Search Results (132)

Urban traffic congestion is a primary driver of greenhouse gas emissions, wasted fuel, and degraded air quality, presenting a significant barrier to achieving sustainable cities (SDG 11) and climate action (SDG 13). Standard Adaptive Traffic Signal Control (ATSC) systems are either financially prohibitive for developing countries or lack certified safety mechanisms for physical deployment on live roads. This paper proposes and validates SYTRAC (System for Adaptive Traffic Control), a low-cost, safety-critical Adaptive Traffic Signal Control system designed for resource-constrained urban environments. SYTRAC implements an asynchronous co-design that combines real-time visual vehicle detection on an NVIDIA Jetson Nano GPU with deterministic safety execution on a Siemens S7-1200 Programmable Logic Controller (PLC). The core of the system is the Density-Weighted Adaptive Green Extension (DWAGE) algorithm. DWAGE provides a stable, interpretable, and computationally lightweight alternative to complex optimization methods such as genetic algorithms, particle swarm optimization, or Deep Reinforcement Learning. We establish a formal mathematical queue-stability guarantee using a closed-form Foster–Lyapunov drift argument. A three-mode fault-tolerant state machine with a 2 s watchdog automatically transitions to fixed-time fallback in the event of hardware or camera stream failures, protecting physical intersection safety. The system was validated through hardware-in-the-loop field deployments at a live intersection in Ouargla, Algeria. SYTRAC achieved a statistically significant 22.1% reduction in average vehicle delay ($p<0.001$), while microscopic simulations confirmed up to 28.0% delay suppression during lane-blockage incidents. Critically, this delay reduction translates to an environmental saving of 53.5–72 kg of CO₂ avoided per day, alongside annual fuel savings of 8430 L. Assembled within a $1257 hardware budget, SYTRAC delivers a cost-effective, open-source, and reproducible platform that bridges the gap between adaptive intelligence and industrial safety, providing a scalable blueprint for sustainable urban traffic management in emerging economies. Full article

This systematic literature review examines 31 peer-reviewed articles released in 2021–2025. It offers a coherent summary of intrusion detection and prevention systems based on machine learning and deep learning of industrial communication protocols. The review categorizes the studies depending on research focus, experimental setup, datasets, and analytical methods. According to the quantitative analysis results, the most suitable model for use in this case is the hybrid deep learning architecture, which includes the combination of Transformer-LSTM models and MODLSTM models, with 29% of the reviewed studies using these models and achieving detection rates of over 99%. Federated learning was mentioned in about 9.7% of the studies, and for 67% of them, real-world data was not available, indicating a lack of access to real-world data. These models are prevalently implemented to identify Denial-of-Service, Man-in-the-Middle, and data injection attacks. The results show that Modbus/TCP is the most studied protocol, which indicates how common it is in industrial systems. Meanwhile, other more recent protocols like MQTT and OPC UA are gaining momentum. Another insight revealed by this review is the tendency towards the use of more realistic validation techniques. Hardware-in-the-loop simulations and physical testbeds are in use in many studies. Integrated solutions which comprise a combination of edge, fog, and cloud computing are gaining popularity. Federated learning (utilized in 6.45% of the selected corpus) and software-defined networking are two emerging directions. Although these developments have taken place, there are still critical gaps, including the scarcity of real-world datasets combined with a lack of robust approaches to address scalability and privacy complications. Furthermore, recent IIoT protocols have not been thoroughly evaluated. The study highlights the need for adaptive and lightweight frameworks and the importance of implementing mechanisms that ensure privacy. There is also a need to have standardized evaluation criteria. These factors combined are instrumental for creating secure, resilient, and interoperable industrial networks during the Industry 4.0 period. Full article

This paper introduces an IIoT-based hybrid predictive maintenance system for industrial steam boilers, which responds to the increased demands for making intelligent and accurate decisions by leveraging data-driven analytics in complex industrial environments. The proposed approach presents comparative hybrid predictive monitoring frameworks based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models integrated with Statistical Process Control (SPC) and Cumulative Sum (CUSUM) monitoring techniques for industrial boiler monitoring; it allows accurate system behavior prediction coupled with enhanced anomaly detection across interconnected subsystems. To ensure practicability, the framework is implemented in an integrated operation technology and information technology (OT–IT) architecture with one year of real operation data from an industrial steam boiler in an oil refinery. A two-phase validation strategy is employed to overcome the gap between offline model development and application. During the initial phase, predictive models are developed and tested based on multivariate time-series data to model both the time dependence of the processes and the mechanical variables. The second phase involves the online deployment of the predictive monitoring framework through a Hardware-in-the-Loop (HiL) implementation with Programmable Logic Controller (PLC)-based and Open Platform Communications Unified Architecture (OPC UA) communication to enhance realistic system validation under emulated boiler process conditions without disrupting live plant operations. The experimental results indicate that the GRU model outperforms the LSTM, achieving good R² (0.8956) and mean absolute percentage error (MAPE, 0.6345%), demonstrating strong predictive accuracy across key operational variables. In addition, SPC is used to set up adaptive operational thresholds based on normal industrial process behavior, and then CUSUM is applied to the prediction residuals to improve the detection of the gradual degradation of the system. Real-time validation ensures system stability, low latency, and bidirectional data transfer between the OT and IT layers, enabling continuous monitoring and real-time decision-making. The proposed solution provides a practical and scalable predictive maintenance framework in an industrial context, particularly in oil and gas operations, that helps to transition to Industry 4.0 and intelligent asset management. Full article

Dataset quality significantly affects the effectiveness of a machine learning (ML) model in an intrusion detection system (IDS) for cyber-physical industrial control systems (CPS/ICS) and Industrial Internet of Things (IIoT). Existing surveys compare datasets qualitatively or along limited dimensions, whereas this review introduces quantitative documentation and decision-aid scoring across 23 ICS/OT/IIoT datasets. These datasets are analyzed along seven measurable axes, with their attacks mapped to MITRE ATT&CK for ICS tactics. Quantitatively, 14 of the 23 datasets (60.9%) are built on physical testbeds, and 22 of the 23 map to MITRE ATT&CK for ICS, spanning 11 of the 12 tactics. We introduce a checklist for documentation completeness (0–7) and a decision-aid rubric (0–15) covering realism, attack diversity, class imbalance, documentation, and reproducibility. Protocol coverage across these datasets is skewed toward Modbus (13 of 23 datasets, 57%), while many other protocols (such as Profinet and OPC UA) are underrepresented relative to their industry deployment. The available datasets show structural gaps in capturing multi-stage adversary behavior. In practice, dataset selection should pair a realism-anchored dataset with a high-reproducibility one, and account for protocol diversity and APT representation. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article

Heterogeneous fault information and a lack of real-time synchronization in CNC machine tools hinder effective Prognostics and Health Management (PHM). This paper designs and implements a digital twin-driven PHM framework for machine tools that integrates a unified machine-tool fault information dictionary and a mechanism-data dual-driven diagnostic model (ResNet-TCN). A cyber–physical platform was developed using OPC UA and RESTful APIs to ensure real-time data synchronization. Experiments on the PHM 2010 dataset demonstrate that the proposed ResNet-TCN model achieves a root mean square error (RMSE) of 5.46 μm for tool wear prediction. Its performance surpasses that of traditional LSTM models, and the proposed framework effectively eliminates information silos, providing a responsive, scalable and accurate PHM solution for smart manufacturing. Full article

Petri nets represent a highly versatile mathematical formalism for modeling discrete event and hybrid systems. For the development of modern complex production processes for Industry 4.0, integrating these formal models with industrial communication standards is an appropriate and effective option. The main aim of the proposed article is to design a new web-based software tool for the modeling, simulation, and control of mechatronic systems with OPC Unified Architecture support. To accomplish this task, an original software solution called PetriLink is proposed. This platform leverages an intuitive graphical interface and significantly expands the formalism by combining hybrid Petri nets with Colored Petri Nets (CPN) data extensions and a reactive OPC UA subscription model. These new features greatly expand the area of systems that can be modeled and controlled, bridging the gap between theoretical academic tools and practical industrial automation. Furthermore, the structural flexibility of the implemented Petri net models enables the explicit representation of symmetric cyber-physical architectures, as well as the design of asymmetric, event-driven control strategies (e.g., using inhibitor and reset arcs) for enhanced system robustness. The platform was evaluated on a reference net of 5000 places and 2500 transitions, where an incremental dirty-flag evaluation mechanism keeps the per-step engine cost below 1 ms for sparse industrial markings and at about 350 µs for a moderate workload of one hundred concurrent tokens, yielding a speed-up of up to roughly three orders of magnitude over naive full re-evaluation and confirming consistent soft real-time behavior on commodity hardware. Offering a graphical environment for the design of discrete event and hybrid system control algorithms, it can be used for education, research and practice in cyber-physical systems (Industry 4.0). Full article

The ongoing evolution of Industry 4.0 technologies necessitates novel and effective modes of human–machine interaction within production environments. This work presents a modular approach to the design and implementation of graphical user interfaces (GUI) in mixed reality, leveraging the Asset Administration Shell (AAS) standard. The proposed method enables the dynamic rendering of GUI elements in a Mixed Reality setting based on structured data retrieved from an AAS server. Developed for the Microsoft HoloLens 2 using the Unity engine and the Microsoft Reality Toolkit 3 (MRTK3), the system allows for the spatial placement of interface components either at predefined coordinates or in relation to specific elements of a production line model. Additionally, it incorporates a real-time distributed architecture utilizing OPC UA PubSub and MQTT protocols for processing and visualising live data. The prototype demonstrates the viability of using AAS as a flexible framework for defining and generating GUI components in immersive environments and lays the groundwork for further research into standardised, easily deployable user interface solutions for industrial applications. Full article

Industry 4.0 is speeding up the move to connected, data-driven, and automated production, where the Internet of Things (IoT) enables sensing, communication, and real-time support for decisions. At the same time, rapid growth in industrial IoT studies has led to scattered technologies, architectures, and results. This paper fills this gap through a systematic literature review on IoT for Industry 4.0. It also helps readers compare methods and choose suitable building blocks for real deployments today. We focus on key technologies, integration architectures, application areas, challenges, trends, and reported benefits. Using PRISMA 2020, we searched five major databases (Scopus, MDPI, IEEE Xplore, ScienceDirect, and Web of Science) for 2020–2025 and found 584 records. After removing duplicates and screening, we kept 96 peer-reviewed studies for detailed analysis. Results show that most studies use a layered stack that combines sensing/actuation, industrial networking, data collection pipelines, and analytics across edge, fog, and cloud resources. MQTT, OPC UA, CoAP, LPWAN, and 5G connectivity are often used for communication, while RAMI 4.0, IIRA, and similar layered models guide system design. Many architectures follow an edge–cloud pattern, with growing focus on digital twin/CPS links and security-by-design. Applications are mainly smart manufacturing, predictive maintenance, and logistics, with added work in energy management, Construction 4.0, and agri-food monitoring. The key barriers remain interoperability, data quality and evaluation gaps, cybersecurity risks, legacy integration, and deployment limits. The review points to future work on edge AI/TinyML, deterministic connectivity, scalable digital twins, trusted data sharing, and sustainable industrial IoT. Full article

Textile equipment monitoring and scheduling are constrained by device heterogeneity, stringent real-time requirements, and complex dynamic resource scheduling. To address these challenges, this study proposes a cloud-edge-end collaborative remote monitoring and scheduling system for textile equipment. The proposed system aims to overcome the limitations of traditional solutions in compatibility, real-time performance, and resource utilization. This work is positioned as an applied systems study, in which the scheduling modules are used as monitoring-driven service extensions rather than as standalone algorithmic contributions. We develop (i) an adaptive multi-protocol parsing mechanism, (ii) a collaborative hierarchical alerting framework, and (iii) monitoring-driven computing-resource and production-scheduling services. The system is implemented across the terminal device layer, edge computing layer, and central cloud layer. Embedded acquisition terminals were designed to support multiple industrial protocols, including Modbus RTU, OPC UA, and EtherCAT. Dynamic protocol adaptation was used to identify, parse, and map heterogeneous protocol frames into a unified information model at runtime. In the workshop deployment reported in this study, field validation was conducted on 120 air-jet looms connected through RS485-based Modbus RTU. Other interfaces were evaluated as prototype-supported communication options rather than as quantitatively validated workshop interfaces. A cloud-edge-end collaborative alerting framework is designed by combining an improved OPTICS algorithm with a graph neural network (GNN) model. It improves the redundant-alarm filtering rate by 42.1%, achieves 96.8% root-cause diagnosis accuracy, and keeps the end-to-end alert latency at or below 200 ms at the 99th percentile. A cross-layer resource scheduling strategy incorporating a fuzzy PID controller is proposed, accompanied by a weighted multi-criteria resource-optimization model. This strategy increases the average CPU utilization of edge nodes to 84.3 ± 3.6% and reduces burst-task response latency to 236 ± 48 ms. In addition, an adaptive particle-swarm optimization module based on a scalarized composite scheduling objective reduces the equipment idle rate to 6.5% and shortens the average order completion time by 28.4%. Overall, the proposed framework demonstrates the feasibility of cloud-edge-end collaborative monitoring and scheduling in the validated RS485/Modbus-RTU-based weaving-workshop scenario, while its application to other textile processes, machine types, and communication configurations requires further protocol-specific adaptation and field validation. Full article

Industry 4.0 and 5.0 technologies have made industrial environments data-rich, yet a persistent cognitive gap remains: operators face substantial difficulty interpreting and acting on this data in unstructured, time-critical situations. This paper presents an architecture that integrates the Asset Administration Shell (AAS), OPC UA, and a Large Language Model (LLM)-based agentic AI within a mandatory Human-in-the-Loop (HITL) framework. The AAS acts as a semantic grounding layer through Retrieval-Augmented Generation (RAG), supplying the LLM agent with ECLASS-referenced technical parameters that reduce the risk of hallucination. OPC UA Methods form a deterministic execution layer that keeps agent actions within PLC-validated safety boundaries. The HITL mechanism enforces a cryptographic approval gate so that no physical machine action can occur without documented human authorization. This requirement was motivated by an industrial survey ($n=117$), in which 47% of employees stated that human oversight is irreplaceable, combined with enterprise safety and accountability requirements and broader governance considerations for AI-driven actuation in safety-critical cyber-physical systems. Two proof-of-concept case studies evaluate the architecture under controlled laboratory conditions. Proof-of-concept results indicate system processing latencies of 1.7 s (maintenance) and ∼15 s (scheduling), with end-to-end latencies (including mandatory human approval) of 14.9 s and 62 s, respectively, representing estimated improvements of approximately 97% and 96% over expert-estimated manual baselines (∼8 min and 25–40 min). All figures derive from single scripted runs under controlled laboratory conditions and should be read as indicating architectural feasibility at Technology Readiness Level 4, not as statistically validated performance benchmarks: variability bounds and confidence intervals are unavailable, the manual baselines are expert estimates rather than instrumented measurements, and operator deliberation times derive from a single response per scenario. A structured comparison with related work shows that, to the authors’ knowledge, no published approach in the surveyed literature combines AAS semantic grounding, OPC UA deterministic execution, and mandatory cryptographic HITL within a single empirically grounded framework. Full article

This work presents a literature review on interoperability in industrial robotics. The analysis of 45 selected studies reveals that existing approaches remain fragmented across communication, control abstraction, and semantic integration layers. The review synthesizes key developments in programming paradigms, communication technologies, and interoperability solutions in heterogeneous industrial environments. Based on the identified gaps, a conceptual interoperability framework, referred to as the Universal Robot Protocol (URP), is derived to support unified integration across system layers. URP is not proposed as an implemented protocol, but as a research-driven conceptual direction intended to integrate existing technologies within a coherent interoperability architecture. This contribution aims to support future research and the industrial adoption of interoperable robotic systems in Industry 4.0 and Industry 5.0 environments. Full article

This paper presents a Digital Twin (DT)-based framework for the control, monitoring, and intelligent optimization of an Assembly/Disassembly/Repair Mechatronic Production Line (A/D/R MPL), developed as a laboratory platform aligned with Industry/Education 4.0/5.0 paradigms. The A/D/R MPL is assisted by two complementary cyber–physical robotic systems: an Assembly/Disassembly/Replacement Cyber–Physical Robotic System (A/D/R CPRS), and a Mobile Cyber–Physical Robotic System (MCPRS), enabling both fixed and mobile intelligent operations. The CPRS is equipped with an industrial robotic manipulator (IRM) responsible for A/D/R tasks, while the A/D Mechatronic Line (A/D ML) consists of seven interconnected workstations (WS1–WS7) dedicated to storage, transport, quality control, and final product handling. MCPRS includes a wheeled mobile robot (WMR), carrying a robotic manipulator (RM) and Mobile Visual Servoing System (MVSS). Each workstation is connected to a local slave programmable logic controller (PLC), which communicates via PROFIBUS with a master PLC located at the CPRS level. Additional communication infrastructures include LAN PROFINET and LAN Ethernet for local integration, and WAN Ethernet connectivity enabled through open platform Communication-Unified Architecture (OPC-UA), ensuring interoperability, scalability, and remote accessibility. Also, MODBUS TCP as serial industrial communication is used between the master PLC and the MCPRS. Virtual environment supports task planning through Augmented Reality (AR) and real-time monitoring through Virtual Reality (VR). The system behaviour is modelled with synchronized hybrid Petri Nets (SHPNs) which describe the discrete and hybrid dynamics of A/D/R processes. Artificial intelligence (AI) techniques are integrated into the DT framework for optimal task scheduling and adaptive decision-making. As a laboratory-scale implementation, the proposed system provides a comprehensive platform for experimentation, validation, and education. It supports Education 4.0/5.0 objectives by facilitating hands-on learning, human–machine interaction, and the integration of emerging technologies such as AI, Digital Twins, AR/VR, and cyber–physical systems. At the same time, it embodies Industry 4.0/5.0 principles, including interoperability, decentralization, sustainability, robustness, and human-centric design. Full article

Digital twin (DT) technology is a key element of Industry 4.0. Despite its rapid development, current research is mainly focused on industrial optimisation and machine-level monitoring. However, its implementation in the educational process lags significantly behind practice. Moreover, existing DT implementations in education often emphasise visualisation or simulation, while neglecting synchronisation and verification of functional equivalence between the physical and virtual systems. This study presents the design, development and experimental verification of a digital twin of a laboratory material handling system. The virtual model created in Tecnomatix Plant Simulation is connected to the physical system controlled by a Siemens PLC SIMATIC S7-1200 and equipped with industrial sensors and an HMI interface. Real-time bidirectional communication is established via the OPC UA protocol using KEPServerEX, ensuring synchronisation between the physical and virtual systems. Experiments confirmed the functional synchronisation of both systems. Additionally, the study presents that DT technology can be adapted for educational purposes and implemented in engineering education. Full article

To enhance the measuring capabilities of modern Non-Destructive Evaluation (NDE) devices, it has become essential to integrate standardized digitization services and industry-compliant functionalities. This perspective paper examines approaches for improving NDE systems by incorporating key Industry 4.0 technologies, specifically digital representations such as the Asset Administration Shell (AAS) and OPC UA (Open Platform Communications Unified Architecture). We discuss requirements for interoperable, semantically rich descriptions of NDE systems, outline how OPC UA information models and AAS submodels can be combined with MQTT-based transport, and illustrate these concepts through representative prototype implementations, including predictive maintenance and chatbot assistant use cases. By leveraging these technologies, NDE devices can be transformed into interoperable, data-rich, and intelligent components within smart industrial ecosystems. Compared with previous studies, this Perspective is the first to systematically bring together the requirements, architectural patterns, and evaluation criteria for digital representations designed specifically for NDE systems. It also provides, in a practical and accessible way, NDE-focused OPC UA and AAS-based architectures that support both predictive maintenance and LLM-assisted operator guidance. The presented implementations are at an early stage and serve as illustrative examples, while systematic quantitative validation is ongoing and is outlined as future work. Full article