Search Results (60)

Material tracking and localization are key applications of Industry 4.0 in manufacturing process control. Traditional approaches—such as barcode or QR code identification and RTLS-based localization using RF/UWB, 5G or GPS–require a large and complex infrastructure. As an alternative, this paper proposes an IoT-based solution that combines short-range Bluetooth Low Energy (BLE) communication with LPWAN LoRaWAN networks. Hybrid solutions using LoRaWAN and BLE technologies already exist, but pure localization based on BLE tags can lead to ambiguous asset identification in geometrically dense scenarios. Our paper aims to solve this problem with an alternative concept called Associated LoRaWAN Sensors (ALSs). An ALS enables logical grouping and integration of heterogeneous LoRaWAN sensors, providing their own data or directly scanning BLE tags. Sensor data can be combined and supplemented with new information, data, and events, supported by application logic (use case). Although ALS represents a general concept that could be applicable to various use cases (such as warehouse monitoring, object tracking), our paper will focus mainly on material tracking and validation in manufacturing. For this purpose, we designed a specific ALS model that integrates a classic LoRaWAN BLE sensor with an additional LoRaWAN magnetic contact sensor. The magnetic contact switch can provide validation of exact position, in addition to localization by BLE tag. Experimental validation using BLE tags (Trax 10229) and LoRaWAN sensors (IoTracker3, Milesight WS301) demonstrates the usability of the ALS model in typical industrial scenarios. We also measured RSSI and evaluated the accuracy of tag localization (3 × 25 = 75 tests) for the worst-case scenario: material validation on a machine with a BLE tag distance of ~0.5 m. While the traditional approach showed up to a 20% failure rate, our ALS model avoided the issue of incorrect accuracy. An additional magnetic switch in ALS confirmed that the correct carrier with the associated tag is attached to the machine and eliminated incorrect localization. The results confirm that a hybrid model based on BLE and LoRaWAN scanning can reliably support material localization and validation without the need for dense RTLS infrastructures. Full article

Industrial Control Systems (ICS) are fundamental to the operation, monitoring, and automation of critical infrastructure in sectors such as energy, water utilities, manufacturing, transportation, and oil and gas. According to the Purdue Model, ICS encompasses tightly coupled OT and IT layers, becoming increasingly interconnected. Smart grids represent a critical class of ICS; thus, this survey examines encryption and relevant protocols in smart grid communications, with findings extendable to other ICS. Encryption techniques implemented at both the protocol and network layers are among the most effective cybersecurity strategies for protecting communications in increasingly interconnected ICS environments. This paper provides a comprehensive survey of encryption practices within the smart grid as the primary ICS application domain, focusing on protocol-level solutions (e.g., DNP3, IEC 60870-5-104, IEC 61850, ICCP/TASE.2, Modbus, OPC UA, and MQTT) and network-level mechanisms (e.g., VPNs, IPsec, and MACsec). We evaluate these technologies in terms of security, performance, and deployability in legacy and heterogeneous systems that include renewable energy resources. Key implementation challenges are explored, including real-time operational constraints, cryptographic key management, interoperability across platforms, and alignment with NERC CIP, IEC 62351, and IEC 62443. The survey highlights emerging trends such as lightweight Transport Layer Security (TLS) for constrained devices, post-quantum cryptography, and Zero Trust architectures. Our goal is to provide a practical resource for building resilient smart grid security frameworks, with takeaways that generalize to other ICS. Full article

The exponential proliferation of the Internet of Things (IoT) has catalyzed a paradigm shift in industrial automation and smart city infrastructure. However, this rapid expansion has engendered significant heterogeneity in communication protocols, creating critical barriers to seamless data integration and interoperability. Conventional gateway solutions frequently exhibit limited flexibility in supporting diverse protocol stacks simultaneously and often lack granular user controllability. To mitigate these deficiencies, this paper proposes a novel, modular IoT gateway architecture, designated as MIGS (Modular IoT Gateway System). The proposed architecture comprises four distinct components: a Management Component, a Southbound Component, a Northbound Component, and a Cache Component. Specifically, the Southbound Component employs instance-based isolation and independent task threading to manage heterogeneous field devices utilizing protocols such as Modbus, MQTT, and OPC UA. The Northbound Component facilitates reliable bidirectional data transmission with cloud platforms. A dedicated Cache Component is integrated to decouple data acquisition from transmission, ensuring data integrity during network latency. Furthermore, a web-based Control Service Module affords comprehensive runtime management. We explicate the data transmission methodology and formulate a theoretical latency model to quantify the impact of the Python Global Interpreter Lock (GIL) and serialization overhead. Functional validation and theoretical analysis confirm the system’s efficacy in concurrent multi-protocol communication, robust data forwarding, and operational flexibility. The MIGS framework significantly enhances interoperability within heterogeneous IoT environments, offering a scalable solution for next-generation industrial applications. Full article

The Internet of Things (IoT) connects billions of heterogeneous devices, necessitating lightweight, efficient, and secure communication protocols to support a diverse range of use cases. While physical and network-layer technologies enable connectivity, transport and application-layer protocols determine how IoT devices exchange, manage, and secure information. The diverse and constrained nature of IoT devices presents a challenge in selecting appropriate communication protocols, with no one-size-fits-all solution existing. This article provides a comprehensive review of key transport and application protocols in IoT, including MQTT, MQTT-SN, CoAP, LwM2M, AMQP, XMPP, WebSockets, HTTP/HTTPS, and OPC UA. Each protocol is examined in terms of its design principles, communication patterns, reliability mechanisms, and security features. The discussion highlights their suitability for different deployment scenarios, ranging from resource-constrained sensor networks to industrial automation and cloud-integrated consumer devices. By mapping protocol characteristics to IoT requirements, such as scalability, interoperability, power efficiency, and manageability, the article provides guidelines for selecting the optimal protocol stack to optimize IoT system performance and long-term sustainability. Our analysis reveals that while MQTT dominates cloud telemetry, CoAP and LwM2M are superior in IP-based constrained networks, and emerging solutions like OSCORE are critical for end-to-end security. Full article

Accessing operational information across industrial systems (ERP, MES, SCADA, PLC) in concrete plants requires 15–30 min and specialized knowledge. This work addresses this accessibility gap by developing a conversational AI system that democratizes industrial information access through natural language. A five-layer cognitive architecture was implemented integrating the Mistral-7B model quantized in GGUF Q4_0 format (3.82 GB) on a Raspberry Pi 5, Spanish speech recognition/synthesis, and heterogeneous industrial protocols (OPC UA, MQTT, REST API) across all automation pyramid levels. Experimental validation at Frumecar S.L. (Murcia, Spain) characterized performance, thermal stability, and reliability. Results show response times of 14.19 s (simple queries, SD = 7.56 s), 16.45 s (moderate, SD = 6.40 s), and 23.24 s (complex multilevel, SD = 6.59 s), representing 26–77× improvement over manual methods. The system maintained average temperature of 69.3 °C (peak 79.6 °C), preserving 5.4 °C margin below throttling threshold. Communication latencies averaged 8.93 ms across 10,163 readings (<1% of total latency). During 30 min of autonomous operation, 100% reliability was achieved with 39 successful queries. These findings demonstrate the viability of deploying quantized LLMs on low-cost edge hardware, enabling cognitive democratization of industrial information while ensuring data privacy and cloud independence. Full article

Electronic communication and industrial protocols are critical to the reliable operation of modern electrical grids and Distributed Energy Resources (DERs). Communication loss between devices in renewable power plants can lead to significant revenue losses and jeopardize operational safety. While current control and automation systems for renewable plants are primarily based on the IEC 61131-3 standard, it lacks defined communication frameworks, leading most deployments to depend on Original Equipment Manufacturer (OEM)-specific protocols. The IEC 61499 standard, in contrast, offers a reference model for distributed automation systems, introducing Service Interface Function Blocks (SIFBs) and high-level communication abstractions that enable hardware-independent integration. This study proposes adopting the IEC 61499 standard for DER automation systems to enhance interoperability and flexibility among plant components. A photovoltaic power plant gateway is developed on a virtualized platform using open-source tools and libraries, including Python version 3, libmodbus version 3.1.7, and open62541 version 1 The implemented gateway successfully interfaces with industry-validated software applications, including UAExpert and Matrikon OPC Unified Architecture (OPC UA) clients, demonstrating the feasibility and effectiveness of IEC 61499-based integration in DER environments. Full article

Stealth attacks targeting industrial control systems (ICS) exploit subtle sequences of malicious actions, making them difficult to detect with conventional methods. The OPC Unified Architecture (OPC UA) protocol—now widely adopted in SCADA/ICS environments—enhances OT–IT integration but simultaneously increases the exposure of critical infrastructures to sophisticated cyberattacks. Traditional detection approaches, which rely on instantaneous traffic features and static models, neglect the sequential dimension that is essential for uncovering such gradual intrusions. To address this limitation, we propose a hybrid sequential anomaly detection pipeline that combines Markov chain modeling to capture temporal dependencies with machine learning algorithms for anomaly detection. The pipeline is further augmented by explainability through SHapley Additive exPlanations (SHAP) and causal inference using the PC algorithm. Experimental evaluation on an OPC UA dataset simulating Man-In-The-Middle (MITM) and denial-of-service (DoS) attacks demonstrates that incorporating a second-order sequential memory significantly improves detection: F1-score increases by +2.27%, precision by +2.33%, and recall by +3.02%. SHAP analysis identifies the most influential features and transitions, while the causal graph highlights deviations from the system’s normal structure under attack, thereby providing interpretable insights into the root causes of anomalies. Full article

This systematic literature review examines the integration of Machine Learning techniques within industrial system architectures using OPC-UA for process automation. Through analyzing primary studies published between 2018 and 2024, the review identifies key trends, methodologies, and implementations across various industrial applications. The review identifies a marked increase in research focused on hybrid architectures that integrate Machine Learning with OPC-UA, particularly in applications such as predictive maintenance and quality control. However, despite reported high accuracy rates—often above 95%—in controlled environments, there is limited evidence on the robustness of these solutions in real-world, large-scale deployments. This highlights the need for further empirical validation and benchmarking in diverse industrial contexts. Implementation patterns range from cloud-based deployments to edge computing solutions, with OPC-UA serving as a communication protocol, information modeling framework, and specifically using the finite state machine specification. The review also highlights current challenges and opportunities, providing valuable insights for researchers and practitioners working on intelligent industrial automation. Full article

This paper presents the development and implementation of a shopfloor visualization-oriented digitalization framework for heterogeneous industrial equipment, aimed to enhance sustainable performance in manufacturing environments. The proposed solution addresses a critical challenge in modern industry: the integration of legacy and modern equipment into a unified, real-time monitoring and control system. In this paper, a modular and scalable architecture that enables data acquisition from equipment with varying communication protocols and technological maturity was designed and implemented, utilizing Industrial Internet of Things (IIoT) gateways, protocol converters, and Open Platform Communications Unified Architecture (OPC UA). A key contribution of this work is the integration of various data sources into a centralized visualization platform that supports real-time monitoring, anomaly detection, and performance analytics. By visualizing operational parameters—including energy consumption, machine efficiency, and environmental indicators—the system facilitates data-driven decision-making and supports predictive maintenance strategies. The implementation was validated in a real industrial setting, where the solution significantly improved transparency, reduced downtime, and contributed to measurable energy efficiency gains. This research demonstrates that visualization-oriented digitalization not only enables interoperability among heterogeneous assets, but also acts as a catalyst for achieving sustainability goals. The developed methodology and tools provide a replicable model for manufacturing organizations seeking to transition toward Industry 4.0 in a resource-efficient and future-proof manner. Full article

With the rapid development of the Industrial Internet of Things (IIoT), the scale of industrial equipment has expanded, leading to an increasing diversity of communication protocols and a significant rise in data transmission volume within industrial networks. Traditional communication systems, constrained by concurrency and throughput limitations, struggle to meet the demands of massive data transmission. To address this issue, this paper proposes a distributed high-availability message communication model for IIoT (DUA-MQTT) based on the OPC UA architecture. It integrates the distributed MQTT protocol to enhance concurrency and throughput performance. Additionally, to improve the information processing capability of the proposed model, this paper designs an information-modeling model based on industrial unstructured text data (MAC-GC), which generates structured data nodes that comply with the OPC UA information model specification through hierarchical annotation, accurately mapping device functions and attributes. Experimental results show that, compared with traditional communication models, the DUA-MQTT model reduces end-to-end latency by 28.6% and increases throughput by 22.2%, effectively enhancing the concurrency of data transmission. In terms of information-modeling capabilities, MAC-GC outperforms other models in accuracy (0.9701), recall (0.9601), and F1 score (0.9651), effectively improving the utilization efficiency and modeling accuracy of unstructured data. Full article

With the advent of Industry 4.0, the manufacturing industry is facing unprecedented data challenges. Sensors, PLCs, and various types of automation equipment in smart factories continue to generate massive amounts of heterogeneous data, but existing systems generally have bottlenecks in data collection standardization, real-time processing capabilities, and system scalability, which make it difficult to meet the needs of efficient collaboration and dynamic decision making. This study proposes a multi-level industrial data processing framework based on edge computing that aims to improve the response speed and processing ability of manufacturing sites to data and to realize real-time decision making and lean management of intelligent manufacturing. At the edge layer, the OPC UA (OPC Unified Architecture) protocol is used to realize the standardized collection of heterogeneous equipment data, and a lightweight edge-computing algorithm is designed to complete the analysis and processing of data so as to realize a visualization of the manufacturing process and the inventory in a production workshop. In the storage layer, Apache Kafka is used to implement efficient data stream processing and improve the throughput and scalability of the system. The test results show that compared with the traditional workshop, the framework has excellent performance in improving the system throughput capacity and real-time response speed, can effectively support production process judgment and status analysis on the edge side, and can realize the real-time monitoring and management of the entire manufacturing workshop. This research provides a practical solution for the industrial data management system, not only helping enterprises improve the transparency level of manufacturing sites and the efficiency of resource scheduling but also providing a practical basis for further research on industrial data processing under the “edge-cloud collaboration” architecture in the academic community. Full article

A digital twin is an effective tool for the design, testing, and validation of control strategies for electro-pneumatic actuators in industrial automation. This study presents and compares three different digital twin models of a pneumatic cylinder with varying levels of physical fidelity—from basic discrete control, through analogue control without pneumatic dynamics, to a complex model simulating pressure, friction, and airflow. The experiments were conducted using the Siemens NX Mechatronics Concept Designer, integrated with the SIMIT emulation platform and a PLC control system via the standardized OPC UA protocol. The main objective was to evaluate simulation accuracy, model flexibility for testing various control strategies, and the ability of the digital twin to reflect changes in PLC algorithms. The results showed that while simple models are suitable for verifying basic logic, only advanced models can realistically replicate the dynamic behaviour of pneumatic systems, including delay phases and pressure influence. A comparison with the experimental study by Jiménez confirmed a strong correlation between the simulated and actual actuator behaviour. In future work, the developed control algorithm will be connected to a physical cylinder to further validate the models and refine control strategies under real-world conditions. Full article

This study addresses the challenges associated with virtual–real interactions, the limitations of one-dimensional data presentation, restricted real-time functionalities, and the lack of effective models for monitoring production line status. It specifically investigates intelligent production lines for automotive wheels as the focal point of the research. This study explores the construction methodology and the application of intelligent production lines through the utilization of digital twin technology. A hierarchical design approach is employed, integrating industrial Internet of Things (IoT) technology to create a comprehensive digital twin system. This system consists of four layers: the physical production line layer, the data acquisition and processing layer, the digital twin production line layer, and the application service layer. Precise mapping from the physical production line to the digital twin model is achieved using the advanced 3D modeling and simulation software, PQ Factory, while real-time data collection and transmission are facilitated through the standardized OPC UA protocol. The effectiveness of the system is substantiated through a detailed case study. The findings demonstrate that the intelligent production line system, which leverages digital twin technology for automotive wheels, enables real-time monitoring of the production process and provides innovative solutions, along with a robust theoretical framework for comprehensive analysis, diagnosis, evaluation, optimization, prediction, and decision making in the production of automotive wheels. Full article

This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward. Full article

This paper introduces a novel approach to frequency regulation in stand-alone synchronous generators by combining particle swarm optimization (PSO) with a Fuzzy PID controller. This study compares three control methods: a programmable logic controller (PLC)-based PID, a Fuzzy PID, and a PSO-Fuzzy PID controller. An experimental setup is implemented using real physical equipment, including an asynchronous motor, a synchronous generator, and various power and control components. The system is monitored and controlled in real-time via an S7-1215 PLC with the TIA Portal V17 interface, and the controllers are designed using MATLAB/Simulink. PLC-MATLAB communication is implemented using the KEPServerEX interface and the OPC UA protocol. The PSO-Fuzzy PID controller demonstrates superior performance, reducing overshoot, undershoot, and settling time compared to the other methods. These results highlight the effectiveness and real-time applicability of the PSO-Fuzzy PID controller for industrial frequency control, especially under varying load conditions and the nonlinear characteristics of the synchronous generator. Full article