Search Results (75)

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

The growing demand for autonomous and energy-efficient telemetry systems in Industrial Internet of Things (IIoT) applications highlights the limitations of GSM-dependent infrastructure. This research proposes and validates a secure and infrastructure-independent telemetry transmission architecture based on Raspberry Pi and LoRa technology. The system integrates lightweight symmetric encryption (AES-128 with CRC-8) and local data processing, enabling long-range communication without reliance on cellular networks or cloud platforms. A fully functional prototype was developed and tested in real urban environments with high electromagnetic interference. The experimental evaluation was conducted over distances ranging from 10 to 1100 m, focusing on the Packet Delivery Ratio (PDR), Packet Error Rate (PER), and Packet Loss Rate (PLR). Results demonstrate reliable communication up to 200 m and high long-term stability, with a 24 h continuous transmission test achieving a PDR of 97.5%. These findings confirm the suitability of the proposed architecture for secure, autonomous IIoT deployments in infrastructure-limited and noisy environments. Full article

5G private mobile networks are becoming a platform for ‘wire-free’ networking for professional applications in smart industry sectors, such as automated warehousing, logistics, autonomous vehicle deployments in campus environments, mining, material processing, and more. It is expected that most of these Machine-to-Machine (M2M) and Industrial Internet of Things (IIoT) communication paths will be realized wirelessly, as the advantages of providing flexibility are obvious compared to hard-wired network installations. Unfortunately, the deployment of private 5G networks in smart industries has faced delays due to a combination of high costs, technical challenges, and uncertain returns on investment, which is reflected in troublesome access to fully operational private networks. To obtain insight into occupational exposure to radiofrequency electromagnetic fields (RF EMF) emitted by 5G private mobile networks, an analysis of RF EMF due to different types of 5G equipment was carried out on a real case scenario in the production and logistic (warehouse) industrial sector. A private standalone (SA) 5G network operating at 3.7 GHz in a real industrial environment was numerically modeled and compared with in situ RF EMF measurements. The results show that RF EMF exposure of the workers was far below the existing exposure limits due to the relatively low power (1 W) of indoor 5G base stations in private networks, and thus similar exposure scenarios could also be expected in other deployed 5G networks. In the analyzed RF EMF exposure scenarios, the radio transmitter—so-called ‘radio head’—installation heights were relatively low, and thus the obtained results represent the worst-case scenarios of the workers’ exposure that are to be expected due to private 5G networks in smart industries. Full article

In Supervisory Control and Data Acquisition (SCADA) systems, central to industrial automation and control systems, the Front-end Processor (FEP) facilitates seamless communication between field control devices and central management systems. As the Industrial Internet of Things (IIoT) and Industry 4.0 centered on the smart factory paradigm gain traction, conventional FEPs are increasingly showing limitations in various aspects. To address these issues, Data Distribution Service, a real-time communication middleware, and Kubernetes, a container orchestration platform, have garnered attention. However, the effective integration of conventional SCADA protocols, such as DNP3.0, IEC 61850, and Modbus with DDS, remains a key challenge. Therefore, this article proposes a Kubernetes-based real-time FEP for the modernization of SCADA systems. The proposed FEP ensures interoperability through an efficient translation mechanism between traditional SCADA protocols—DNP3.0, IEC 61850, and Modbus—and the Data Distribution Service protocol. In addition, the performance evaluation shows that the FEP achieves high throughput and sub-millisecond latency, confirming its suitability for real-time industrial control applications. This approach overcomes the limitations of conventional FEPs and enables the realization of more flexible and scalable industrial control systems. However, further research is needed to validate the system under large-scale deployment scenarios and enhance security capabilities. Future work will focus on performance evaluation in realistic conditions and the integration of quantum-resistant security mechanisms to strengthen resilience in critical infrastructure environments. Full article

Recently, a new concept has emerged for the development of Industrial Internet of Things (IIoT) applications, the Industrializable Industrial Internet of Things (I3oT). As a criterion for the design of industrial applications, the I3oT imposes the exclusive use of pre-installed elements in the company such as PLCs, sensors, IT/OT networks, etc., trying to minimize the impact on the factories and guaranteeing a cheap and assumable scalability for companies, something that cannot be implemented with the vast majority of IIoT applications available in the market. In our previous work, we have used I3oT applications for predictive maintenance on different components: cylinders, presses, welding clamps and also energy-saving tools, detection of bottlenecks and sub-bottlenecks, etc., all of them generalized for the entire factory. However, the main drawback comes from the flow of data through the IT/OT network. This article presents the Cross-PLC, a tool to allow massive data extraction using the company’s IT/OT network by communicating with any type of PLC or brand existing in the market. The Cross-PLC performs passive listening, and through different communication criteria, the Cross-PLC becomes a virtual PLC containing all the parameters necessary for the I3oT applications developed. This article presents the design of this tool, its implementation and use at Ford Factory in Almussafes (Valencia). Full article

This paper presents an advanced framework for real-time monitoring and analysis of network traffic and endpoint security in large-scale enterprises by addressing the increasing complexity and frequency of cyber-attacks. Our Network Security Traffic Analysis Platform employs a comprehensive technology stack including the Elastic Stack, ZEEK, Osquery, Kafka, and GeoLocation data. By integrating supervised machine learning models trained on the UNSW-NB15 dataset, we evaluate Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM), with the Random Forest classifier achieving a notable accuracy of 99.32%. Leveraging Artificial Intelligence and Natural Language Processing, we apply the BERT model with a Byte-level Byte-pair tokenizer to enhance network-based attack detection in IoT systems. Experiments on UNSW-NB15, TON-IoT, and Edge-IIoT datasets demonstrate our platform’s superiority over traditional methods in multi-class classification tasks, achieving near-perfect accuracy on the Edge-IIoT dataset. Furthermore, Network Security Traffic Analysis Platform’s ability to produce actionable insights through charts, tables, histograms, and other visualizations underscores its capability in static analysis of traffic data. This dual approach of real-time and static analysis provides a robust foundation for developing scalable, efficient, and automated security solutions, essential for managing the evolving threats in modern networks. Full article

Modern manufacturing systems are influenced by the growing complexity of mechatronics, control systems, IIoT, and communication technologies integrated into cyber-physical systems. These systems demand flexibility, modularity, and rapid project execution, making digital tools critical for their design. Virtual commissioning, based on digital twins, enables the testing and validation of control systems and designs in virtual environments, reducing risks and accelerating time-to-market. This research explores the development of digital twin models to bridge the gap between simulation and real-world validation. The models identify design flaws, validate the PLC control code, and ensure interoperability across software platforms. A case study involving a modular Festo manufacturing system modelled in Tecnomatix Process Simulate demonstrates the ability of digital twins to detect inefficiencies, such as collision risks, and to validate automation systems virtually. This study highlights the advantages of virtual commissioning for optimizing manufacturing systems. Communication testing showed compatibility across platforms but revealed limitations with certain data types due to software constraints. This research provides practical insights into creating robust digital twin models, improving the flexibility, efficiency, and quality of manufacturing system design. It also offers recommendations to address current challenges in interoperability and system performance. Full article

The convergence of cloud computing and the Industrial Internet of Things (IIoT) has significantly transformed industrial operations, enabling intelligent, scalable, and efficient systems. This survey provides a comprehensive analysis of the role cloud computing plays in IIoT ecosystems, focusing on its architectural frameworks, service models, and application domains. By leveraging centralized, edge, and hybrid cloud architectures, IIoT systems achieve enhanced real-time processing capabilities, streamlined data management, and optimized resource allocation. Moreover, this study delves into integrating artificial intelligence (AI) and machine learning (ML) in cloud platforms to facilitate predictive analytics, anomaly detection, and operational intelligence in IIoT environments. Security challenges, including secure device-to-cloud communication and privacy concerns, are addressed with innovative solutions like blockchain and AI-powered intrusion detection systems. Future trends, such as adopting 5G, serverless computing, and AI-driven adaptive services, are also discussed, offering a forward-looking perspective on this rapidly evolving domain. Finally, this survey contributes to a well-rounded understanding of cloud computing’s multifaceted aspects and highlights its pivotal role in driving the next generation of industrial innovation and operational excellence. Full article

This narrative review evaluates the curtail components of data maturity in manufacturing industries, the associated challenges, and the application of industrial data science (IDS) to improve organisational decision-making. As data availability grows larger, manufacturing organisations face difficulties comprehending heterogeneous datasets of varying quality, which may lead to inefficient decision-making and other operational inefficiencies. It underlines that data appropriate for its intended application is considered quality data. The importance of including stakeholders in the data review process to enhance the data quality is accentuated in this paper, specifically when big data analysis is to be integrated into corporate strategies. Manufacturing industries leveraging their data thoughtfully can optimise efficiency and facilitate informed and productive decision-making by establishing a robust technical infrastructure and developing intuitive platforms and solutions. This study highlights the significance of IDS in revolutionising manufacturing sectors within the framework of Industry 4.0 and the Industrial Internet of Things (IIoT), demonstrating that big data can substantially improve efficiency, reduce costs, and guide strategic decision-making. The gaps or maturity levels among industries show a substantial discrepancy in this analysis, which is classified into three types: Industry 4.0 maturity levels, data maturity or readiness condition index, and industrial data science and analytics maturity. The emphasis is given to the pressing need for resilient data science frameworks enabling organisations to evaluate their digital readiness and execute their data-driven plans efficiently and effortlessly. Simultaneously, future work will focus on pragmatic applications to enhance industrial competitiveness within the heavy machinery sector. Full article

The Internet of Things (IoT), namely, the set of intelligent devices equipped with sensors and actuators and capable of connecting to the Internet, has now become an integral part of the most competitive industries, as it enables optimization of production processes and reduction in operating costs and maintenance time, together with improving the quality of products and services. More specifically, the term Industrial Internet of Things (IIoT) identifies the system which consists of advanced Internet-connected equipment and analytics platforms specialized for industrial activities, where IIoT devices range from small environmental sensors to complex industrial robots. This paper presents an integrated high-level SDN-NFV architecture enabling clusters of smart devices to interconnect and manage the exchange of data with distributed control processes and databases. In particular, it is focused on 5G RAN-MEC slice management in the IIoT context. The proposed system is emulated by means of two distinct real-time frameworks, demonstrating improvements in connectivity, energy efficiency, end-to-end latency and throughput. In addition, its scalability, modularity and flexibility are assessed, making this framework suitable to test advanced and more applications. Full article

This paper presents a novel waste-heat-powered, wireless, and battery-less Industrial Internet of Things (IIoT) device designed for predictive maintenance in Industry 4.0 environments. With a focus on real-time quality data, this device addresses the limitations of current battery-operated IIoT devices, such as energy consumption, transmission range, data rate, and constant quality of service. It is specifically developed for heat-intensive industries (e.g., iron and steel, cement, petrochemical, etc.), where self-heating nodes, low-power processing platforms, and industrial sensors align with the stringent requirements of industrial monitoring. The presented IIoT device uses thermoelectric generators based on the Seebeck effect to harness waste heat from any hot surface, such as pipes or chimneys, ensuring continuous power without the need for batteries. The energy that is recovered can be used to power devices using mid-range wireless protocols like Bluetooth 5.0, minimizing the need for extensive in-house wireless infrastructure and incorporating light-edge computing. Consequently, up to 98% of cloud computation efforts and associated greenhouse gas emissions are reduced as data is processed within the IoT device. From the environmental perspective, the deployment of such self-powered IIoT devices contributes to reducing the carbon footprint in energy-demanding industries, aiding their digitalization transition towards the industry 5.0 paradigm. This paper presents the results of the most challenging energy harvesting technologies based on an all-silicon micro thermoelectric generator with planar architecture. The effectiveness and self-powering ability of the selected model, coupled with an ultra-low-power processing platform and Bluetooth 5 connectivity, are validated in an equivalent industrial environment to monitor vibrations in an electric machine. This approach aligns with the EU’s strategic objective of achieving net zero manufacturing capacity for renewable energy technologies, enhancing its position as a global leader in renewable energy technology (RET). Full article

With the rapid development of the Internet of Things (IoT), the growth of the Industrial Internet of Things (IIoT) applied in the industrial sector has also been swift. However, in practical applications, there are still issues such as the misalignment between theory and application, the lack of a unified standardization framework, and the frequent occurrence of data silos. These issues limit the maintainability and scalability of IIoT systems and increase the digitalization costs for enterprises. Based on this, drawing from the design principles of classic general-purpose operating systems, we propose the concept of an IoT operating system platform. As a software infrastructure aimed at the next generation of IIoT, the IoT OS platform consists of general-purpose computer operating systems and the platform software running on them. It manages computing resources and entities such as sensors, networks, and ubiquitous artificial intelligence applications or systems, and provides service support for IIoT applications upwards, aiming to improve existing issues and enhance the specificity and scalability of IIoT systems. This paper presents the current status of IIoT systems, the definition and architecture of the IoT OS platform, and validates the theoretical architecture through specific cases. Full article

The integration of Artificial Intelligence (AI) models in Industrial Internet of Things (IIoT) systems has emerged as a pivotal area of research, offering unprecedented opportunities for optimizing industrial processes and enhancing operational efficiency. This article presents a comprehensive review of state-of-the-art AI models applied in IIoT contexts, with a focus on their utilization for fault prediction, process optimization, predictive maintenance, product quality control, cybersecurity, and machine control. Additionally, we examine the software and hardware tools available for integrating AI models into embedded platforms, encompassing solutions such as Vitis AI v3.5, TensorFlow Lite Micro v2.14, STM32Cube.AI v9.0, and others, along with their supported high-level frameworks and hardware devices. By delving into both AI model applications and the tools facilitating their deployment on low-power devices, this review provides a holistic understanding of AI-enabled IIoT systems and their practical implications in industrial settings. Full article

The research and management of Industry 4.0 increasingly relies on accurate real-time quality data to apply efficient algorithms for predictive maintenance. Currently, Low-Power Wide-Area Networks (LPWANs) offer potential advantages in monitoring tasks for predictive maintenance. However, their applicability requires improvements in aspects such as energy consumption, transmission range, data rate and constant quality of service. Commonly used battery-operated IIoT devices have several limitations in their adoption in large facilities or heat-intensive industries (iron and steel, cement, etc.). In these cases, the self-heating nodes together with the appropriate low-power processing platform and industrial sensors are aligned with the requirements and real-time criteria required for industrial monitoring. From an environmental point of view, the carbon footprint associated with human activity leads to a steady rise in global average temperature. Most of the gases emitted into the atmosphere are due to these heat-intensive industries. In fact, much of the energy consumed by industries is dissipated in the form of waste heat. With this scenario, it makes sense to build heat transformation collection systems as guarantors of battery-free self-powered IIoT devices. Thermal energy harvesters work on the physical basis of the Seebeck effect. In this way, this paper gathers the methodology that standardizes the modelling and simulation of waste heat recovery systems for IoT nodes, gathering energy from any hot surface, such as a pipe or chimney. The statistical analysis is carried out with the data obtained from two different IoT architectures showing a good correlation between model simulation and prototype behaviour. Additionally, the selected model will be coupled to a low-power processing platform with LoRaWAN connectivity to demonstrate its effectiveness and self-powering ability in a real industrial environment. Full article

Ensuring fairness and equity in value distribution is crucial for the sustainability of platform ecosystems. However, existing approaches to distributing benefits among cooperative entities often find it difficult to accurately assess each stakeholder’s contributions. This paper tackles this challenge through a case study of the Haier COSMOPlat IIoT platform. By analyzing its value creation and value distribution processes, the research uncovers how platform enterprises can overcome existing limitations by quantifying and revealing intangible customer relationships alongside financial metrics. This revised value-sharing mechanism encourages a shift from “post-event value-sharing” to “mid-event adjustment”, promoting a fair and equitable profit distribution framework that motivates stakeholders toward sustainable value co-creation. Full article