Industrial Internet Of Things Platform Research Articles

Smart product service resources composition optimization for smart product service system in context of industrial IoT platform

Smart product service resources composition (SPS-RC) is a core issue of smart product service system (PSS) development in the emerging context of industrial internet of things (IIoT) platform, aiming to organize distributed resources for smart product service operation tasks. Existing research mainly focuses on the traditional physical PSS, while contains scant exploration of the new features emerging in context of IIoT platform, leading to an urgent updating need for SPS-RC approaches. Therefore, this study proposes a systematic framework for SPS-RC in context of IIoT platform based on cyber physical system (CPS)-based SPS blueprint, graph theory and adaptive genetic algorithm. In the proposed framework, the SPS-RC problem is formulated in accordance with the realistic smart product service operation process using the CPS-based SPS blueprint. The graph theory and depth-first search algorithm are employed to calculate the composition performance under the environment of heterogenous connection structures between SPS tasks. In addition, an optimization model considering heterogenous composition structures is established with aggregating various quality of service into one comprehensive objective and solved by an adaptive genetic algorithm. Finally, an application of this systematic framework for smart port container crane service demonstrates the feasibility and effectiveness of the proposed approach.

An industrial IoT-based deformation resistance prediction and thickness control method of cold-rolled strip in steel production systems

Different from traditional analytical and numerical simulation methods, data-driven methods can more effectively describe the effects of nonlinear and coupling factors. Although the fluctuations of deformation resistance and thickness for hot-rolled strips significantly influence the thickness accuracy in strip cold rolling, the traditional deformation resistance prediction model and thickness control mode don't involve that. Thus, based on the Industrial Internet of Things (IIOT) platform and data-driven technology, this work proposed a new method for deformation resistance prediction and a new mode of feed-forward control for strip thickness. IIOT platform can collect production data from the various levels to provide a complete data set. The deformation resistance prediction model is established by adopting the differential evolutionary algorithm to optimize the bi-directional long and short-term memory network. In addition, the feed-forward control (DFFC) strategy for strip thickness is proposed based on deformation resistance prediction. An application results of a 1420 mm production line of CR indicated that the established prediction model could provide a high accuracy prediction for deformation resistance, and compared to traditional thickness gauges-based automatic gauge control methods, the DDFC can capture the effect of the fluctuations of deformation resistance and thickness for the hot-rolled strip to improve thickness accuracy effectively.

End to End secure data exchange in value chains with dynamic policy updates

Data exchange among value chain partners offers a competitive advantage, but it also exposes sensitive data to increasing risks of compromise. To protect this data, securing it during storage and transmission is essential, allowing only the data producer and final consumer to access or modify it. This can be particularly challenging for Industrial Internet of Things (IIoT) devices, which often have limited processing and memory capabilities. To address these challenges, End-to-End (E2E) security mechanisms are needed to prevent data breaches resulting from value chain attacks. Additionally, value chain environments are dynamic and involve multiple data generation and consumption entities, requiring the ability to update access policies as needed. This paper presents a Ciphertext-Policy Attribute-Based Encryption (CP-ABE) data exchange system that provides E2E security for value chains, considering the most relevant security and industrial requirements. The proposed solution can protect data according to access policies while also allowing those policies to be updated without breaking E2E security or overloading field devices. The experimental evaluation demonstrates the feasibility of the proposed solution for IIoT platforms.

Unlocking the potential of Industrial Internet of Things (IIOT) in the age of the industrial metaverse: Business models and challenges

While the Industrial Internet of Things (IIoT) holds much promise, there is a mismatch between its potential and companies capturing value from investments in IIoT. Indeed, even when companies recognize the value of IIoT, they do not necessarily know how to grasp related opportunities and are challenged in developing a suitable business model. Accordingly, to alleviate roadblocks to capturing value from IIoT, in this paper we address the challenge of identifying suitable business models in the age of the industrial metaverse. We do so through an extensive review and classification of main IIoT business model archetypes that are successful in practice. In particular, we conduct a content analysis of IIoT projects based on over 2000 articles in industry trade magazines and newspapers. Our analysis identifies four distinct business model archetypes in the context of IIoT, viz. IIoT digical, IIoT service-centered, IIoT data-driven, and IIoT platform, and further explores the challenges that need to be addressed to ensure that companies can capture value from their IIoT initiatives. We explore appropriate contexts for these business model archetypes, and, in doing so, we provide actionable guidance for industrial (marketing) managers seeking to position their IIoT offerings and maximize their value.

IOTA-Based Distributed Ledger in the Mining Industry: Efficiency, Sustainability and Transparency.

The paper presents a traceability framework founded upon a methodological approach specifically designed for the integration of the IOTA-based distributed ledger within the mining industry. This framework constitutes an initial stride towards the certification and labelling of sustainable material production. The efficacy of this methodology is subject to real-world evaluation within the framework of the European Commission funded project DIG_IT. Within the architectural framework, the integration of decentralized identifiers (DIDs) and the IOTA network are instrumental in effecting the encryption of data records, with associated hashes securely anchored on the explorer. Recorded environmental parameters, encompassing metrics such as pH level, turbidity, electrical conductivity, and emissions, serve as tangible evidence affirming their adherence to prevailing regulatory standards. The overarching system architecture encompasses a sophisticated Industrial Internet of Things platform (IIoTp), facilitating the seamless connection of data from a diverse array of sensors. End users, including governmental entities, mining managers, and the general public, stand to derive substantial benefits from tailored dashboards designed to facilitate the validation of data for emission compliance.

Retracted: Optimization and Simulation of Controller Area Network Communication Model Based on Industrial Internet of Things Platform

Retracted: Optimization and Simulation of Controller Area Network Communication Model Based on Industrial Internet of Things Platform

Development of a monitoring system for technological equipment of a meat processing plant

Relevance. In the production of meat products and semi-finished products, equipment from the 1990s is often used. Such equipment is not equipped with modern means of transmitting diagnostic data and information about the current status via wireless communication, and requires periodic monitoring during the work shift. Lack of timely diagnostic data can lead to equipment failures and unexpected downtime. At the same time, the development of technical means of communication makes it possible to connect industrial equipment to the Internet and remotely receive data on the current state of the equipment online.Methods. The objects of the study were the MULTIVAC R5200 vacuum thermoforming packaging machine and thermal chambers with installed controllers Aditec MIC 2410, Aditec TP 1000, MIKSTER MCC 2100. Methods of object-oriented programming in Python 3.4.4 were used for the monitoring system under study. For network interaction of technological equipment with cloud services, the MQTT-v3.1.1 data transfer protocol was used.Results. The use of a monitoring system based on the industrial Internet of Things platform made it possible: when packing boiled and smoked sausages, to reduce the maintenance time of the packaging machine and the consumption of spare parts, to reduce the production of low-quality products; when carrying out technological processes in thermal chambers, to completely eliminate spoilage of semi-finished products, to improve the quality of the finished product. In the future, the spread of such technology to the equipment of the entire enterprise will make it possible to solve the problems of collecting a large amount of data on the current state of the equipment, conducting analysis and making managerial decisions.

Contrasting value creation strategies of industrial-IoT-platforms – a multiple case study

Previous platform research has drawn attention primarily to digital consumer platforms, ignoring the specificities of industrial settings. Hence, existing research findings fall short regarding Industrial Internet-of-Things Platforms (IIoTP), which leads to an incomplete understanding of digital platforms. Particularly, IIoTPs from Internet technology firms (Hyperscalers) and incumbent industrial firms (Integrated IIoTPs) deserve special interest due to their coopetitive relationships. Addressing this research need, the study applies explorative multiple case study research to five of these IIoTPs, by analyzing 22 interviews, to describe this phenomenon in-depth in its contextual conditions. As a result, the authors empirically derive, based on existing frameworks and classifications, (1) which strategic measures are needed for a successful evolvement, and (2) which dynamic strategic trajectories occur. We report several findings. First, this study contributes to the literature by showing that existing theories of digital platforms are fertile ground for further and more nuanced empirical research. Second, through the linkage of five strategic measures to the dominant value creation logics, the authors provide important contextualization on different vertical platform layers. Third, the study offers IIoTP managers insights by identifying dynamic strategic trajectories that reveal Hyperscalers are in the best position to profit from innovation, thereby reversing the common notion that downstream positioning is a prerequisite for success.

Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance

In a world increasingly aware of its carbon footprint, the quest for sustainable energy production and consumption has never been more urgent. A key player in this monumental endeavor is fuel conservation, which helps curb greenhouse gas emissions and preserve our planet’s finite resources. In the realm of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) technologies, Caterpillar (CAT) generator set (genset) operations have been revolutionized, unlocking unprecedented fuel savings and reducing environmental harm. Envision a system that not only enhances fuel efficiency but also anticipates maintenance needs with state-of-the-art technology. This standalone IIoT platform crafted with Visual Basic.Net (VB.Net) and the KEPware Object linking and embedding for Process Control (OPC) server gathers, stores, and analyzes data from CAT gensets, painting a comprehensive picture of their inner workings. By leveraging the Modbus Remote Terminal Unit (RTU) protocol, the platform acquires vital parameters such as engine load, temperature, pressure, revolutions per minute (RPM), and fuel consumption measurements, from a radar transmitter. However, the magic does not stop there. Machine Learning.Net (ML.Net) empowers the platform with machine learning capabilities, scrutinizing the generator’s performance over time, identifying patterns and forecasting future behavior. Equipped with these insights, the platform fine tunes its operations, elevates fuel efficiency, and conducts predictive maintenance, minimizing downtime and amplifying overall efficiency. The evidence is compelling: IIoT and AI technologies have the power to yield substantial fuel savings and enhance performance through predictive maintenance. This research offers a tangible solution for industries eager to optimize operations and elevate efficiency by embracing IIoT and AI technologies in CAT genset operations. The future is greener and smarter, and it starts now.

COMPATIBILITY PROVISIONING OF SENSOR SYSTEMS AND INDUSTRIAL INTERNET OF THINGS PLATFORMS

The article relates to the issue of ensuring the compatibility of different sensors types, their measurements results and the industrial Internet of things platforms. The solution offered to ensure semantic interoperability is based on semantic technology with the architecture development and further creation of a common ontological model. The approach offered allows integrating both semantic content of sensor information and methods for arrangement and normalization of measurement results, which is relevant for subsequent analysis and use of industrial Internet of things data on various platforms, including data depersonalization for computer-assisted learning. A study of regulatory documentation and technical solutions to ensure the compatibility of the industrial Internet and sensor systems was carried out, existing similar solutions were considered, shortcomings in comparison with the proposed method were identified. Ontology concepts for the model of semantic compatibility of sensor systems and the industrial Internet of things platform were developed. The article gives an example of the developed logical inference procedure in the framework of the ontology implementation with use of the free software tool Protégé. Using the proposed approach allows to proceed to knowledge-based interoperability.

Platform-Based Manufacturing Service Collaboration: A Supply-Demand Aware Adaptive Scheduling Mechanism

With the development of new-generated IT technologies and the launch of a series of industrial Internet of things platforms, service-oriented manufacturing is an inevitable trend of manufacturing industry. Therefore, the platform-based manufacturing service collaboration (MSC) becomes a recognized answer to the complex and personalized manufacturing demands. However, the changes in both supply and demand of the platform in its operation process are usually unpredictable. To cope with the scheduling problem on the platform-based MSC with the dynamic uncertainties of both supply and demand, an adaptive scheduling mechanism is explored in this article. In which, the real-time system state evaluation method considering supply and demand are designed, and a supply-demand aware rescheduling trigger judgement is proposed. Experimental results show the effectiveness and adaptiveness of the proposed mechanism, which also provides a reference for other MSC scheduling problems towards different dynamic situations.

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism’s functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.

Application of Industrial Internet of Things (IIoT) in Crude Oil Production Optimization Using Pump Efficiency Control

The collapse of oil prices in mid-2014 and early 2016 was the biggest in modern history that witnessed more than a 70% drop in the price to around $40 barrel. It prompted companies to think seriously to maintain profitability. Most companies were able to survive partly by simplifying their operations. Price recovery in 2021 is only 70% of its peak value. Companies are focusing on reducing the cost of operations, increasing production simultaneously, and finding new and different strategies to survive. The current scenario is witnessing strong research focusing on the development of process control for oil and gas upstream and downstream to improve the control and preventive maintenance to reduce operating costs and increase production. This paper presents the Industrial Internet of Things (IIoT) practical solution that improves the oil production rate from the well and increases the average pump efficiency (fillage) to 90%. This paper proposes a mechanism for collecting, storing, and analyzing all required parameters to build valuable charts. These charts’ data help optimize the values and parameters for controller setpoints to prevent pump gas lock problems. An artificial lift is required to lift the oil from the well. In this paper, the sucker rod pump is driven by a gas engine fed by the well’s gas. At the same time, SCADAPack 535E remote terminal unit collects all pump and well parameters such as hydraulic pressure, casing pressure, tubing pressure, and pump speed in stroke per minute (SPM). The remote terminal unit sends the data through a wireless network using a 5 GHz antenna to the main control room. The IIoT platform is designed using a visual basic programming language. Microsoft DDE (Dynamic Data Exchange) and Kepware OPC server were used to work on the received data to monitor, generate charts, and apply the controllers.

SPTM-EC: A Security and Privacy-Preserving Task Management in Edge Computing for IIoT

Data/tasks of Industrial Internet of Things (IIoT) are extremely private and valuable to computing. Edge computing facilitates IIoT services by offering platforms of supportive facilities and functionalities. However, the convergence of intelligent edge computing and IIoT platforms has been held back by the need to develop systems for maintaining security and privacy. In this article, we propose a new framework for securely and confidentially sending, storing, and computing IIoT tasks. This framework employs a lightweight encryption scheme alongside modified ElGamal encryption and digital signature schemes. We analyzed the robustness of this framework in terms of security and privacy, and assessed its performance through simulations. Ultimately, the proposed framework outperformed the existing models in terms of time complexity, latency, and energy consumption.

Design and Deployment of a Data Lake at a Pilot Plant Scale for a Smart Electropolishing Process

In order to remain competitive and satisfy the demands of today’s customers in a timely manner, manufacturing industries are embracing the Industry 4.0 philosophy where automation is pushed beyond robotics to new technologies emerging from data science and artificial intelligence. The aim is to reduce time spent on none added value tasks and help learning from past experience in order to enhance efficiency and quality of manufacturing processes.Traditional industries, such as electropolishing, need to find ways to automate their, often heavily artisanal-based techniques and develop an intelligent network of machines and processes taking advantage of information and communication technology such as Big Data, IoT (Internet of Things), or Artificial Intelligence (AI). This digital transition can be realized through the application of an IIoT (Industrial Internet of Things) platform that constructs a massive, sophisticated information network of interconnected sensors, equipment, and processes known as cyber-physical systems.Within this network, large amounts of data (for example process bath attributes such as temperature or viscosity and part characteristics such as roughness or brightness) can be collected automatically via sensors and through user-friendly applications from manual measurements and observations. All data are uploaded automatically into a cloud-based data storage system. In order for this collected information to be useful, the data needs to be processed to allow pattern discovery and extraction of useful information regarding the system performance, probable faults in the process, and product quality. Besides others, machine learning algorithms play a key role in extracting useful information.Classification and processing of such massive, diverse, and rapidly arriving data sets are known to be challenging. As a result, the concept of data lake has arisen in the last decade as an appealing and cost-effective approach for companies to manage large amounts of data. It consists of a large repository of datasets designed to transform raw and unstructured data into structured, usable information to allow further processing. A data lake, organized typically in four layers (ingestion, distillation, processing, and insights layers), stores both old and near real-time data in one location for initial assessment, with comprehensive data organization, analysis, and visualization being performed only when necessary 1,2. This promotes agility by allowing data to be accessed by everyone in the company. 2 In this work, a data lake is designed and implemented in conjunction with a pilot plant to demonstrate how in the electropolishing process of stainless-steel samples in an aging electrolyte, data can be collected and organized for further processing using machine learning techniques in order to optimize the process and part quality based on the data analysis results.

Промышленный интернет вещей для предприятий радиоэлектронной промышленности

The industrial Internet of Things platform is described. It is focused on solving the problems that are relevant for the management of the electronic industry. It has been shown that the use of modern tools based on intelligent analysis of Big Data makes it possible to provide managers with reliable operational information for management decisions.

IIoT platforms’ architectural features – a taxonomy and five prevalent archetypes

In the industrial Internet of Things (IIoT), digital platforms have recently received significant attention. Although IIoT platforms revolve around similar business objectives, they address various use cases and, thus, differ considerably in their architectural setup. While research has already investigated IIoT platforms from a business or design perspective, little is known about their underlying technology stack and its implications. To unveil different IIoT platform configurations and better understand their architectural design, we systematically develop and validate a taxonomy of IIoT platforms’ architectural features based on related literature, real-world cases, and expert interviews. On this foundation, we identify and discuss five IIoT platform archetypes (Allrounder, Device Controller, Data Hub, Service Enabler, Connector). Our findings contribute to the descriptive knowledge in this ambiguous research field while also elucidating the interplay of IIoT platforms’ architectural setup and their purpose. From a managerial viewpoint, our results may guide practitioners in comparing and selecting a suitable IIoT platform.

An Intelligent Vision-Based Method of Worker Identification for Industrial Internet of Things (IoT)

With the rapid development of Internet of things (IoT) and computer vision (CV), the application of combining the IoT platform and CV technology to monitor the worker safety has attracted more and more attention in the field of industrial information. Worker identification is a prerequisite for safety management in industrial production, and safety helmet can not only protect worker’s head from accidental injuries but also help to identify the work types of workers through different colors. Therefore, this study proposes an intelligent method for worker identification based on moving personnel detection and helmet color characteristics. First, the motion objects that contain personnel and nonpersonnel are detected by the Gaussian mixture model (GMM) and extracted to generate the region of interest (RoI) images. Then, the multiple-scale histogram of oriented gradient (MHOG) features of the RoI images are extracted, and the personnel images are identified by the support vector machine (SVM). Third, the workers’ head images are obtained by the OpenPose model and personnel mask, and the GoogLeNet-based transfer learning network is established to extract the head images features and realize worker identification. This method is tested on our dataset, and the average accuracy of worker identification for multiple helmet color combinations reaches 99.43%, which is robust to workers’ angle, scale, and occlusion.

Graph-based operational robustness analysis of industrial Internet of things platform for manufacturing service collaboration

As industrial Internet of things (IIoT) for Manufacturing Service Collaboration (MSC) is becoming the current trend to accelerate the upgrade iteration of manufacturing capability, developing the robust IIoT platform operation mechanism for MSC is crucial to promote the continuous and stable service collaboration in the presence of supply and demand uncertainties. This paper studies the operational robustness of the IIoT platform for MSC. Firstly, the operation performances, requirements, and challenges of the IIoT platform towards manufacturing collaboration are analysed in classified platform practices, which can provide a comprehensive cognition about platform operation for manufacturing collaboration. Then, to evaluate the tolerance and persistence capabilities of MSC under supply and demand uncertainties, a graph-based operational robustness analysis method of the IIoT platform for MSC is proposed. The IIoT platform operation network for MSC is modelled as an interdependent network-of-network structure based on graph theory, which helps to characterise MSC performance properties under complexities. By combining manufacturing properties with network statistics, the evaluation metrics of operational robustness are established, which is done to quantise the MSC effectiveness under uncertainty effects. A case about customised manufacturing of automobiles illustrates the application of the proposed methods. Finally, future studies about robust MSC regulation are discussed.

Interactive IIoT-Based 5DOF Robotic Arm for Upper Limb Telerehabilitation

Significant advancements in contemporary telemedicine applications enforce the demand for effective and intuitive telerehabilitation tools. Telerehabilitation can minimize the distance, travel burden, and costs between rehabilitative patients and therapists. This research introduces an interactive novel telerehabilitation system that integrates the Industrial Internet of Things (IIoT) platform with a robotic manipulator named xARm-5, aiming to deliver rehabilitation therapies to individuals with upper limb dysfunctions. With the proposed system, a therapist can provide upper limb rehab exercises remotely using an augmented reality (AR) user interface (UI) developed using Vuforia Studio, which transmits bidirectional data through the IIoT platform. The proposed system has a stable communication architecture and low teleoperation latency. Experimental results revealed that with the developed telerehabilitation framework, the xArm-5 could be teleoperated from the developed AR platform and/or use a joystick to provide standard upper limb rehab exercises. Besides, with the designed AR-based UI, a therapist can monitor rehab/robot trajectories along with the AR digital twin of the robot, ensuring that the robot is providing passive therapy for shoulder and elbow movements.