating characteristic AUC of 0.898 for stealthy drift and 1.0 for hard physical range violations. In our experiments, STGen instantiates 6000 concurrently emulated sensor nodes on a commodity workstation in 1.02 s using 0.62 GB of memory (approximately 99 KB per node), which is more than two orders of magnitude below the per-node memory costs of container- and VM-based testbeds. STGen also exposes deployment-relevant behavior that controlled emulation alone may hide. Under live wide-area jitter, MQTT and CoAP exhibit different loss and latency patterns than those observed under uniformly degraded NetEm conditions, including MQTT reconnection storms. These results show that STGen provides a scalable and reproducible bridge between lightweight protocol emulation and practical deployment-oriented IoT protocol evaluation.
The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines.
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.
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.
AI-assisted machine–environment interaction has emerged as an important research direction at the intersection of artificial intelligence (AI), sensor and actuator networks, and the Internet of Things (IoT) [...]
Industrial Internet of Things (IIoT) deployments face a security challenge that neither physics-based nor AI-based anomaly detection addresses alone: physics models are adversarially robust but miss behavioral attacks that remain within physical bounds, while AI models detect behavioral anomalies but are vulnerable to adversarial evasion and blind to physical sensor spoofing. This paper proposes PhySec-Edge, a hybrid framework integrating a Physics Validation Engine (PVE) with a multi-model Edge AI Detection Engine (EADE) in a layered, residual-sharing architecture. The PVE applies process model residuals, Kalman filter state estimation, cross-sensor consistency checks, and temporal gradient validation to generate physics-grounded anomaly signals. The EADE is designed around LSTM temporal detection, variational autoencoder reconstruction analysis, and graph neural network process monitoring augmented with PVE residuals; the current evaluation uses computationally tractable proxy implementations to provide a conservative lower bound on the benefits of residual sharing. Randomized smoothing is applied under bounded perturbation assumptions to improve adversarial robustness. PhySec-Edge is evaluated in a controlled synthetic IIoT setting parameterized using SWaT-inspired structural and statistical assumptions, comprising 9875 samples across seven attack classes. Across five random seeds, the hybrid framework achieves mean precision = 0.789 ± 0.004, recall = 0.808 ± 0.003, F1 = 0.798 ± 0.003, and FPR = 5.0% ± 0.0%, compared to F1 = 0.654 ± 0.006/FPR = 24.0% for the physics-only baseline and F1 = 0.774 ± 0.003/FPR = 5.0% for the AI-only baseline. An ablation study identifies residual augmentation as the primary individual improvement mechanism (ΔF1 = +0.017), while the full hybrid configuration achieves a combined gain of ΔF1 = +0.025 over the AI-only baseline. Critical hybrid advantages appear on adversarial evasion (+0.15 F1) and firmware implant (+0.17 F1), the two attack classes where neither layer alone is sufficient. A preliminary feasibility check on an Edge-IIoTset-inspired benchmark confirms that the architectural advantage pattern generalizes across dataset structures. Gateway latency analysis confirms compatibility with soft real-time industrial monitoring constraints.
The paper suggests a distributed cross-layer IoT architecture that combines LoRaWAN (Long Range Wide Area Network) with federated learning (FL) to improve reliability, scalability, and fault tolerance in multi-layer vertical farming systems in dense and dynamic environments. Unlike the traditional frameworks that rely on independent measures of QoS (Quality of Service), the proposed framework directly represents the inter-layer relationships, such as heterogeneity of latencies, robustness of connectivity, and propagation of faults. One of the contributions is the development of a cohesive cross-layer evaluation framework with six strictly defined metrics: MLDC (Multi-Layer Deployment Capacity), C-LCRI (Cross-Layer Connectivity Robustness Index), C-LFCI (Cross-Layer Fault Containment Index), SART (Smart Adaptive Recovery Time), and AIRSM (AI Resilience Score Metric), which allows for quantitatively characterizing latency differences, network resilience, fault containment, recovery efficiency, AI robustness, and energy-performance trade-offs. The experimental results show that the proposed Smart Distributed LoRaWAN–Federated Learning architecture operates reliably in high-density and multi-layer vertical farming environments, and is scalable to handle larger amounts of data. The proposed system guarantees a packet delivery ratio (PDR) of around 95% under a large-scale deployment with up to 1050 IoT nodes spread across seven cultivation layers, with a latency reduction of nearly 60%, less than 1.6 J/msg on average energy consumption, and a fault recovery time of less than 0.3 s in case of network disruptions. The proposed framework was validated using large-scale simulation scenarios developed based on experimentally reported LoRaWAN communication characteristics and agricultural IoT deployments, and operational conditions at the edge intelligence. This evaluation included up to 1050 sensing nodes in 7 vertical farming layers to approximate a realistic deployment of smart farming in a large-scale environment while keeping consistency with the recorded communication and reliability profile.
This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy environments. Monte Carlo analysis is used to generate a synthetic dataset with 200 runs per fault class by introducing component tolerances and realistic faults. A multi-stage pipeline is proposed; it begins with resampling the signals and normalizing them, and then noise is added at different levels: 5 dB, 10 dB and 20 dB. Feature fusion is performed by combining time-, frequency-, and statistical-domain features. Statistical-domain features are extracted by applying Variational Mode Decomposition (VMD) to split them into four IMF levels, followed by the application of Continuous Wavelet Transform (CWT) for time–frequency-domain analysis. Support Vector Machine (SVM), Random Forest, and Gradient Boosting are used as base-level classification models. A stacking ensemble model is developed which uses Random Forest, Gradient Boosting, and Extra Trees as base learners and Logistic Regression as the meta-learner.
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.
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.
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.
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.
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.
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.
