Industrial Internet Of Things Research Articles

Cloud and On-premises Based Security Solutionfor Industrial IoT

Cloud and On-premises Based Security Solutionfor Industrial IoT

An Analytical Review on Predictive Fault Tolerance for Industrial IoT Using Advanced Learning Techniques

This article presents a thorough examination of hybrid machine learning methodologies employed to improve fault tolerance in Industrial Internet of Things (IIoT) systems. These hybrid models enhance fault detection precision and resilience by integrating various machine learning techniques, surpassing conventional methods. The research commences with a comprehensive literature review of contemporary fault tolerance techniques, subsequently presenting a comparison of various datasets and a proposal for hybrid predictive algorithms. The efficacy of these algorithms is subsequently assessed in comparison to cutting-edge techniques. Principal findings underscore the enhanced precision of hybrid models in real-time applications, their diminished computing complexity, and their scalability in dynamic industrial settings. Nonetheless, issues pertaining to integration, data reliance, and real-time implementation are also addressed. The article concluded by outlining prospective research methods, encompassing sophisticated integration techniques, automated model optimisation, adaptive self-learning frameworks, and methodologies to safeguard data privacy. This paper outlines a framework for future advancements in fault tolerance, with considerable implications for both academia and industry.

Development of a Digital Twin Driven by a Deep Learning Model for Fault Diagnosis of Electro-Hydrostatic Actuators

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration.

The Method of Diversity-Based Ensuring the Reliability of the Router in the IIoT System

The architecture of the Industrial Internet of Things (IIoT) system assumes the presence of a router as the main subsystem, which will allow connecting the subsystems into a single network and connecting them to the equipment of the Internet service provider. Failure of the router can lead to downtime of industrial equipment and the whole workshop at the industrial enterprise, and to the failure of the IIoT system as a whole. Control processing units of industrial routers (RCPU) are complex microprocessor systems (MS). Violation of their operability can be caused by both hardware (HW) and software (SW) failures. Therefore, to increase the reliability of RCPU it is reasonable to use such methods of reliability assurance as HW redundancy (duplication of processors with comparison of results), as well as the use of modern means of control and diagnostics. However, the number of HW and SW failures in RCPUs, is still quite high. In this connection, the solution of issues related to the development of methods for ensuring high reliability of RCPU is of great urgency. This paper proposes a method of ensuring the reliability of RCPU using SW diversity on the basis of priority series. The method let to choose the variant of redundancy of HW and the number of versions of SW as diversity in RCPU.

Serverless Computing in the Edge-Cloud Continuum : Challenges, Opportunities, and a Novel Framework

This article explores the integration of serverless computing across the edge-cloud continuum, addressing the growing demand for low-latency, data-intensive applications in the era of the Internet of Things (IoT). We present EdgeServe, a novel framework that extends the serverless paradigm to encompass edge devices and cloud resources, overcoming the limitations of traditional cloud-centric approaches. Through comprehensive simulations and real-world case studies, including a smart city traffic management system, an industrial IoT predictive maintenance application, and a mobile augmented reality gaming platform, we evaluate the performance, scalability, and cost-effectiveness of our proposed framework. Our results demonstrate significant improvements in application response times, with latency reductions of up to 82% in time-critical functions, and an average cost reduction of 43% compared to cloud-only serverless deployments. We also observe a 28% decrease in overall energy consumption in certain scenarios. The article addresses key challenges in resource management, data consistency, adaptive function placement, and security in distributed environments. Our findings have important implications for application architects and cloud service providers, paving the way for a new generation of edge-native serverless platforms. This research contributes to the growing body of knowledge on distributed systems and offers insights into the future evolution of serverless computing in heterogeneous computing environments.

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

Establish intelligent fault detection in electrical power system: evolutionary computation in industrial IoT environments

An intelligent native network slicing security architecture empowered by federated learning

Network Slicing (NS) has transformed the landscape of resource sharing in networks, offering flexibility to support services and applications with highly variable requirements in areas such as the next-generation 5G/6G mobile networks (NGMN), vehicular networks, industrial Internet of Things (IoT), and verticals. Although significant research and experimentation have driven the development of network slicing, existing architectures often fall short in intrinsic architectural intelligent security capabilities. This paper proposes an architecture-intelligent security mechanism to improve the NS solutions. We idealized a security-native architecture that deploys intelligent microservices as federated agents based on machine learning, providing intra-slice and architectural operation security for the Slicing Future Internet Infrastructures (SFI2) reference architecture. It is noteworthy that federated-learning approaches match the highly distributed modern microservice-based architectures, thus providing a unifying and scalable design choice for NS platforms addressing both service and security. Using ML-Agents and Security Agents, our approach identified Distributed Denial-of-Service (DDoS) and intrusion attacks within the slice using generic and non-intrusive telemetry records, achieving an average accuracy of approximately 95.60% in the network slicing architecture and 99.99% for the deployed slice – intra-slice. This result demonstrates the potential for leveraging architectural operational security and introduces a promising new research direction for network slicing architectures.

Proposed Modbus Extension Protocol and Real-Time Communication Timing Requirements for Distributed Embedded Systems

The general evolution of fieldbus systems has been variously affected by both computer electrical engineering and science. First, the main contribution undoubtedly originated from network IT systems, when the Open Systems Interconnection model was presented. This reference model with seven layers was and remains the foundation for the development of numerous advanced communication protocols. In this paper, the conducted research resulted in a major contribution; specifically, it describes the mathematical model for the Modbus protocol and defines the acquisition cycle model that corresponds to incompletely defined protocols in order to provide a timestamp and achieve temporal consistency for proposed Modbus Extension. The derived technical contribution of the authors is to exemplify the functionality of a typical industrial protocol that can be decomposed to improve the performance of data acquisition systems. Research results in this area have significant implications for innovations in industrial automation networking because of increasing distributed installations and Industrial Internet of Things (IIoT) applications.

Enhancing industrial IoT with edge computing and computer vision: An analog gauge visual digitization approach

The advent of Industry 4.0 has led to the widespread adoption of Industrial Internet of Things (IIoT) and Computer Vision technologies in manufacturing. However, handling the massive data generated by IIoT poses challenges. To address this, we propose a software and hardware architecture that combines edge computing with IIoT to enable computer vision applications on manufacturing floors. This research contributes to integrating edge computing and IIoT with Computer Vision technologies in manufacturing facilities, offering a cost-effective and scalable solution for machine vision applications. This approach benefits Small and Medium Manufacturers (SMMs) by utilizing low-cost hardware and open-source technologies, enabling them to harness Industry 4.0 advantages. Our approach involves IoT devices using Raspberry Pi microcomputers and USB webcams as clients and a Python-based edge node server software as the central component. Our test case study results show that a centralized edge-computing solution can offer several useability benefits and a significant performance increase over using a non-edge-enabled IIoT approach. The proposed architecture is tested, validated, and verified through a case study focused on the digitization of the readings of analog gauges. Our experimentation results can offer valuable insights and guidance for the broader adoption of edge-enabled IIoT solutions in manufacturing contexts.

Designing the future of coopetition: An IIoT approach for empowering SME networks

In an era where Information and Communication Technologies (ICT) redefine the boundaries of competition and collaboration, the concept of coopetition — simultaneous competition and cooperation — emerges as a strategic imperative for small and medium-sized enterprises (SMEs). This study presents the design and development of an Industrial Internet of Things (IIoT) artefact designed to enable competition among SMEs. Bringing the Service-Dominant Logic (S-D logic) foundations to IIoT, a Design Science Research (DSR) approach was employed. This approach facilitated the integration of design theory with practical problem-solving, creating the Coopetition IIoT-based System. The primary goal of this solution is to augment the dynamics of coopetition networks, with a particular focus on SMEs. The practical evaluation of the Coopetition IIoT-based System is assessed through a prototype evaluation by experts representing twenty-four manufacturing stone SMEs, a crucial sector in the Portuguese economy. The feedback received was highly positive, indicating a positive evaluation rate of 78.9%. This favourable response highlights the Coopetition IIoT-based System’s proficiency in fostering simultaneous competition and cooperation throughout the lifecycle of business opportunities in SME networks, thereby underscoring its potential as a facilitator of effective competition. Theoretically, this research enriches the application of S-D logic in coopetition networks and advances state-of-the-art IIoT systems. Practically, the Coopetition IIoT-based System demonstrates significant potential in boosting the competitiveness of SMEs in developed economies. Nonetheless, the ultimate efficacy of such IIoT systems will be best determined through real-world application and evaluation. Future research should concentrate on the real-case deployment and assessment of Coopetition IIoT-based Systems within coopetition networks.

Decoding the silent language: A framework for advanced visual emotion recognition to enhance worker well-being on IIoT-enabled manufacturing

The Industrial Internet of Things (IIoT), pivotal in the Industry 4.0 revolution, has transformed manufacturing, particularly assembly lines, by enabling real-time process monitoring and data analysis to enhance productivity. Yet, its potential impact on worker well-being has been less explored. As the National Association of Manufacturers anticipates an opening of 2.1 million unfilled jobs by 2030, the necessity for frameworks that not only understand but also improve employee well-being becomes apparent. We introduce a novel, non-intrusive visual emotion recognition framework that incorporates facial emotion recognition (FER) and emotional body posture recognition (EBPR) to assess workers’ psychological states. This initiative aims to mitigate human errors and accidents linked to stress or high cognitive load, thereby enhancing the manufacturing environment. Strategies such as optimized break time planning, line balancing, procedure modifications, and improved training are proposed to ensure both employee well-being and stakeholder benefits. This framework indirectly supports the industry’s efforts to address the looming challenge of unfilled jobs by fostering a supportive work environment that enhances job satisfaction, potentially making positions more appealing and retaining valuable talent. By bridging the gap between technological advancements and human-centric considerations, our approach underscores the significance of integrating IIoT solutions with employee welfare initiatives to address both productivity and workforce sustainability challenges.

A review of ice cream manufacturing process and system improvement strategies

The food industry faces several challenges, including intricate supply chains, compliance with food safety regulations, sustainability concerns, and the rising demand for high-quality products. Furthermore, consumers increasingly seek personalized food products with specific fat, sugar, and micronutrient levels. The ice cream industry is no exception in facing these challenges. Fortunately, Industry 4.0 technologies, such as smart manufacturing, data analytics, and the Industrial Internet of Things (IIoT), offer viable solutions to many of the aforementioned challenges. However, a deeper understanding of industrial ice cream manufacturing processes and systems is essential to apply these technologies effectively. While the related literature has often focused on ingredient selection to achieve the desired ice cream flavor and texture, there is a noticeable absence of comprehensive efforts to evaluate the impact of process- and systems-related aspects in ice cream manufacturing. This study employs a semi-systematic literature review approach to compile recent research that examines the influence of process- and system-level factors on ice cream product quality and production processes, focusing on the aspects that can benefit from implementing Industry 4.0 technologies. The literature review reveals that 1) at the process level, researchers have focused on three key processes (i.e., pasteurization, homogenization, and dynamic freezing) and their impact on the quality of the ice cream; 2) at the system level, researchers have concentrated their efforts on techno-economic factors, process scheduling, productivity, and sustainability.

Enhancing the security in IoT and IIoT networks: An intrusion detection scheme leveraging deep transfer learning

The Internet of Things (IoT) networks, which are defined by their interconnected devices and data streams are an expanding attack surface for cyber adversaries. Industrial Internet of Things (IIoT) is a subset of IoT and has significant importance in-terms of security. Robust intrusion detection systems (IDS) are essential for protecting these critical infrastructures. Our research suggests a novel approach to the detection of anomalies in IoT and IIoT networks that leverages the capabilities of deep transfer learning. Our methodology begins with the EdgeIIoT dataset, which serves as the basis for our data analysis. We convert the data into an appropriate image format to enable Convolutional Neural Network (CNN)-based processing. The hyper-parameters of individual machine learning models are subsequently optimized using a Random Search algorithm. This optimization phase optimizes the performance of each model by modifying the hyper-parameters that are unique to the learning algorithms. The performance of each model is meticulously assessed subsequent to hyper-parameter optimization. The top-performing models are subsequently, strategically selected and combined using the ensemble technique. The IDS scheme’s overall detection accuracy and generalizability are improved by the integration of strengths from multiple models. The proposed scheme demonstrates significant effectiveness in identifying a broad spectrum of attacks, encompassing a total of 14 distinct attack types. This comprehensive detection capability contributes to a more secure and resilient IoT ecosystem. Furthermore, application of quantization to our best models reduces resource utilization significantly without compromising accuracy.

Enhancing readiness degree for Industrial Internet of Things adoption in manufacturing enterprises: An integrated Pythagorean fuzzy approach

This study proposes a hybrid multi-criteria decision-making (MCDM) methodology designed to enhance the readiness for adopting the Industrial Internet of Things (IIoT) in manufacturing enterprises. The Pythagorean fuzzy approach is employed to address uncertainty and imprecision throughout decision-making processes. The development framework in this study incorporates TOE (Technology-Organization-Environment) and HOT fit (Human-Organization-Technology) to pinpoint barriers to IIoT adoption. Additionally, a triple helix model (THM) emphasizing on the synergy among university-industry-government is utilized to formulate pragmatic strategies. The agro-food processing industry in Thailand is used as a case study. In this study, even barriers are identified and validated through the Delphi method. The SWARA (Step-wise Weight Assessment Ratio Analysis) method determines the importance weights of these barriers, revealing “lack of digital culture”, “lack of knowledge and expertise,” and “job displacement concerns” as the three most critical barriers. The COBRA (COmprehensive Distance Based Ranking) method is employed to prioritize pragmatic strategies under THM, indicating that the role of the university in enhancing human capital capabilities is the most important, followed by the government's roles in enabling national ICT infrastructures and offering investment incentives as the second and third pragmatic strategies, respectively. A sensitivity analysis validates the proposed framework's reliability and robustness. The study's findings emphasize the potential of this integrated framework to guide future research endeavors among scholars and academicians across diverse industries beyond agri-food processing.

IEC104 protocol-based substation data upload IoT cloud platform implementation

Abstract The Industrial Internet of Things (IoT) has been developing rapidly under the background of Internet technology, and the most crucial part of its realization is the unified collection and access of heterogeneous data from multiple sources across networks and regions. For the access of substation system data, this paper designs a scheme to read data from IEC 60870-5-104 protocol and upload the data to IOT cloud platform by converting the data to data format supported by Kafka protocol, solving the problem of incompatibility between communication protocols, obtaining data from 104 secondary stations by connecting to them, parsing and processing the data in different formats, and converting the data to JSON format by processing and sending it to the IOT cloud platform. The data is converted into JSON format through processing and sent to the Kafka server, and the cloud platform obtains its data by subscribing to the corresponding topics. This approach makes substation data access more convenient, and efficiently and effectively saves the cost of hardware gateway, solves the incompatibility and interoperability between the underlying equipment access protocol and the cloud platform communication protocol, and builds a bridge for the remote transmission of the underlying equipment data of the Industrial Internet of Things.

An IoT platform for smart metal forming

Abstract Manufacturers in the metal forming industry are paying attention on smart manufacturing with the industrial internet of things (IIoT). An innovation has been occurring by changing metal forming processes from analog to digital by utilizing servo press machine and IT technology for improving efficiency and worth of the mass production manufacturing technology and of ability for the SDGs. This paper aims to introduce initiatives to survey of the current situation and formulation of strategies on the digital transformation in metal forming promoted by the Sokeizai Centre in Japan. An IoT platform for smart metal forming which has been developed based on the strategies is also introduced. The platform consists of sensor system for visualization of forming processes, sensor data wireless communication using local 5G and data assimilation with process simulation. Several sensing systems developed for process visualization are introduced.

Application and practice of industrial IoT cloud platform in oilfield intelligent transformation

Abstract Under the context of global energy transformation and low-carbon development, the traditional petroleum industry is facing a series of challenges such as technological innovation, market competition, environment protection, etc. Therefore, there is an urgent demand to realize green, safe, economical, and efficient development of oil and gas through oilfield intelligent transformation. This study develops a novel cloud platform covering the whole business based on the technical architecture of the industrial Internet of Things (IoT) for assisting the intelligent transformation of oilfields. Since the application of the IoT-based cloud platform in Qingcheng Oilfield, functions such as automatic collection of whole-domain data, remote monitoring, early warning, as well as intelligent analysis and decision-making have been realized. Furthermore, compared with the traditional platform, the new platform has improved eight indicators, such as data processing efficiency and sharing capacity, by more than 20%. Consequently, the developed IoT-based cloud platform can significantly improve the production and management level of the oilfield, and efficiently support oilfield intelligent transformation.

The use of digital twin for mobile robot swarm task allocation

Cyber-physical systems, with the advent of the Internet of Things (IoT), have paved the way for the rise of the Industrial Internet of Things (IIoT) in industrial environments. A significant manifestation of this synergy is the Digital Twin—a virtual representation of industrial entities, including robots. This paper examines the fusion of digital twins in swarm robotics, emphasizing communication, and task allocation among robots. This study employs several mobile robots interconnected via a wireless network, each equipped with a Raspberry Pi and specific sensors. A centralized server, acting as both a Docker host and a Docker registry, facilitates task allocation. The results indicate effective communication among robots, with the digital twin playing an instrumental role in safety assessment and task allocation, as well as deploying Docker containers using Portainer and GitLab APIs. Unity3D aids in visualizing IMU motion and orientation. The motivation behind this study stems from the need to enhance efficiency and safety in industrial settings through advanced automation and coordination in swarm robotics. This research not only demonstrates the feasibility of integrating digital twins with IoT-enabled robots but also sets the stage for future investigations into more complex task allocation and safety protocols in industrial environments. By leveraging digital twins in swarm robotics, we open new pathways for research into more adaptive, responsive, and efficient industrial systems.

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

A Double-Timescale Reinforcement Learning Based Cloud-Edge Collaborative Framework for Decomposable Intelligent Services in Industrial Internet of Things

CAPP-GPT: A computer-aided process planning-generative pretrained transformer framework for smart manufacturing

Smart manufacturing (SM) constitutes the backbone of Industry 4.0 (I4.0), allowing for heightened autonomy of the various interacting cyber-physical systems, making the various entities on the production floor. Connectivity, a vital enabler, plays a crucial role through state-of-the-art Digital Twinning (DT) technologies driven by underlying innovations like the industrial Internet of Things, Cloud Computing, and advancements in sensory devices. DT, which plays a vital role in the various planning functions under the production and operations management umbrella, is being used in the developed combined CAPP-GPT (Computer-Aided Process Planning-Generative Pretrained Transformer) and production scheduling approach to address disruptions on the shopfloor and in self-healing of the manufacturing processes at a micro-CAPP level by optimally adapting the process parameters and the developed toolpath on the fly based on online process signature measurements. In a leap commensurate with that which has taken place in Natural Language Processing-Large Language Models (Chat-GPT), similar efforts are currently being undertaken to parse CAD data structures and blueprints, fusing operations research and predictive analytics algorithms to carry out setup planning as well as sequencing and grouping manufacturing sub-operations. A hybridized Optimization and Machine Learning (ML) approach is employed where Logical Analysis of Data is used to solve the problem heuristically, exploiting various generative and variant methods at heart. Another extension of this macro-CAPP problem is being tackled by integrating the problem with delayed product differentiation, lot-sizing, and transfer line balance for futuristic batch-production shops employing Hybrid Manufacturing (HM) and Smart Assembly. At the micro-CAPP level, HM process parameters are optimized using a comprehensive approach employing the Taguchi loss function to assess surface roughness, internal failure costs, and other criteria, including greenhouse gas emissions and expended energy. Online measurements of the process signatures are also employed to adapt the initial set of process parameters using different automatic control schemes. ML is used to identify the process parameters carrying simulations on Simulink before the system is deployed.