Search Results (2,858)

Federated learning (FL) has emerged as an effective paradigm for collaborative model training in Industrial Internet of Things (IIoT) environments by enabling distributed devices to learn shared models without exchanging raw data. However, existing FL defence mechanisms predominantly rely on either behavioural analysis of client updates or semantic validation of model performance, limiting their ability to detect sophisticated poisoning and stealthy backdoor attacks that evade single-dimensional trust assessment. This paper proposes DBST-FL, a dynamic behavioural and semantic trust framework for robust federated learning in the Industrial IoT. The proposed framework evaluates each client through two complementary trust dimensions: a behavioural trust layer that measures gradient alignment, historical consistency, and collective deviation and a semantic trust layer that assesses benign utility and template-free semantic stress validation using server-side data. The two trust scores are integrated through a non-compensatory multiplicative trust fusion mechanism, ensuring that weaknesses in one trust dimension cannot be masked by strengths in the other. The resulting trust score guides a trust-aware aggregation strategy that reduces the influence of malicious participants while preserving the contributions of reliable clients. Extensive experiments are conducted on the Edge-IIoTset and UNSW-NB15 datasets using ANN, 1D-CNN, and LSTM models under multiple poisoning and backdoor attack scenarios. The proposed framework achieves overall classification performance competitive with the strongest robust aggregation baselines while consistently delivering stronger resilience against adversarial attacks and lower backdoor attack success rates than representative trust-based and Byzantine-robust aggregation methods, all while maintaining linear per-round computational complexity suitable for large-scale IIoT deployments. The results demonstrate that integrating behavioural and semantic trust within a unified aggregation framework provides an effective and scalable defence against advanced adversarial threats in federated learning. Full article

In Industrial Internet of Things (IIoT) scenarios, data privacy constraints and distribution discrepancies caused by time-varying working conditions hinder existing intelligent fault diagnosis models from maintaining robust generalization performance across different, particularly unknown, working conditions. To address this challenge, a federated generalization fault diagnosis method driven by statistical manifold generation is proposed. The method constructs a closed-loop collaborative strategy. Initially, individual users extract and upload representative statistical information (SI) as lightweight, privacy-preserving knowledge carriers. Subsequently, a global Gaussian mixture model coupled with a covariance expansion mechanism is established in the cloud to fit multi-source distributions and extrapolate uncertainty boundaries, thereby generating virtual SI. Finally, this virtual SI is assigned via a difference-aware mechanism and integrated locally using instance normalization to achieve domain-invariant augmented training. Extensive distributed collaborative fault diagnosis experiments conducted on rolling bearing and gearbox datasets demonstrate that, when facing completely unknown working conditions, the proposed method achieves an average diagnostic accuracy of over 85%, exceeding 90% in some tasks. Furthermore, the communication payload per round is merely 1.25 KB. While strictly preserving data privacy, the proposed method significantly enhances the cross-domain generalization capability of local models with minimal communication overhead, providing an efficient and robust collaborative intelligent diagnosis solution for resource-constrained IIoT edge devices. Full article

With the rapid development of the Industrial Internet of Things, data-driven deep learning has achieved remarkable success in bearing fault diagnosis. However, traditional static models suffer from catastrophic forgetting when facing continuously emerging fault categories and limited edge storage. Existing class-incremental learning frameworks expose critical limitations when applied to 1D vibration signals on micro edge devices, including feature space oscillation, difficulty in anchoring lightweight classifiers, and prototype drift over long incremental cycles. To address these challenges, this paper proposes a novel end-to-end class-incremental fault diagnosis method based on lightweighting and feature calibration tailored for severe memory-constrained conditions. Specifically, a lightweight feature extraction mechanism based on an $L_2$ constraint is introduced to replace computationally expensive similarity distillation, effectively suppressing feature space oscillations and providing stable spatial coordinates for old knowledge. Moreover, a mandatory balanced center–margin hybrid replay (CAHM) strategy is designed to balance class representation while proportionally retaining class center prototypes and marginal hard examples, balancing the anchor accuracy of the Nearest Class Mean (NCM) classifier and the discriminability of the decision boundary. Furthermore, an ultra-low-cost linear prototype calibration module is constructed using a learnable affine transformation to actively redirect shifted old class prototypes with negligible inference latency. Extensive long-tail incremental experiments on the CWRU bearing dataset demonstrate that the proposed method forms a highly synergistic anti-forgetting closed loop. Under an extremely limited memory budget ($K=40$), the proposed framework achieves an outstanding final average accuracy of 98.92% after five incremental stages, significantly outperforming mainstream baselines such as iCaRL, PRIL, and SCKD and exhibiting exceptional robustness for continuous online monitoring on industrial edge devices. Full article

Small and Midsized Manufacturers (SMM) face challenges as they adopt digital technologies to transform their production environment. A Manufacturing Execution System (MES) requires timely accurate data from shop floor processes to efficiently control production operations. An Industrial Internet of Things (IIoT) platform of sensors provides MES software with operational information through a communications network to enable data-driven decision-making. A midsized manufacturer in southeastern Pennsylvania provides comprehensive thermoformed and injected molded products for diverse markets. Their production equipment includes light and heavy gauge thermoforming, polymer calendaring, and Computer Numerical Control (CNC) part trimming equipment supported by air compressors, vacuum pumps, and water chillers. This project partnered a manufacturer with researchers to deploy engineering and information science students to access, catalog, and characterize shop floor Programmable Logic Controllers (PLC) inputs and outputs. Utilizing this critical PLC data, the expert lead team determined quality-critical parameters, machine counters, and fault codes essential for process optimization. Full article

Industrial Internet of Things (IIoT) networks use on-demand sensing and wireless power transfer (WPT) for self-sustainable operation. Existing scheduling frameworks are fundamentally limited because they react only after energy levels decline. Consequently, IoT nodes enter charging mode only when their residual energy falls below a threshold, causing energy outages, increased latency, and missed sensing tasks while preventing proactive WPT resource allocation. This paper proposes a Digital Twin (DT)-enabled proactive scheduling framework that transforms IIoT scheduling from reactive to proactive. The key innovation is a closed-loop virtual–real integration in which a DT layer, co-located with the control centre, maintains a Kalman filter predictor to forecast node energy over an H-slot horizon, enabling scheduling decisions before energy shortages occur. Physical layer security (PLS) constraints and DT-based anomaly detection protect against eavesdropping, energy depletion, and false data injection attacks. A multi-objective formulation jointly optimises sensing utility and WPT efficiency while accounting for DT synchronisation overhead and uplink bandwidth consumption. The resulting multi-slot Binary Integer Linear Programmes (BILP) are solved using branch-and-bound with a reliability branching rule, and a fast greedy heuristic is also developed. Simulation results over 50 Monte Carlo iterations show that the proposed framework reduces energy outage events by approximately 70% compared with the reactive baseline, activates less than 50% of available sensing nodes, and schedules less than 60% of energy transmitters for WPT. Ablation studies confirm that DT prediction is the primary contributor to the outage reduction. DT-based anomaly detection achieves a false alarm rate below 3% while maintaining a detection rate above 95%. The proposed framework improves the sustainability, efficiency, and security of IIoT networks with practical computational overhead, making it well suited for Industry 5.0 deployments. Full article

Industrial Internet of Things (IIoT) edge computing supports latency-sensitive services through task offloading to distributed edge resources. However, large-scale emergencies such as node failures and traffic surges may trigger cascading failures, leading to severe performance degradation and poor post-crisis recovery. Existing offloading methods mainly optimize operational efficiency under normal conditions while overlooking resilience against cascading disruptions. To address this issue, we propose Cascade-Emergency Resilient Offloading (CERO), an adversarial deep reinforcement learning framework for resilient task offloading in IIoT edge computing. Distinct from existing works, CERO introduces a structure-aware shared node encoder to capture heterogeneous topological roles of edge nodes, providing critical structural information for cascade-aware decision making, and incorporates cascade-oriented adversarial training to enhance robustness against compound disturbances. CERO integrates structure-aware state representation, minimax adversarial training, and potential-based reward shaping to learn resource-allocation policies balancing task efficiency and system resilience. By interacting with dynamically generated crisis scenarios, the agent learns resilient offloading policies and achieves high post-crisis recovery performance after cascading disruptions. All performance evaluations are conducted via discrete-event simulation experiments. Simulation results for normal, single-crisis, and compound-crisis scenarios show that CERO achieves comparable task efficiency under normal conditions and significantly superior post-crisis recovery performance compared to conventional rule-based strategies. In the hardest compound-crisis case involving simultaneous node failures and load surges, CERO achieves a post-recovery task-completion rate of 97.8%, surpassing the best rule-based baseline by more than 63 percentage points. Statistical significance is confirmed by the Wilcoxon signed-rank test with Bonferroni correction over 10 independent runs. These results demonstrate that CERO effectively improves the robustness and recoverability of IIoT edge-computing systems under cascading emergency scenarios. Full article

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. Full article

The rapid proliferation of the Internet of Things (IoT) has transformed industries by enabling seamless interconnectivity among devices, applications, and networks. However, this widespread adoption has also introduced significant security vulnerabilities, exposing IoT ecosystems to cyber threats such as unauthorized access, data breaches, and large-scale cyber-attacks. As IoT technology continues to evolve, mitigating these vulnerabilities remains a complex and pressing challenge. In this context, penetration testing, which is also known as pen testing, serves as a proactive security measure, enabling organizations to identify and address potential weaknesses before they can be exploited by malicious actors. Penetration testing for IoT systems is a specialized security assessment that addresses the unique vulnerabilities of interconnected devices, networks, and communication protocols, differing significantly from traditional computing and network penetration testing methodologies. In this regard, this study presents a review of penetration testing as a critical methodology for identifying, assessing, and mitigating security risks in IoT environments. We examine the key steps, tools, and methodologies specifically designed for IoT penetration testing, demonstrating their applicability across diverse infrastructures through a simple case study. Further, this study also proposes a novel Artificial Intelligence (AI)-enabled automated conceptual framework, AutoAIPenTest, that integrates machine learning, reinforcement learning, and large language models to perform intelligent, real-time security assessments in dynamic IoT ecosystems. Our findings highlight the critical role of proactive security measures, including structured penetration testing, secure development practices, and regulatory compliance, in strengthening the resilience of the IoT ecosystem. By discussing existing challenges and proposing effective security strategies, this study contributes to ongoing efforts to secure IoT domains and ensure that technological advancements do not come at the expense of cybersecurity. Full article

This applied systems-integration study addresses the “Impedance Mismatch” inherent in multi-energy microgrids—namely, the conflict where disparate physical domains traditionally force the use of isolated or highly rigid Supervisory Control and Data Acquisition (SCADA) data silos. We present a scalable, open-source edge-to-cloud telemetry pipeline orchestrated entirely through a custom MING+React stack (Mosquitto, InfluxDB, Node-RED, Grafana). The core architectural contribution is a category-specific Canonical Data Model (CDM) that functions as a translation layer, effectively decoupling sensor hardware from ingestion logic. Coupled with an automated Null-Pruning middleware loop and Metadata Separation, this architecture maintains high input purity prior to time-series persistence. To validate the system, the middleware was subjected to a high-fidelity stochastic simulation utilizing a Strict Corridor Algorithm to mimic physical inertia across 12 distinct energy vectors. Simulation-based validation demonstrates that under simulated conditions, the pipeline maintains data integrity for valid telemetry packets, achieves high accuracy in pruning malformed data, and operates with low latency under concurrency. These findings demonstrate the feasibility of this applied architecture as a resilient, cross-domain research environment for modern Digital Learning Factories. Full article

Damage localization based on ultrasonic guided waves (UGWs) ensures the reliability and safety of composites. Efficient and accurate damage localization requires full integration of different modality features. However, existing deep learning-based damage localization methods usually focus on single-modal features and cannot deeply mine and fuse different features. In this paper, we propose a novel Multimodal Feature Sensing and Fusion Neural Network (MSFN) for damage localization by UGWs in composites. This method uses an innovative multimodal input mode, in which three different modal signals, namely, the damage signal, scattered wave signal, and energy density signal, are fed into the network as inputs. We use Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs) and Bidirectional Gated Recurrent Units (BiGRUs) to construct specific encoders for the characteristics of the three signals to extract the features of different modalities efficiently and quickly. Then we employ an attention mechanism-guided feature fusion strategy to aggregate the various features, map out the correlation between the damage zones and the signal features, and finally decode them through successive linear layers to output the final damage localization results. Subsequent experimental results show that the damage localization accuracy of the MSFN can reach 98.13% even under noise interference. It is shown that its robustness and accuracy are much better than those of other existing networks and it has better localization speed and generalization. The proposed MSFN architecture comprises a CNN-based DS-encoder, a GRU-based SW-encoder, and a BiGRU-based ES-encoder, followed by an attention-guided fusion module, demonstrating its feasibility for near-real-time SHM applications. Full article

Federated learning (FL) enables privacy-preserving fault diagnosis across distributed industrial devices, but most existing methods assume homogeneous model architectures and comparable client resources. This assumption is unrealistic in resource-constrained Industrial Internet of Things (IIoT) scenarios, where clients may have substantially different memory and computation capacities. To address this challenge, we propose FedAnchor, an anchored and adaptive FL framework for resource-heterogeneous fault diagnosis. FedAnchor decomposes each client submodel into a shared anchored core and a client-specific adaptive extension. The anchored core provides a common parameter subspace for consistent masked aggregation, while the adaptive extension is selected by a server-side reinforcement-guided policy under client memory budgets. This design couples resource-aware submodel allocation with structurally aligned aggregation. Experiments on four benchmark datasets and six heterogeneous memory configurations show that FedAnchor achieves competitive or superior accuracy compared with representative homogeneous and model-heterogeneous FL baselines. Under the evaluated non-IID settings, FedAnchor improves accuracy by up to 9.8 percentage points over the strongest baseline, while maintaining favorable communication–accuracy trade-offs and empirical stability. Full article

This paper presents an innovative method and information system developed by Tre Esse Srl for the integrated management of industrial equipment. The proposed solution supports asset management, operator training, maintenance recording, and the assessment of plant reliability. Its core component is the “SSS” marker, which combines QR Code, Data Matrix, and PDF417 technologies through a dedicated encoding and decoding logic. The combined marker expands storage capacity and enables information to be distributed across different barcode types according to integrity and confidentiality requirements. The approach is particularly relevant in ATEX environments, where network connectivity may be unavailable or restricted and maintenance information must remain accessible offline. Encryption and spatially distributed encoding are used to protect confidential industrial information and support compliance with data-protection and industrial-secrecy requirements. The system integrates the markers with a cloud platform for equipment records, maintenance operations, and reliability monitoring, providing a practical bridge between offline identification and Industry 4.0 asset-management processes. Full article

The Internet of Things (IoT) is rapidly being integrated into critical infrastructure sectors, such as energy, transportation, healthcare, and industry. This surge of interconnected devices dramatically expands the attack surface and increases the risk of cascading system failures and data breaches. To address these emerging threats, this work proposes an intrusion detection system (IDS) for IoT networks that incorporates machine learning techniques, considering side-channel (power) and network traffic features. We collected power consumption traces and network metrics from IoT devices during normal operation and under diverse cyberattacks (e.g., cryptomining, flooding, port scanning). Time-series machine learning classifiers are trained on this hybrid dataset to differentiate benign versus malicious behavior. The experimental results show that the combined-feature model significantly outperforms models using only one data type, achieving high detection accuracy ($F1\approx 0.89$) and correctly identifying the attack type. The resulting IDS generalizes to previously unseen attacks, demonstrating robust, adaptive defense capabilities. The novelty of our approach lies in integrating physical side-channel signals into an automated ML framework, enhancing robustness and resilience. This smart, data-driven solution operates in near real time and helps build autonomous, constantly evolving defenses against cyber threats. Overall, our study delivers a state-of-the-art ML-based tool that learns and evolves to counter modern IoT cyberattacks. Full article

This paper presents the design and experimental validation of a hybrid architecture for the Industrial Internet of Things (IIoT), based on a Siemens LOGO! v8.4 programmable logic controller (PLC) (Siemens AG, Munich, Germany), Node-RED v3.1.3 (OpenJS Foundation, Wilmington, DE, USA), Microsoft Azure (Microsoft Corporation, Redmond, WA, USA), and Factory I/O v2.5.5 (Real Games Unipessoal Lda, Gondomar, Porto, Portugal). The proposal integrates three main functions—real-time telemetry, structured historical data storage and bidirectional remote control—over an automated tank level process. The architecture was organized into four functional layers: local automation, middleware integration, cloud services and web application. Validation was conducted using five performance metrics: end-to-end latency, remote command latency, successful delivery rate, update rate, and service recovery time. The results confirm the technical feasibility of the proposed architecture for remote monitoring and control, while also identifying the main bottlenecks in the transmission chain. Overall, the study provides an experimental basis for modular and reproducible IIoT solutions with a low adoption barrier and potential applicability to progressive digitalization scenarios in small- and medium-sized enterprises. Full article