Industrial Internet Of Things Technologies Research Articles

Wire and Cable Quality Traceability System Based on Industrial Internet of Things and Blockchain

Wire and cable are important industrial products involving the national economy and people’s livelihood, which are hailed as the “blood vessel” and “nerve” of the national economy, providing the basic guarantee for the normal operation of modern economy and society. The data traceability of their production and circulation process is a key factor in ensuring their quality and safety management. We aim to solve the problems of unsafe data transmission, weak quality control, and information islands in the process of wire and cable quality traceability in order to improve the production management efficiency of wire and cable manufacturing enterprises and to reduce the cost of consumer quality traceability of wire and cable products. We analyzed the technical characteristics and advantages of Industrial Internet of Things (IIoT) identity resolution and the blockchain. Key technologies are introduced, a traceability method that integrates the two is proposed, and a quality traceability framework based on the IIoT identity resolution system and blockchain technology is constructed. By analyzing the quality information composition of the wire and cable supply chain, a new quality traceability model based on the wire and cable supply chain is established. Finally, through the verification of the developed quality traceability system, the quality traceability function and quality information of each production link of wire and cable are successfully realized. This paper fills a gap in the field of cable product quality traceability using the combination of IIoT and blockchain technology. According to this model, it also has some potential for the traceability of other industrial products.

IoT Real-Time Production Monitoring and Automated Process Transformation in Smart Manufacturing

Conventional automobile manufacturing plants involve intricate assembly, testing, and debugging processes heavily reliant on manual operations. This study aims to explore the application of industrial internet of things (IIoT) and deep learning algorithms to achieve process automation in manufacturing. Firstly, utilizing IIoT technology, OPC UA, and point cloud fitting techniques, a comprehensive modeling of most equipment and materials within the factory is conducted, constructing a digital twin (DT) model as a virtual representation of actual equipment. Subsequently, the study innovatively introduces the deep Q network algorithm, facilitating the automatic transition of the production process and improving production efficiency. Through comparison with ten baseline models, the proposed model demonstrates an improvement in production efficiency of at least four percentage points compared to other models. Experimental validation confirms the effectiveness of the proposed model in the smart factory for electric vehicle manufacturing.

Factors influencing the adoption of industrial internet of things for the manufacturing and production small and medium enterprises in developing countries

AbstractSmall and Medium Enterprises (SMEs) are steadily moving in the direction of implementing digital and smart technologies, including the Industrial Internet of Things (IIoT) for improving their products and services. The adoption of IIoT allows manufactures and producers to make quick decisions for improving productivity and quality in real‐time. For this purpose, the era of digital industrial revolution from IR 1.0 to IR 5.0 is briefly explained. In this research study, the authors have reviewed and analysed the existing reviews, surveys and technical research studies on IIoT technologies for the manufacturing and production SMEs to highlight the concern raised. Forty‐seven (47) influencing factors are identified and classified into four groups based on the TOEI framework. Based on the identified influencing factors, IIoT adoption model is proposed for the manufacturing and production SMEs to adopt the new IIoT technologies in their business environments. Furthermore, a comparative analysis of the influencing factors has been done for the adoption of IIoT to increase efficiency, productivity and competitiveness for the manufacturing and production SMEs in developing countries. The proposed IIoT adoption model will help future policymakers and stakeholders to develop policies and strategies for the successful adoption and implementation of IIoT in manufacturing and production SMEs in developing countries. Also, recommendations are suggested to encourage IIoT adoption in production and manufacturing environments so that manufacturers and producers can respond easily and quickly to highly changing demands, product trends, skills gaps and other unexpected challenges in the future.

Modified graph-based algorithm to analyze security threats in IoT.

In recent years, the growing and widespread usage of Internet of Things (IoT) systems has led to the emergence of customized structures dependent on these systems. Industrial IoT (IIoT) is a subset of IoT in terms of applications and usage areas. IIoT presents many participants in various domains, such as healthcare, transportation, agriculture, and manufacturing. Besides the daily life benefits, IIoT technology provides major contributions via the Industrial Control System (ICS) and intelligent systems. The convergence of IoT and IIoT systems brings some integration and interoperability problems. In IIoT systems, devices interact with each other using information technologies (IT) and network space. However, these common usages and interoperability led to some security risks. To avoid security risks and vulnerabilities, different systems and protocols have been designed and published. Various public databases and programs identify and provide some of the security threats to make it easier for system administrators' missions. However, effective and long-term security detection mechanisms are needed. In the literature, there are numerous approaches to detecting security threats in IoT-based systems. This article presents two major contributions: First, a graph-based threat detection approach for IoT-based network systems is proposed. Threat path detection is one of the most critical steps in the security of IoT-based systems. To represent vulnerabilities, a directed acyclic graph (DAG) structure is constructed using threat weights. General threats are identified using Common Vulnerabilities and Exposures (CVE). The proposed threat pathfinding algorithm uses the depth first search (DFS) idea and discovers threat paths from the root to all leaf nodes. Therefore, all possible threat paths are detected in the threat graph. Second, threat path-reducing algorithms are proposed considering the total threat weight, hop length, and hot spot thresholds. In terms of available threat pathfinding and hot spot detecting procedures, the proposed reducing algorithms provide better running times. Therefore, all possible threat paths are founded and reduced by the constructed IoT-based DAG structure. Finally, simulation results are compared, and remarkable complexity performances are obtained.

Ensemble Learning for Multi-Label Classification with Unbalanced Classes: A Case Study of a Curing Oven in Glass Wool Production

The Industrial Internet of Things (IIoT), which integrates sensors into the manufacturing system, provides new paradigms and technologies to industry. The massive acquisition of data, in an industrial context, brings with it a number of challenges to guarantee its quality and reliability, and to ensure that the results of data analysis and modelling are accurate, reliable, and reflect the real phenomena being studied. Common problems encountered with real industrial databases are missing data, outliers, anomalies, unbalanced classes, and non-exhaustive historical data. Unlike papers present in the literature that respond to those problems in a dissociated way, the work performed in this article aims to address all these problems at once. A comprehensive framework for data flow encompassing data acquisition, preprocessing, and machine class classification is proposed. The challenges of missing data, outliers, and anomalies are addressed with critical and novel class outliers distinguished. The study also tackles unbalanced class classification and evaluates the impact of missing data on classification accuracy. Several machine learning models for the operating state classification are implemented. The study also compares the performance of the proposed framework with two existing methods: the Histogram Gradient Boosting Classifier and the Extreme Gradient Boosting classifier. It is shown that using “hard voting” ensemble learning methods to combine several classifiers makes the final classifier more robust to missing data. An application is carried out on data from a real industrial dataset. This research contributes to narrowing the theory–practice gap in leveraging IIoT technologies, offering practical insights into data analytics implementation in real industrial scenarios.

BE-TRDSS: Blockchain-Enabled Secure and Efficient Traceable-Revocable Data-Sharing Scheme in Industrial Internet of Things

As an important component of the industrial Internet of Things (IIoT), the smart factory uses IIoT and equipment-monitoring technology to collect data to reasonably arrange production. A large number of data is collected and uploaded to the IIoT cloud platform. However, the IIoT cloud platform is semi-trusted and has structural limitations and vulnerability, which makes it necessary to realize data dynamic security sharing and malicious users tracking. In this paper, we show that most recent work on this issue is still vulnerable to security threats at first. Then a blockchain-enabled dynamic and traceable data-sharing scheme for a smart factory is proposed. Blockchain performs the user authentication and stores the ciphertext index and public keys to avoid tampering with shared data. The tracking algorithm tracks malicious users and adds them to a revocation list embedded in the ciphertext. And the authority can flexibly select domain or user revocation as required. The LSSS access policy is hidden to protect user privacy, and the cloud server uses match test algorithm to detect whether users meet hidden access policy. Additionally, online-offline encryption and outsourced decryption improve the efficiency of the scheme where the ciphertext and the pairing operations required for decryption achieve constant size. A performance analysis shows that the scheme can resist a variety of collusion attacks, and simulations show that it outperforms current schemes.

An approach towards demand response optimization at the edge in smart energy systems using local clouds

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

Linkages between technologies and supply chain performance: benefits, power and risk reduction

PurposeThe purpose of this paper is to assess the combined impact of the Industry 4.0 blockchain and industrial internet of things technology (IIoT) technologies on the development of supply chain linkages associated with power, benefits and risk reduction and the ultimate impact of the linkages on supply chain performance.Design/methodology/approachA structural model with blockchain and IIoT as antecedents to the supply chain power, benefits and risk reduction linkages and the linkages as antecedent to supply chain performance is theorized. Data collected from 303 US manufacturing managers are analyzed using a covariance-based structural equation modeling (CB/SEM) methodology.FindingsThe CB/SEM results indicate that blockchain technology does not directly impact implementation of the linkages. Rather, implementation of blockchain technology supports implementation of IIoT technology, which strengthens supply chain linkages, thereby improving supply chain performance.Research limitations/implicationsTo the best of the authors’ knowledge, this study is one of the first to provide empirical evidence that Industry 4.0 technologies such as blockchain and IIoT strengthen linkages among supply chain partners related to power, benefits and risk reduction and that those stronger linkages lead to improved supply chain performance. It should be noted that this study is based on data from managers representing only one sector (manufacturing) and one country (USA). Replication based on data from other sectors and countries is needed to support generalization of the results.Practical implicationsPractitioners are provided with empirical evidence that the implementation of Industry 4.0 technologies such as blockchain and IIoT support supply chain management. These technologies facilitate data and information sharing among supply chain partners, enabling the integration and coordination of business processes throughout the entire supply chain.Social implicationsThe ultimate customers of supply chains benefit when supply chain partners work together efficiently and effectively. The implementation of blockchain and IIoT digital technologies lead to improve linkages among supply chain partners driving improvements in both efficiency and effectiveness, thus benefiting customers and society.Originality/valueIndustry 4.0 technologies are relatively new with the promise of improved supply chain performance. The efficacy of Industry 4.0 technologies as mechanisms to enhance information sharing is demonstrated based on the results of this study.

Implementation of a Production Monitoring System Using IIoT Based on Mobile Application

Productivity is a key factor in the success of a company, and real-time monitoring systems are necessary to achieve this goal. Manual data collection is time-consuming and exhausting. Industrial Internet of Things (IIoT) technology has been rapidly advancing in monitoring and optimizing industrial processes. Production processes can be disrupted due to machine problems; hence, the need to analyze machine efficiency using the overall equipment effectiveness (OEE) method. This study implements a system that uses Industrial Internet of Things technology based on mobile application to monitor production processes, report production results, assess machine performance using the OEE method and provide maintenance notifications based on time-based maintenance. Research findings indicate that the production monitoring system implemented on a prototype press machine based on an interactive mobile application interface is capable of monitoring production processes and reporting production results. The system can also assess machine performance using the OEE method, with a calculation accuracy of 99.95% and maintenance notifications with a delay time of 1.04 seconds.

AN INTERACTION MODEL OF RELAY PROTECTION MEASURING TRANSDUCERS WITH COMPUTING SYSTEMS VIA IIOT TECHNOLOGY

"The object of this research is the interaction model between measuring transducers and computing systems, particularly focusing on its application within the framework of Industrial Internet of Things (IIoT) and Cloud technologies. The imperative driving this study is the increasing demand for precise and efficient data acquisition, protection and processing in various industrial sectors. The research aims to address this need by elucidating an interaction model that optimizes measuring computing systems through IIoT and Cloud technologies. As a result of our research, a comprehensive interaction model that encompasses the entire data flow process was established. This model integrates data acquisition, analytics, real-time communication protocols, and secure data transmission methodologies. It offers a structured framework for acquiring, transmitting, and analyzing data with high accuracy and efficiency. The significance of our results lies in their capacity to resolve critical challenges in data acquisition and processing of the electrical power data, as well as monitoring and protection against electrical faults. These results hold practical value under diverse industrial conditions, including manufacturing, energy production and energy distribution. Keywords: relay protection, open architecture, encryption, data processing, data visualization, internet of things "

Improving manufacturing supply chain performance: nexus of industrial Internet of Things, blockchain technology and innovativeness

Purpose This paper aims to model the mediating effects of facilitating conditions and innovativeness in the industrial Internet of Things (IIoT) and blockchain technology (BT) on manufacturing supply chain performance (MSCP). Design/methodology/approach Partial least square structural equation modelling was used to test the goodness of the model fit and hypotheses by using SmartPLS 3.3.3. Data was collected from 464 managers in Pakistan’s automotive industry through a stratified random sampling technique. Findings IIoT, BT, facilitating conditions and innovativeness significantly enhanced the MSCP. Therefore, the mediation between facilitating conditions and innovativeness to IIoT and BT adoption was significant in the MSCP. Practical implications The adoption of digital technology to improve the MSCP can assist companies in reducing the cost of complex procurement, production and distribution processes through secured and efficient operations. Furthermore, organisations must establish a conducive atmosphere that fosters experimentation, collaboration and resource allocation towards technological advancements to capitalise on the advantages of these technologies effectively. Originality/value This study developed a research model integrating IIoT technology, BT, facilitating conditions and innovativeness to determine the MSCP under the resource-based view theory. The outcome of this study could help organisations design a framework to improve supply chain performance by integrating innovativeness.

Learning human-process interaction in manual manufacturing job shops through indoor positioning systems

Nowadays, manufacturing systems are increasingly embracing the Industry 4.0 paradigm. Therefore, manual and low-standardized manufacturing environments are often digitized through Industrial Internet of Things technologies to quantitatively assess and investigate the role of the human factor from multiple points of view. This approach is commonly known as Operator 4.0. In such a scenario, this manuscript proposes an original digital architecture to monitor the efficiency and the social sustainability of labor-intensive manufacturing job shops. While the anonymous spatio-temporal trajectories of tagged workers are acquired through an ultrawide band radio network, machine learning algorithms autonomously detect the human-process interactions with strategic industrial entities upon developing industrial key performing indicators. The proposed architecture is tested and validated in a real manual manufacturing system. In detail, the performing accuracies of the machine learning-based software provide industrial plant supervisors with several production metrics to identify the hidden weaknesses and bottlenecks of the monitored manufacturing system. Such digital assessment may trigger a re-organization of the considered process to, for instance, enhance the allocation of the material in storage areas while fairly re-balancing the distances traveled by workers for picking activities.

Intrusion Detection-Based Ensemble Learning and Microservices for Zero Touch Networks

The industry has undergone a digital transformation facilitated by the Industrial Internet of Things (IIoT) technology, ushering in the era of Industry 4.0. However, the widespread use of IIoT devices, such as sensors, robots, and other IIoT technologies, has made IIoT systems and associated services vulnerable to a range of network-based attacks, which can impede their performance and disrupt operations. Moreover, the automation process is essential for IoT-based industries to meet future demands. The concept of a zero-touch network has recently evolved to coordinate and manage network resources automatically. Machine learning (ML) plays a critical role in its architecture due to its ability to facilitate a close-loop automation process. With ML, analytical tasks and real-time predictions can be achieved effectively to build smart applications for the early detection of cyberattacks in IIoT systems. The ML components, such as preprocessing, training, and testing, can be subdivided into microservices to improve service while allowing interaction with edge and cloud services. In this article, we propose an ensemble learning-based intrusion detection system (IDS) for a zero-touch network automation process by leveraging ML and microservice technology to improve the trustworthiness of IIoT systems. More specifically, we use the feature selection technique to select significant features and pass them to different models, then blend their predictions based on a stacked ensemble learning approach. Experiments are conducted to evaluate the performance of the proposed framework compared to existing studies.

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.

Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network

Application of industrial Internet of things technology in fault diagnosis of food machinery equipment based on neural network

Determination of the speed of a microprocessor relay protection device of open architecture with a reed switch and the industrial internet of things

The paper presents the development of a microprocessor-based relay protection device on open architecture. Currently, there is a problem with modern microprocessor relay protection: the impossibility to replace the damaged element with alternatives from other manufacturers. The solution to this problem is the use of devices with open architecture. The study developed a structural model of a microprocessor-based relay protection device based on an open architecture with the industrial Internet of things application. Open architecture is achieved through open protocols and the principle of modularity. The industrial Internet of things technology transfers the control action of triggering blocking. A microprocessor-based relay protection device prototype based on an open architecture was developed. The simulation of the developed device was conducted. The appearance of higher harmonics and aperiodic components in the short-circuit current was not considered during modeling. Due to the study's limitations in the form of lack of load, current and voltage sensors, such as Hall sensors, and inductance coils, the subject of this study is only the speed of operation. A high multiplicity current generation setup was assembled for experimental testing. The developed relay protection device on an open architecture trips faster than the traditional solution. The application of the Internet of Things allowed it to ensure the blocking of non-selective tripping. The obtained results are provided by structural simplification compared to traditional solutions and speed of information transfer with the application of the Internet of things. The developed open architecture device with the industrial Internet of things technology application gives new possibilities for relay protection systems, including flexibility to meet the requirements in connection with the introduction of distributed power

Securing Industrial Internet of Things (Industrial IoT)- A Reviewof Challenges and Solutions

Industrial IoT (Industrial IoT) is a new promising technology which can be used to increase the amount of productions with high qualities. Industrial IoT technologies guarantee full control to the processes remotely through the internet which can reduce the number of workers in the field. As a result, this can reduce the percentage of worker injuries and accidents in addition to the total costs. The Industrial IoT systems are attractive targets to attackers. For this reason, these systems require high levels of security since such levels have direct effects on physical devices which may be dangerous on human life and safety. To guarantee high level of security, a combination between Information Technologies (IT) and Operation Technologies (OT) with new innovative methods should take place. In this paper, many new technologies and security methods are reviewed with their possible attacks in order to provide Industrial IoT infrastructure designers with the required information to take them into consideration. Also, differences and convergences between both classical Information Technology (IT) and operational Technology (OT) and their relations to the Industrial IoT systems are investigated with the possible attacks on each layer of the IT and the OT.

Intelligent Delay-Aware Partial Computing Task Offloading for Multiuser Industrial Internet of Things Through Edge Computing

The development of Industrial Internet of Things (IIoT) and Industry 4.0 has completely changed the traditional manufacturing industry. Intelligent IIoT technology usually involves a large number of intensive computing tasks. Resource-constrained IIoT devices often cannot meet the real-time requirements of these tasks. As a promising paradigm, the mobile-edge computing (MEC) system migrates the computation intensive tasks from resource-constrained IIoT devices to nearby MEC servers, thereby obtaining lower delay and energy consumption. However, considering the varying channel conditions as well as the distinct delay requirements for various computing tasks, it is challenging to coordinate the computing task offloading among multiple users. In this article, we propose an autonomous partial offloading system for delay-sensitive computation tasks in multiuser IIoT MEC systems. Our goal is to provide offloading services with minimum delay for better Quality of Service (QoS). Enlighten by the recent advancement of reinforcement learning (RL), we propose two RL-based offloading strategies to automatically optimize the delay performance. Specifically, we first implement the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning algorithm to provide a discrete partial offloading decision. Then, to further optimize the system performance with more flexible task offloading, the offloading decisions are given as continuous based on deep deterministic policy gradient (DDPG). The simulation results show that the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning scheme reduces the delay by 23%, and the DDPG scheme reduces the delay by 30%.

Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach

The deep integration of industrial internet of things technologies in the industrial smart grid (ISG) brings many privacy and security attacks, threatening the trustworthiness of underlying system infrastructures and associated services. That, in turn, raises the necessity for anomaly detection staged by appropriate authorities. Deep learning can provide a promising solution for anomaly detection, but it remains untrustworthy as it fails to do well with small-size labeled data and class-imbalanced data. To solve these issues, this article introduces a novel privacy-preserving federated semisupervised class-rebalanced (Fed-SCR) framework for the detection of anomalous power data in fog-assisted smart grids. Fed-SCR introduces a semisupervised generative network to enhance the quality of generated minority samples and model the relationships between labeled and unlabeled data. Moreover, the generator and discriminator are block-structured leveraging temporal convolutions to improve the representation power during the training. A novel aggregation scheme [termed federated geometric median aggregation (Fed-GMA)] is also introduced based on selective and periodic geometric-median-based aggregation with the main aim to increase the robustness of the federated semi supervised class-rebalanced (Fed-SRC) against noisy gradients while maintaining communication efficiency. The evaluations of Fed-SCR on public power grid datasets reveal its efficiency in improving the trustworthiness of the ISG platform, outperforming the competing methods in terms of binary classification (accuracy: 97.28) and multiclass classification (accuracy: 96.36–95.04).

IIOT HARDWARE ARCHITECTURE OF DISTRIBUTED CONTROL SYSTEMS FOR CONTINUOUS INDUSTRIAL PRODUCTION AND AGRICULTURAL COMPLEXES

Currently, the classical hardware architecture of their construction has been established in distributed control systems for continuous technological processes and productions in various agricultural complexes and industries. Such an architecture has a number of disadvantages, in particular, the presence of a large number of different data bus standards at different levels of the platform architecture, which often complicates the integration of individual parts of the system. Modern automation systems are developing rapidly, which leads to the need for new system functionality that classical architecture cannot provide. (Research purpose) The research purpose is developing a concept of hardware architecture of distributed control systems for continuous industrial production and agricultural complexes using modern technologies and practices of the industrial Internet of Things. (Materials and methods) Used open sources of international communities in the field of automation and industrial Internet of Things, domestic and foreign scientific publications. The following methods were applied: decomposition by automation levels; decomposition by functionality performed by individual hardware components of the system; generalization of information from modern sources on the subject of research; formalization of the hardware structure in the form of circuit diagrams. (Results and discussion) We have proposed a basic hardware diagram of the IIoT platform for a distributed control system based on IIoT industrial Internet of Things technologies. We have made the main vector in the development of the architecture based on the use of the Ethernet data bus at all levels of the platform, as well as on the implementation of modern and necessary IIoT functions. (Conclusions) It has been shown that the classical hardware architecture of distributed management systems for agricultural complexes and continuous production has proven itself well, however, the need to introduce new functionality and awareness of the need to use unified data transmission buses at all levels of the enterprise leads to the development and gradual transition to new IIoT architectural models for the design and development of such systems. We analyzed the existing global trends in this area and proposed an up-to-date and modern concept of the hardware architecture of IIoT distributed systems.