Search Results (498)

Wearable ECG monitors enable continuous cardiac surveillance, but most still rely on cloud-based analysis with limited on-device support for multi-class arrhythmia detection. Spiking neural networks (SNNs) are promising for low-power edge inference, yet it remains unclear how class-imbalance loss design interacts with RR-interval features in directly trained quantized SNNs, and FPGA validation in this setting is largely unexplored. We propose a quantized convolutional spiking neural network (QCSNN) for real-time arrhythmia detection on resource-constrained hardware. The model uses a dual-head architecture that jointly trains binary and four-class classifiers, subsequently reorganized into a cascaded pipeline that routes only abnormal beats to the second stage. At inference, beats classified as Normal exit at Stage 1; only beats classified as Abnormal are routed to the four-class head, so the bulk of the inference cost is absorbed by Stage 1. We evaluate two loss functions, Cross-Entropy and Focal Loss, under four RR-feature routing strategies. Without RR features, Focal Loss improves macro F1 by 2.3–2.5% over Cross-Entropy (mean Δ = +0.013 in Stage-2 macro F1; Wilcoxon two-sided p = 0.031). With RR features, this advantage largely disappears (Wilcoxon two-sided p ≥ 0.219 at all RR routings); meanwhile, RR features at the strongest routing improve Stage-2 macro F1 by +0.028 to +0.034 depending on loss function—a gain that exceeds the entire Focal-Loss-over-Cross-Entropy advantage, suggesting that RR features provide discriminative information that compensates for class imbalance at the input level. Based on clinically prioritized sensitivity, the CE:RR→Both configuration was deployed on a PYNQ-Z2 FPGA, achieving 99.02% cascaded accuracy, 11.54 ms per-beat latency, and 0.33 W accelerator power—a 31.66× power reduction and 4.01× energy reduction versus GPU inference, within 1% macro F1. These results demonstrate quantized SNNs as a practical solution for real-time edge arrhythmia monitoring that operates independently of cloud connectivity—removing the network-dependent latency, connectivity-dropout failure modes, and continuous-transmission energy burden that constrain current wearable monitors and, to our knowledge, represent one of the first systematic studies of loss-function/RR-feature interactions in directly trained SNN arrhythmia classification and one of the first FPGA deployments of a fully quantized, directly trained SNN for multi-class ECG arrhythmia detection. All code generated and used in this study has been made publicly available. Full article

Latency is a determining factor in an IoT-enabled Intelligent Transportation System. To solve the latency issue in an edge computing system connected to the cloud, where the primary challenge is the distance between the end device and the cloud server, an implementation in a real urban environment is presented to illustrate the architecture of Intelligent Edge Computing. The IEC design is scalable through a communication system that incorporates latency and distance measurements in the transmission of a detection signal using deep learning at the edge node. This enabled the transmission of 2-byte detection signals to the fog node, where the received information was processed to count vehicles on up to three streets near the intersection. The vehicle detection signal is transmitted between two different embedded platforms. This architecture enabled an average transmission latency of 15.45 ms and a total end-to-end latency of 47.9087 ms over a distance of 600 m in a real-world urban environment. The IEC system leverages this low latency and offers intelligent processing closer to the data source and, therefore, to the user. Full article

In vehicular ad hoc networks (VANETs), using vehicle platooning can improve traffic efficiency, reduce driving energy consumption, and ease traffic congestion. However, since land-based stations have limited coverage (about 7% of the Earth’s surface), ensuring low-latency communication is challenging. To address this issue, the introduction of solar-powered unmanned aerial vehicles (UAVs) as aerial base stations provides flexible and extensive communication support for vehicle platooning. Additionally, intelligent connected vehicles (ICVs) adopt non-orthogonal multiple access (NOMA) techniques for uplink transmission to further enhance transmission performance. Motivated by the above, this paper investigates the latency optimization problem of UAV-supported vehicle platooning by jointly considering multi-dimensional resource allocation and imperfect channel state information (CSI) affected by mobility. To solve this problem, we propose an iterative optimization approach with polynomial complexity, where the transmitted power and channel allocation are tackled in turn. Then, an analytical framework is developed to analyze the probability that NOMA is superior to OMA, guiding parameter settings for UAV-supported vehicle platooning. Finally, the simulation results show that the proposed latency optimization scheme can achieve lower total and average latencies on the uplink compared to state-of-the-art works and the benchmark scheme using OMA. Moreover, this paper elucidates the convergence, performance gap, and computational complexity associated with the proposed iterative optimization approach. Furthermore, the probability of NOMA outperforming OMA is quantified through Monte Carlo experiments, which validates the correctness of the developed analytical framework. Full article

This paper investigates secure and low-latency communications in UAV-mounted simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted urban vehicular networks, where severe blockage, high vehicle mobility, eavesdropping threats, and delay-sensitive traffic services coexist. In the considered system, the UAV is used not only as an aerial carrier for the STAR-RIS but also as a mobile intelligent control node that can dynamically adjust its horizontal aerial position according to vehicle distribution, blockage conditions, and eavesdropping threats. First, a UAV-STAR-RIS-assisted vehicular communication system model is developed by jointly considering urban blockage, vehicle mobility, passive eavesdropping attacks, queueing dynamics, and UAV flight constraints. Then, a high-dimensional, non-convex, and strongly coupled dynamic optimization problem is formulated to maximize the long-term average secure and low-latency utility through the joint optimization of the UAV trajectory, the STAR-RIS transmission–reflection partition ratio, the phase-shift matrices, and the transmit power allocation. Furthermore, the problem is modeled as a Markov decision process with continuous state and action spaces, and a hierarchical constrained soft actor–critic (HC-SAC)-based joint control algorithm is proposed to enable adaptive UAV movement, STAR-RIS configuration, and power control in complex dynamic environments. Simulation results demonstrate that the proposed method outperforms DDPG and several structural benchmark schemes. In the representative evaluation, the proposed HC-SAC achieves an average delay of 10.85 slots and a secrecy outage probability of 0.7160, compared with 11.72 slots and 0.8501 for PPO, and 11.94 slots and 0.8599 for DDPG. Although PPO provides the highest average secrecy rate and successful service ratio, the proposed method still maintains a competitive secure communication capability and service reliability. A normalized composite utility analysis further shows that HC-SAC attains the highest utility value of 0.9254, indicating a more favorable security–latency trade-off in complex urban vehicular scenarios. Full article

Phase-modulated continuous wave (PMCW) has emerged as a promising waveform candidate for next-generation integrated sensing and communication systems due to its favorable sensing performance and multiplexing capability. In multiple-input and multiple-output (MIMO) PMCW systems, fast-time code-division multiplexing enables simultaneous transmission from multiple transmitters but causes inter-transmitter code interference due to non-ideal cross-correlation properties. The interference is observed to manifest as a low-rank component in the range–Doppler domain while target echoes appear as sparse components. This structural distinction motivates the use of robust principal component analysis (RPCA) for interference mitigation. In practice, conventional RPCA incurs high computational complexity due to the singular value decomposition (SVD) required at every iteration. To address this limitation, we propose an unfolded RPCA network in which each iterative step is mapped to a network stage and SVD is replaced by a factorized low-rank approximation. The proposed network also incorporates stage-wise learnable parameters for adaptive interference mitigation in MIMO PMCW systems. Simulation results demonstrate that the proposed method achieves interference mitigation performance comparable to conventional RPCA with 21.2 times lower inference latency. These results confirm the effectiveness and computational efficiency of the proposed method for real-time mitigation of inter-transmitter code interference in MIMO PMCW systems. Full article

The evolution from the Internet of Things (IoT) to the Internet of Vehicles (IoV) has expanded intelligent connectivity across embedded systems while increasing cybersecurity risks arising from large scale data exchange and device heterogeneity. As IoV environments become more dynamic and safety critical, centralized Intrusion Detection Systems (IDSs) face constraints related to latency, privacy exposure, and bandwidth overhead. These limitations motivate a transition to edge-enabled IoV architectures, where localized vehicular and anchor nodes supported by edge servers enable decentralized processing, enhanced privacy, and reduced communication load. To address these operational challenges, this paper proposes FedX (Federated Explainable Ensemble Intrusion Detection System), a privacy-preserving and explainable federated ensemble IDS that integrates XGBoost and LightGBM models across resource-constrained edge vehicles and roadside units (RSUs) to enable collaborative, low-latency anomaly detection without sharing raw data. By applying adaptive weighting based on model confidence and resource availability, FedX enhances robustness and efficiency while enabling explainable decisions via SHAP and LIME analysis, which highlights reliance on key features (flow duration, speed, RPM) for high-confidence (>97%) intrusion alerts grounded in domain-specific behavior. Privacy is further enforced through Gaussian differential privacy and secure aggregation to mitigate inference and inversion attacks. Experiments on the CICIoV2024 dataset show that FedX achieves 99.1% accuracy, outperforming existing federated ensemble IDS models by up to 2.1%. The system reduces communication overhead by 17% relative to full synchronization through adaptive weighted transmission and secure aggregation. It maintains negligible accuracy loss (<1.5%) under a strong privacy budget ($ϵ$ = 1.1). The deployment of proposed IDS on Raspberry Pi 4 underscores its efficacy for edge computing. Experimental results indicate that adaptive weighting yields a 1.8% performance increase, while resource profiling shows 45% lower CPU utilization and over 50% lower power consumption compared with centralized baselines. The findings demonstrate that FedX, combined with explainable AI enables trustworthy, interpretable, and energy-efficient intrusion detection for secure next-generation Edge-enabled IoV networks. Full article

The progress made in several fields after 2023 is rather significant. Attenuation achieved by the best HCFs was reduced to 0.05–0.10 dB/km at 1550 nm, while the lowest attenuation achieved in a single-mode fiber with a pure silica core equals 0.14 dB/km. Polarization mode dispersion (PMD) has been reduced to a level typical of SMFs, through fiber spinning. In November 2024, Microsoft announced a 2-year plan to install 15,000 km of HCF cables between and within data centers processing data for Microsoft Azure cloud services. Furthermore, several HCF manufacturers have emerged: UK-based Microsoft Azure Fiber and two Microsoft subcontractors, namely Corning Inc. and Heraeus Covantics, plus two major HCF manufacturers in China, YOFC and Linfiber. Additionally, extensive work was carried out on optical amplifiers to enable new transmission bands in HCFs, both at short wavelengths (≈1300–1500 nm), with bismuth-doped active fibers, and long wavelengths (≈1700–2100 nm), with thulium- and holmium-doped fibers. On the other hand, progress in HCF standardization, splicing and elimination of loss bands introduced by contaminants, has been marginal. Standardization is blocked by multiple fiber designs being tried, with no clear winner emerging yet. Despite this, hollow-core fibers have been successfully debuted in large-scale commercial data centers and are also used in low-latency data links. Full article

Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and packet corruption can inject invalid payloads into the edge processing pipeline and trigger unnecessary buffering, parsing, and, most critically, computationally expensive cryptographic operations such as digital signature verification. This leads to wasted computation, increased latency, and reduced energy efficiency at the edge, particularly under dense IoT traffic. This paper presents an energy-aware and fault-tolerant Quantum-Dot Cellular Automata (QCA)-based integrity pre-filter for IoT-to-edge blockchain transaction ingestion. At the circuit level, we adapt and modify a previously reported fault-tolerant five-input majority gate (MV5) structure and use it as a robust primitive for nanoscale integrity-screening circuits. Building on this modified MV5, we design a set of QCA integrity blocks, including a parity checker, a compact XNOR gate circuit, a parity-bit generation circuit, and a sender-to-channel/receiver nano-communication integrity workflow suitable for early screening of corrupted payloads. Compared with the best previously reported baseline considered in this study, the modified MV5 achieves 76.47% tolerance to single-cell omission defects, corresponding to a 17.47 percentage-point increase and an approximately 29.61% relative improvement over the prior 59% omission-tolerance result, while preserving 100% tolerance against extra-cell deposition defects. At the system level, the proposed circuit is discussed as a potential early screening stage for edge-IoT blockchain transaction ingestion. A bounded analytical model is used to estimate the possible reduction in unnecessary signature-verification workload under assumed corruption and detection conditions. This analysis is not intended as a deployment-level validation; full edge-node implementation, throughput measurement, queueing-delay evaluation, real traffic traces, retransmission behavior, and empirical signature-verification profiling remain future work. The proposed parity/chunk-parity pre-filter is designed for low-cost detection of random transmission-induced corruption and does not replace cryptographic authentication, hashing, digital signatures, CRC-based detection, or blockchain validation. All proposed designs are validated using QCADesigner tools. Full article

With the rapid development of automotive intelligent driving technologies, the demand for real-time performance and bandwidth in in-vehicle bus networks is increasing day by day. When contrasted with conventional in-vehicle bus protocols like LIN and CAN, FlexRay delivers superior performance in bandwidth capacity, communication latency and data transmission speed. Such prominent strengths establish it as a core technical solution for modern automotive network systems. Targeting the flexible bandwidth characteristics of FlexRay bus systems, this work develops a novel heterogeneous message scheduling algorithm (DHSA) tailored for the dynamic segment of FlexRay. The DHSA enables flexible timeslot and priority configuration for event-triggered and low-priority messages, thereby improving the overall scheduling efficiency of FlexRay bus communication. This work adopts the CANoe.FlexRay simulation tool to construct a dedicated experimental platform and perform comparative simulations for the proposed algorithm. The experimental results show that the bandwidth utilization of the heterogeneous scheduling algorithm proposed in this paper reaches 96.6%, an increase of 13.4% compared to the Earliest Deadline First (EDF) algorithm; meanwhile, the fastest response time of the proposed algorithm is reduced by 50% compared to the EDF algorithm. This study effectively reduces message transmission latency and enhances system real-time performance and determinism, thereby further improving the communication efficiency of the in-vehicle FlexRay bus network. Full article

The rapid growth of Internet of Things (IoT) applications over 5G networks demands secure, low-latency data transmission while operating under strict resource constraints. However, existing studies have relied on simulations or partial implementations that fail to capture real 5G features, thus producing overly optimistic elucidations of cryptographic performance. In addition, the absence of end-to-end validation across system layers introduces an opaque flow effect, where transparency lacks across the full transmission path. To address this gap, this paper presents a fully integrated end-to-end 5G IoT security framework that introduces a modified RC4-NL (nonlinear) algorithm to enhance the security of lightweight stream ciphers while preserving computational efficiency. Environmental sensor data is encrypted on a Raspberry Pi 4B and transmitted over a commercial 5G standalone network using a Quectel FG50V module to a Multi-access Edge-Computing (MEC) server. A web-based dashboard built with FastAPI, accessed securely through an Ngrok tunnel, performs real-time decryption and visualization on 5G-connected mobile devices. This architecture eliminates the opaque flow effect and enables realistic performance evaluation, thereby avoiding the optimistic elucidations observed in simulation-based studies. This work experimentally evaluates cryptographic algorithms named Ascon, ChaCha20, AES, standard RC4, and the proposed RC4-NL under the same conditions. Experimental findings indicate that modified RC4-NL achieved an encryption time of 977 µs, a decryption time of 456 µs, and provides a lower power consumption of 0.40 watts, thus giving a proper trade-off between efficiency and enhanced security compared to standard RC4. Full article

The space–air–ground integrated network (SAGIN) is a key 6G architecture that provides seamless three-dimensional connectivity, exhibiting hierarchical structural symmetry between LEO satellite and HAP layers. Integrating information-centric networking (ICN) with caching on Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs) significantly enhances content distribution efficiency. Existing studies on caching mechanisms have made progress but lack optimized cache resource allocation and accurate popular content identification. Thus, an interest-aware caching scheme (ICRL) based on reinforcement learning is proposed to optimize the SAGIN’s popular content caching decisions, aiming to achieve rational symmetric allocation of cache resources across LEO and HAP layers. Different from existing RL-based caching methods, the proposed ICRL scheme considers the LEO-HAP hierarchical architecture and designs an improved reinforcement learning mechanism to adapt to the dynamic characteristics of the SAGIN. First, an air–space two-tier caching architecture is constructed to enable collaborative caching between LEO satellites and HAPs. Second, to select high-value nodes intelligently, the proposed scheme leverages a comprehensive importance model that quantitatively analyzes HAP and LEO indicators such as topology, transmission capacity, and location. Finally, a reinforcement learning-based dynamic cache mechanism is developed. It captures real-time network requests and cache states to select optimal actions and adapt to network dynamics for better content popularity matching. Extensive evaluations based on NDNSIM demonstrate that ICRL outperforms baseline schemes in terms of cache hit ratio, server load, and request latency and achieves a symmetric balance of network load and service performance in the whole SAGIN. Full article

The demands of competitiveness in global markets require the integration of Industry 4.0 (I4.0) digital technologies for any manufacturing company, regardless of size. Industrial operations require complete supply chain visibility to ensure data protection and authenticity throughout the process. This document presents a distributed architecture based on RAMI 4.0, designed for product traceability in industrial environments. It integrates automation tools, IIoT communication, cloud storage, artificial intelligence, and secure data transmission using encrypted communication protocols. The system consists of a hybrid architecture; only the first, lower-level layer corresponds to a simulated manufacturing plant with deterministic and stochastic dynamics within the production line. In the second part, the middle and upper layers are implemented, where plant data is transmitted to a cloud instance, stored in a PostgreSQL database, and subsequently analyzed using automated scripts. Reporting capabilities are incorporated with ChatGPT-3.5 Turbo, and visualization is provided through Odoo. Experimental tests demonstrated an average end-to-end communication latency of less than 200 ms, a packet loss rate of 2.67%, and 100% reliability in verifying requested reports when using the cognitive computing service. Furthermore, the results of the systematic vulnerability identification process for the architecture show a significant reduction in overall risk for most assets, with a predominant shift from high or moderate to low or moderate. The proposed architecture is validated in a simulated industrial environment under controlled conditions, demonstrating its viability as a prototype rather than as a fully implemented industrial solution. Full article

To meet the growing computational demands of expanding intelligence application scenarios, computing networks are becoming increasingly complex. However, operational challenges such as frequent node online/offline events and dynamic link status changes significantly complicate the coordinated scheduling of computational and transmission resources. Furthermore, traditional static optimization methods exhibit stability issues and slow convergence in medium-scale dynamic networks, limiting their ability to satisfy low-latency and high-reliability requirements for complex services. Therefore, we propose a dynamic computing network transmission optimization method that integrates Dynamic Graph Neural Networks (DGNN), dual virtual queues, and Proximal Policy Optimization (PPO) with Nesterov momentum acceleration. This approach aims to optimize network transmission performance during frequent node changes. This approach employs DGNN for real-time modeling of dynamic topology and node load characteristics, utilizes a dual virtual queue mechanism to characterize stability constraints of computational and transmission loads, and incorporates Nesterov momentum during policy optimization to enhance convergence speed and stability of policy updates. Simulation experiments indicate that the proposed method outperforms comparison models in queue stability, convergence performance, and quality of service, thereby improving transmission efficiency in dynamic computing networks. Full article

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

The increasing digitalization of energy transmission and distribution infrastructures has made industrial control systems (ICS), and especially IEC 61850-based communication structures, critical. IEC 61850 performs protection and control functions in substations in real time via GOOSE and MMS protocols. The fast and low-latency operation of these protocols is essential; however, their open structure leaves systems vulnerable to cyberattacks. Traditional signature-based solutions are insufficient for detecting such anomalies, and models capable of learning both time and state relationships are needed. This study develops a time-aware probabilistic NFA model to detect anomalous behavior in IEC 61850 traffic. The model analyzes GOOSE and MMS message sequences with both state transitions and time differences (Δt). Thus, not only the message sequence but also the timing variations between events are learned. The probability of each transition is dynamically updated, and deviations from normal behavior are marked as “anomalies”. The dataset used in this study was created based on normal and attack scenarios conducted in the Sakarya University Critical Infrastructure National Testbed Center Energy Laboratory (Center Energy). The experimental results obtained in the study show that the model detects time-based, structural, and behavioral anomalies with high accuracy. With a dual-model configuration, results of 91.7% accuracy, 88.9% precision, 100% recall, and a 94.1% F1-score were achieved; particularly in time-based attack scenarios, the model performance reached an accuracy level of up to 93%. Full article