Search Results (14,878)

The rapid development of the Industrial Internet of Things (IIoT) generates massive heterogeneous
sensor data, complicating data cleaning and normalization. Existing algorithmcentric
methods often treat quality issues in isolation and lack unified governance. This
paper proposes a governance-centered framework for multi-source industrial sensor data.
We introduce an Intelligent Catalog as the semantic governance layer to standardize metadata
and achieve semantic alignment before numerical processing. Building upon this,
an AI Agent-driven mechanism dynamically orchestrates cleaning and normalization
strategies based on real-time data status and heterogeneous features. This framework
modularly integrates classical algorithms (e.g., PCA, KPCA, LSTM) without model dependency.
Experimental results on public IIoT datasets demonstrate that our framework
significantly outperforms baseline methods in normalization consistency, noise robustness,
and stability across heterogeneous data. By shifting from an algorithm-centered to a
governance-centered paradigm, this approach provides a scalable and adaptive solution
for complex industrial sensor data management. Full article

The massive heterogeneous data streams and stringent low-latency requirements in the Industrial Internet of Things (IIoT) pose new challenges for edge network resource management. This paper addresses the joint optimization problem of Service Function Chain (SFC) orchestration and resource allocation in edge gateway-assisted IIoT networks, formulated as a mixed-integer nonlinear programming (MINLP) model to minimize end-to-end latency and energy consumption while satisfying quality-of-service (QoS) constraints. To tackle this NP-hard problem and the challenges of partial observability in distributed environments, we propose the SFC Orchestration and Resource Allocation-based Multi-Agent Proximal Policy Optimization (SORA-MAPPO) algorithm. The algorithm adopts a centralized training with decentralized execution (CTDE) paradigm with an intelligent agent cooperation mechanism. Simulation results validate the effectiveness of the proposed scheme in complex IIoT scenarios. Full article

Wireless Sensor Networks (WSNs) are increasingly evolving toward intelligent distributed systems in which local sensing, on-device inference, and collaborative model training are becoming central to scalable Internet of Things (IoT) deployments. However, the practical adoption of Federated Learning (FL) in WSN-oriented environments remains constrained by three major challenges: limited and unevenly depleted node energy, heterogeneous non-IID local data distributions, and variable client reliability during collaborative training. This paper proposes a Trust- and Energy-Aware Federated Learning (TEA-FL) framework specifically designed for resource-constrained WSN settings, in which client participation and server-side aggregation are jointly guided by residual energy estimates and dynamically updated trust scores. The proposed method prioritizes reliable, energy-efficient sensor nodes while reducing the impact of weakly aligned or low-quality local updates during global aggregation. The framework is evaluated on two representative WSN/IoT-oriented proxy benchmarks, Human Activity Recognition (HAR) and UNSW-NB15 intrusion detection, under both IID and Dirichlet-based non-IID federated partitions. Under non-IID HAR partitioning, TEA-FL improved final accuracy from 0.6752 with FedAvg to 0.7636 and final Macro-F1 from 0.5623 to 0.7185. On the more challenging non-IID UNSW-NB15 benchmark, TEA-FL achieved the highest final Macro-F1, 0.3711, compared with 0.3230 for FedAvg and 0.3323 for the trust-only baseline, although with a lower final accuracy. These results indicate that TEA-FL is particularly useful when final-round robustness, class-balanced behavior, and client sustainability are more relevant than maximizing a single peak intermediate accuracy value. Additional ablation and unreliable-client experiments further show that the trust–energy-aware aggregation component is particularly influential and that TEA-FL can improve behavior under selected low-quality participation scenarios, although it should not be interpreted as a complete Byzantine-robust defense. Overall, the findings suggest that jointly modeling update consistency and residual energy offers a practical, lightweight pathway toward more dependable and sustainable federated intelligence in next-generation WSN and IoT deployments. Full article

The rapid proliferation of Narrowband Internet of Things (NB-IoT) devices necessitates robust, privacy-preserving intrusion detection systems. While Federated Learning (FL) mitigates data privacy risks through localized training, it introduces vulnerabilities to model poisoning and computational bottlenecks on edge devices. To address these challenges, we propose a secure, hardware-optimized Blockchain-Federated Learning (BC-FL) framework. Deploying a lightweight Hybrid CNN-RNN model on Edge Gateways, we relieve end-sensors of heavy computational tasks. To overcome the ‘cold-start’ problem, we introduce a Domain-Adaptive Transfer Learning strategy, dynamically adapting a pre-trained binary classifier to a multi-class task (Normal, Mirai, Bashlite). Furthermore, a lightweight blockchain ledger provides an immutable audit trail and a reputation-based isolation mechanism to penalize malicious nodes. Evaluated on the N-BaIoT dataset, the proposed 3-class CNN-RNN model achieves 95.62% overall accuracy, with precision/recall/F1-scores of 0.99/0.91/0.95 for Mirai and 0.93/0.99/0.96 for Bashlite attacks. The framework reduces communication bandwidth by 96% compared to centralized learning. During simulated Byzantine attacks, the reputation mechanism successfully banned malicious nodes, maintaining a robust 95.62% global accuracy. This framework offers a highly scalable, secure, and computationally feasible solution for real-time anomaly detection in resource-constrained IoT edge environments. Full article

Water-quality monitoring is critical for maintaining the symbiotic balance and productivity of aquaponic systems. This study presents the design, implementation, and evaluation of a remote, real-time monitoring system based on the Internet of Things (IoT) paradigm. The system continuously monitors the key parameters of temperature, pH, electrical conductivity, total dissolved solids, salinity, dissolved oxygen, turbidity, and total suspended solids. Utilizing a modular architecture, the platform provides real-time visualization, cloud-based data management, and automated alerts via SMS and e-mail to notify operators of deviations from established tolerance ranges. The system was experimentally validated over a six-month period in a pilot-scale aquaponics system cultivating common carp (Cyprinus carpio). Statistical analysis demonstrated a 97% data acquisition reliability rate. Furthermore, no statistically significant differences (p > 0.05) were observed between the sensor-based measurements and reference laboratory analyses, confirming the system’s high accuracy. This versatile and cost-effective tool enables data-driven decision-making, facilitates timely interventions to reduce production losses, and ensures the long-term environmental stability of integrated aquaculture systems. Full article

As lithium-ion batteries are widely deployed, thermal runaway (TR) poses severe safety risks, making early and accurate warning systems critical. While machine learning (ML) has advanced data-driven TR prediction, challenges remain regarding model interpretability, generalization under unseen conditions, and real-time deployment. This review evaluates recent progress in ML-driven TR warning technologies, moving beyond a mere compilation of algorithms to provide an organized synthesis of the field. As a key contribution, we critically analyze the paradigm shift toward physics-informed ML, demonstrating how embedding electrochemical and thermodynamic principles into neural networks reduces prediction errors by 40–60% while enhancing robustness. Furthermore, we synthesize a Battery Digital Twin (BDT) framework integrating Internet of Things (IoT), cloud computing, and on-board master BMS for closed-loop collaboration, effectively balancing low-latency control with high-precision health assessment. Finally, we outline strategic pathways for future breakthroughs: advancing physics-informed cross-scale modeling, optimizing cloud-edge architectures, and establishing open access benchmark databases. By calling for standardized evaluation protocols to break down data silos, this review provides a comprehensive roadmap and actionable insights to accelerate the industrial implementation of next-generation intelligent battery safety management. Full article

Background/Objectives: Robot-assisted surgery (RAS) has grown rapidly over the past few decades. To determine the effect of high stress levels on the performance of RAS, monitoring some parameters of surgeons is critical. This can be aided by the development of Artificial Intelligence (AI), which has exponentially grown in recent years. This study aims to predict the surgeon’s stress level based on ergonomic, kinematic and physiological parameters of the surgeon obtained in the immediately previous situation during RAS activities. Methods: Physiological data were recorded from surgeons during twenty-six surgical sessions involving twelve participants with different levels of experience and surgical specialties. After dataset generation, two preprocessing procedures (scaling and normalization) were applied to the recorded signals. The processed data were then partitioned into two subsets: 80% of the samples were used for model training and cross-validation, while the remaining 20% were reserved for testing. Six AI approaches were evaluated to build predictive models: multiple linear regression (MLR), a support vector machine (SVM), a multilayer perceptron (MLP), a convolutional neural network (CNN), random forest (RF), and a U-Net algorithm (UNET). These algorithms were trained using the training dataset and subsequently assessed on the independent test set. In addition, after each surgical session, surgeons completed a questionnaire reporting their perceived stress level, which was later compared with the stress estimates generated by the predictive models. Results: The results obtained showed that MLR and scaling pre-processing reached the highest R² coefficients and the lowest error for each studied parameter. The results of the surgeons’ surveys were highly correlated for microsurgery activities (R² = 0.7989) and for laparoscopy RAS (R² = 0.8381). Conclusions: The linear models proposed were correctly validated on cross-validation and the test dataset. This fact demonstrates the possibility of predicting factors that help us to improve the surgeon’s health during RAS. Full article

Knee Osteoarthritis (KOA) is a leading contributor to global physical disability, where delayed diagnosis often results in irreversible joint damage and socio-economic cost. Early diagnosis remains challenging due to subtle radiographic biomarkers and limited access to specialized expertise, particularly in distributed healthcare settings. Within the evolving landscape of the Future Internet, characterized by Internet of Medical Things (IoMT), edge–cloud computing, and intelligent digital health infrastructures, there is an increasing demand for scalable, low-latency, and explainable AI-driven diagnostic solutions. In this work, we propose a Dual-Stream Wavelet Fusion Network (DS-WFN) alongside a distributed edge-cloud architectural roadmap tailored for deployment in distributed and edge-enabled healthcare ecosystems. The framework integrates a spatial morphological stream with a spectral wavelet stream, augmented by an Adaptive Wavelet Selection Mechanism (AWSM). The AWSM dynamically selects optimal frequency bases (Haar, Symlet, Daubechies) to preserve fine-grained diagnostic features typically lost in conventional CNN architectures. An Adaptive Spatial Alignment (ASA) module further ensures efficient fusion of heterogeneous representations, enabling robust feature integration across computational nodes. Experimental results across a five-fold patient-isolated cross-validation protocol demonstrate that the DS-WFN achieves a mean classification accuracy of 76.3% (95% CI: 71.6–80.8%) and a macro-averaged F1-score of 0.747 (95% CI: 0.697–0.795), consistently outperforming single-stream baselines while preventing patient-level data leakage. Furthermore, Grad-CAM visualizations provide interpretable outputs aligned with clinical diagnostic criteria, supporting trustworthy AI integration into digital healthcare workflows. Furthermore, we disclose a methodological framework for edge-based implementation, highlighting how localized inference ensures data sovereignty and real-time clinical support. By combining multiscale signal processing with deep learning under a Future Internet paradigm, this work contributes a scalable, explainable, and edge-ready diagnostic framework for early KOA detection, enabling intelligent, connected, and resource-efficient healthcare services. Full article

The use of the Internet of Things (IoT) and wearable devices to enhance construction safety has recently attracted growing attention from the construction research community. In this paper, a system-level Structural Equation Model (SEM) is proposed to examine the relationships among perceived Safety System Value (SSV), Organizational Readiness (OR), and Adoption Barriers (AB). A survey of 567 construction professionals in Saudi Arabia was used to collect the data, which was analyzed using covariance-based SEM with Robust Maximum Likelihood (MLR) estimation. SSV was found to act as a perceptual antecedent of OR (β = 0.719). OR, in turn, was found to strongly affect AB (β = 0.712). The direct effect of SSV on AB was statistically significant (β = 0.191). Furthermore, the mediation analysis showed that approximately 73% of the total effect of SSV on AB is transmitted through OR (indirect β = 0.512, total β = 0.703). The model explained 51.6% of the variance in OR and 73.9% of the variance in AB. Data were collected through a structured questionnaire survey of 567 construction professionals in Saudi Arabia. This research contributes to the broader field of systems research by presenting a framework for the adoption of safety-related construction technologies as a systems phenomenon. The research has practical implications for building readiness-driven approaches for the effective integration of safety technologies in safety-critical construction environments. Full article

The continuous growth of IIoT systems has significantly increased the number of connected devices and message interactions, creating higher requirements for communication mechanisms in terms of scalability and adaptability under dynamic network environments. Although MQTT is widely used for its lightweight communication, its traditional centralized broker architecture limits scalability and fault tolerance in large-scale data transmission, reducing system scalability and fault tolerance. Additionally, static QoS configuration is difficult to adapt to dynamic environmental changes, resulting in high end-to-end latency and limited system throughput. To address these issues, this paper proposes a distributed MQTT communication method based on intelligent QoS routing and hierarchical collaboration (DAIS-MQTT). This method designs a network routing algorithm based on a hierarchical tree structure (LCN), which effectively addresses the scalability limitation of centralized proxies by enabling multi-level proxy collaboration and self-recovery from faults. At the same time, it proposes a QoS routing algorithm based on intelligent decision trees (IQR), which jointly optimizes proxy selection and QoS levels to dynamically adapt to changes in the network environment, thereby solving the problem of insufficient adaptability in static QoS configurations. Experimental results show that compared with the traditional MQTT-based communication method, the DAIS-MQTT method reduces the average message delay by 29.9%, increases system throughput by 28.2%, and maintains a reliable transmission rate of 98.7% in unreliable network environments, making it suitable for high-dynamic and large-scale IIoT communication scenarios. Full article

This paper proposes a lightweight and adaptive anomaly detection framework for Internet of Things (IoT) time-series data that progressively combines statistical filtering with reinforcement learning (RL)-based decision mechanisms. Three classical statistical filters, Hampel, interquartile range (IQR), and Z-score, are initially evaluated under controlled IoT anomaly scenarios. While fixed-parameter configurations perform well under specific conditions, their performance degrades in non-stationary and heterogeneous environments. To address this limitation, a tabular Q-learning agent is introduced to dynamically select both filtering methods and their associated parameters according to scenario-specific signal characteristics. By extending the action space to include joint filter and parameter selection, the framework improves adaptability while reducing the need for manual tuning. A multi-agent reinforcement learning (MARL) formulation is further introduced to support distributed learning across heterogeneous IoT environments. The framework is additionally evaluated using real-world IoT temperature data augmented with controlled anomaly injection, enabling reproducible benchmarking under partially realistic sensing conditions. Experimental results show that both RL and MARL maintain stable detection performance across heterogeneous sensor streams. While MARL does not systematically outperform the single-agent approach in detection accuracy, it improves scalability and supports scenario-specific policy specialization, which is particularly relevant for distributed IoT deployments. Overall, the proposed approach provides a lightweight, interpretable, and computationally efficient solution for adaptive anomaly detection in resource-constrained IoT systems. Full article

Grant-free non-orthogonal multiple access (NOMA) enables communication without a scheduling process. Base station (BS) must detect active users without knowing their number, a challenge that also occurs in grant-free NOMA–Internet of Things (IoT). Device detection in grant-free NOMA-IoT can be considered as signal reconstruction in compressive sensing (CS). To address this limitation, we propose extended sparsity estimation- orthogonal matching pursuit (ESE-OMP) to detect active devices in single measurement vector (SMV) and multiple measurement vector (MMV) problems for grant-free NOMA-IoT systems, a reconstruction method in CS that operates without prior knowledge of the sparsity level, which corresponds to the number of active devices. The algorithm iteratively detects active devices by monitoring the absolute difference in $l_1$-norm of successive residuals, terminating when the change falls below a predefined threshold $\epsilon$. ESE-OMP is evaluated under various grant-free NOMA-IoT systems, irregular low-density spreading-orthogonal frequency division multiplexing (LDS-OFDM), regular LDS-OFDM, and pattern division multiple access (PDMA) systems. When the signal-to-noise ratio (SNR) is 10 dB for the SMV problem with static active device composition, the regular LDS-OFDM system achieves a bit error rate (BER) of $2.95\times {10}^{-4}$, while irregular LDS-OFDM and PDMA systems achieve BERs of $3.78\times {10}^{-3}$ and $1.79\times {10}^{-2}$, respectively. The smaller the number of active devices, the better the performance of ESE-OMP. Full article

Existing noisy-label hyperspectral image classification (HSIC) methods usually address clean sample selection and representation regularization as separate problems, although the reliability of observed labels varies substantially across samples in hyperspectral data. This issue is amplified by mixed pixels, boundary ambiguity, spectral overlap, and limited labeled samples, which make hard clean samples difficult to distinguish from mislabeled ones. We therefore propose PDAM, a sample-reliability-guided training framework for noisy-label HSIC. The method first estimates feature-space class consistency by comparing each sample with the prototype of its observed class and converting this consistency into a reliability probability with a Gaussian mixture model. To reduce conservative false negatives, matched high-confidence selection is further used to recover hard but correctly labeled samples. The resulting reliability estimate then determines how strongly the observed label is trusted through target refinement and how strongly the input is perturbed through reliability-guided masking. Finally, masked reconstruction provides label-independent structural regularization so that uncertain samples can still contribute to spectral–spatial representation learning. Under the evaluated synthetic symmetric noise settings on the University of Pavia (UP), Salinas Valley (SV), and Kennedy Space Center (KSC) datasets, PDAM achieves the best OA and Kappa in most reported comparisons and improves robustness under both moderate and severe noise. At 30% noise, PDAM reaches 97.30% OA on UP, 98.13% OA on SV, and 95.37% OA on KSC. Ablation studies further support the necessity of reliability estimation, hard clean sample recovery, and reliability-guided supervision and regularization within this unified training mechanism. Full article

The transition toward smart grids and Industry 4.0 demands a fundamental shift in maintenance strategies, as manual inspection methods are increasingly being supplanted by automated monitoring systems. Among the advanced technologies for smart inspection, infrared imaging has advantages including non-contact operation, intuitive visualization, and predictive capabilities, which has become a cornerstone for autonomous inspection of critical power infrastructure. This review provides recent advancements in infrared imaging, with a specific focus on automated power system inspection. The discussion starts with an overview of the fundamental principles and system architectures, emphasizing the pivotal role of infrared detectors. A detailed analysis traces the technological evolution from traditional photon detectors to current uncooled microbolometers, and critically assesses emerging low-dimensional materials. The analysis highlights inherent performance trade-offs among sensitivity, operating temperature, and fabrication cost. Subsequently, the review explores advanced signal processing algorithms, such as real-time non-uniformity correction and adaptive noise suppression, which are typically implemented on FPGA platforms. Advanced optical configurations—encompassing computational imaging, lensless designs, and scattering suppression methods—are also discussed, demonstrating how their convergence enhances image fidelity and operational reliability in complex field environments. Representative application paradigms are surveyed, including drone-based transmission line inspections, patrol robots in substations, and fault diagnosis in photovoltaic plants; for each, operational efficacy and economic benefits are assessed. Despite considerable progress, several challenges persist, notably the performance–stability–cost trilemma in novel detector development, the substantial computational demands of end-to-end optimized systems, and a lack of standardization. Finally, the review outlines future research directions, such as high-performance uncooled arrays, AI-driven co-design of optics and algorithms, and the development of standardized, low-cost, intelligent inspection platforms. Full article

Microwave wireless power transfer (MWPT) is a promising technology for powering dedicated industrial Internet of Things (IoT) devices, enabling battery-free operation. However, in realistic MWPT deployments, the received RF signals fluctuate drastically due to varying transmission distances and multipath fading. Additionally, the equivalent impedance of sensor nodes varies significantly during duty cycles, shifting between a low-resistance active state and a high-resistance sleep state. Consequently, maintaining high rectification efficiency under these dynamic conditions remains a critical challenge. This paper proposes a high-efficiency rectifier with a wide input power and load range based on the suppression of second and third harmonics. The rectifier adopts a dual-diode parallel configuration. By leveraging the impedance compensation characteristics of two short-circuited stubs with distinct electrical lengths, it simultaneously achieves fundamental-frequency impedance matching and harmonic suppression without the need for an additional matching network. Validated through theoretical derivation, simulation analysis, and physical prototype testing, the proposed 2.45 GHz rectifier realizes high-efficiency rectification over a wide dynamic range. Experimental results demonstrate that the power dynamic range reaches 10 dB when the rectification efficiency exceeds 70%, and extends to 17 dB when the efficiency is above 60%. Furthermore, the rectification efficiency is insensitive to load variations (100–1200 Ω), making it highly suitable for powering wireless sensor nodes with varying operating modes in complex electromagnetic environments. Full article