Industrial IoT and Intelligent Manufacturing

Introduction. Industry 4.0 is a concept that is considered a new phase in the Industrial Revolution, closely related to the application of information technologies and the digital transformation of manufacturing. The main purpose is to be created a more holistic and more connected ecosystem, focused on supply chain management in industrial companies. Implementation of solutions in Industry 4.0 is mostly related to the concept of the Internet of Things (IoT). Mass deployment of this type of technology in industrial enterprises is the basis of the so-called Industrial Internet of Things (IIoT). Achieving interoperability in the IIoT requires the combination of two technologies: the Internet of Things and the Internet of People.
 Aim and tasks. This article describes the implementation of the concept of the Internet of Things in industrial enterprises, as a key technology factor for developing Industry 4.0.
 Results. A brief overview of the evolution of industrial production - from the beginning of the Industrial Revolution to the emergence of Industry 4.0 is made. The main principles for implementing Industry 4.0 solutions ensure that the entire production process is computerized. Industry 4.0 solutions are mostly associated with the concept of the Internet of Things (IoT) whose definition and essence are obtained in this article. Based on the various concepts of the IoT are presented solutions that can be used in the industry, namely: in consumer devices in technology used in public organizations in infrastructure applications in industrial applications, also called the Industrial Internet of Things (IIoT). Therefore, we can say that there is a significant potential for improving production processes as regards: optimization of operations, forecasting equipment support, inventory optimization, improving workers' security, shipping chain optimization, etc.
 Conclusions. The application of the Internet of Things in enterprises is an important and decisive step in the process of their digital transformation and transition to Industry 4.0. The interaction between humans and machines, carried out through Internet technologies, leads to the emergence of the Internet of Everything, which will be a basic concept in industrial production in the coming years. However, the role of man in the production process should not be completely eliminated, but solutions should be sought that support and intellectualize his work.

The modern developments in the Internet of Things (IoT) have been producing an increasing number of interconnected devices, allowing a variety of elegant applications. These IoT devices generate huge information that require smart data study and processing methods, for instance, deep learning (DL). Remarkably, the DL algorithms, when amalgamated with the industrial IoT (IIoT), can facilitate a mixture of applications, for example, elegant assembling, effective manufacturing, efficient networking, accident identifying, and prevention of errors. Also, IIoT can change the way in which industries use to produce autonomous self-healing machines and improve stocks using machine learning (ML). Alternatively, ML carries hidden imminence in IoT data for quick, automated answers, and enhanced assessment making. ML for IoT can be used to venture future trends, become aware of anomalies, and augment intelligence by consuming images, videos, and audios. Various industries currently use ML in IoT to keep up acceptable standards as well as maintain predictable things in terms of benefits and profits at different times. Banks and other businesses in the financial industry make use of ML technology for two main reasons: to recognise significant imminence in data and to avoid scams. Data mining can also be used to make out clients with high-risk outlines or use cyber-surveillance to locate notifying indications of fraud. IIoT has been a means to digital transformation in manufacturing. It takes up a network of sensors to implement a serious building of data and brings cloud software into action to use this data in expensive approaches about the effectiveness of manufacturing procedures. Machines can gain knowledge of the data and algorithms that are accountable for origin faults in the system to identify the errors before those can do damage. Manufacturers can make use of ML to get better safeguarding processes and facilitate them to construct real-time, smart judgments derived from data. So the main aim of this chapter is to focus on data acquisition, integration, and predictive control in the industry using ML for IIoTs for real-time collection and analysis of thousands of measurement points, an early detection of equipment failures and process disturbances, detection of new types of anomalies as they occur for the first time, and Web-based UI for handling alerts.

The rapid development of the supply chain of the Internet of Things (IoT) industry may trigger financial risk contagion among IoT manufacturers. This article collects data on listed IoT companies in the Chinese market from 2010 to 2019 and explores the development of the environment for the IoT industry. Two dynamic time wrapping (DTW) networks are created to analyze the topological structures of the IoT industry environment. Both the standard DTW and strongly connected DTW networks are revolutionary in terms of their interconnectedness in the IoT industry. We found that the level of clustering and transitivity of the network continued to decline between 2010 and 2019; i.e., the efficiency of financial risk contagion on IoT networks was significantly reduced. This article contributes to the literature in two aspects. First, it reveals that China's IoT industry is increasingly competitive; financial risks have become more difficult to transfer. The IoT industry has become more robust and exhibits a lower likelihood of financial risk contagion. Second, the article provides empirical evidence for the theory of coevolution, showing that risk contagion ability in an industry setting is decreasing with the development of individual firms.

Intelligent manufacturing is the future development direction of manufacturing industry. There are still some problems lurk in the traditional mode of production, such as “information island” and “network security”, which result in low productivity but high-cost production, and information leakage. With application of the new generation information technology in manufacturing field is expanded, the deep integration of information technology and manufacturing industry promotes the development of production towards intellectualization, networking, and service. Single mode of production is gradually replaced by the way of large-scale collaborative production, so the traditional industrial internet of things (IoT) and manufacturing architecture are not suitable for the needs of actual production any more. For instance, the great challenges of compatibility and coordination of interface protocols, huge data, cost, and safety. In order to solve these problems, a new model of industrial IoT with security mechanism used in intelligent workshop is proposed. Firstly, this article analyzes the current situation of manufacturing industry and information technology, a secure transmission model of industrial IoT was designed based on the new generation information technology. Secondly, combined with the structure of the IoT and the real production demands, a six-tier industrial IoT architecture based on the new generation information technology is proposed. Finally, the three dimensions of the intelligent manufacturing model are expanded according to the architecture of the IoT. Not only a six-tier intelligent manufacturing system framework based on the new generation of information technology is obtained, but also the network security mechanism is introduced. On one hand, it realizes the coordinated operation of industrial chain, supply chain, and value chain of production process, on the other hand, it also improves the networking, intellectualization, and service level of industrial production. What is more the system can also provide the reference model for the various application of IoT.

The rise of 5G networks represents a significant leap in technology, marked by increased bandwidth, better service quality, reduced latency, and enhanced support for numerous connected devices compared to their predecessor, 4G. This advancement is crucial for fostering the Internet of Things (IoT) ecosystem, particularly in industrial sectors, where it enables the operation of intelligent Cyber-Physical Manufacturing Systems (CPMS). These systems leverage augmented intelligence, advanced manufacturing capabilities, real-time monitoring, and collaborative functions, collectively known as the Industrial Internet of Things (IIoT). Growing smart industries, driven by IIoT, produce vast amounts of data and require high-bandwidth, low-latency connections, which are shortcomings in earlier generations like 3G and 4G. To address these needs, the latest iteration, 5G, is designed to accommodate features essential for CPMS-enabled IIoT applications. In line with this shift, the proposed architectural framework utilizes 5G functionalities to enhance industry-specific smart services, including Enhanced Mobile Broadband (eMBB), Massive Machine-Type Communication (mMTC), Ultra-Reliable and Low-Latency Communication (URLLC), and Narrowband Internet of Things (NB-IoT). This paper presents an innovative architectural plan tailored for the forthcoming fifth-generation-enabled Industrial Internet of Things, meeting the evolving requirements of smart manufacturing technology in the industrial sector.

This study uses a wind turbine case study as a subdomain of Industrial Internet of Things (IIoT) to showcase an architecture for implementing a distributed digital twin in which all important aspects of a predictive maintenance solution in a DT use a fog computing paradigm, and the typical predictive maintenance DT is improved to offer better asset utilization and management through real-time condition monitoring, predictive analytics, and health management of selected components of wind turbines in a wind farm. Digital twin (DT) is a technology that sits at the intersection of Internet of Things, Cloud Computing, and Software Engineering to provide a suitable tool for replicating physical objects in the digital space. This can facilitate the implementation of asset management in manufacturing systems through predictive maintenance solutions leveraged by machine learning (ML). With DTs, a solution architecture can easily use data and software to implement asset management solutions such as condition monitoring and predictive maintenance using acquired sensor data from physical objects and computing capabilities in the digital space. While DT offers a good solution, it is an emerging technology that could be improved with better standards, architectural framework, and implementation methodologies. Researchers in both academia and industry have showcased DT implementations with different levels of success. However, DTs remain limited in standards and architectures that offer efficient predictive maintenance solutions with real-time sensor data and intelligent DT capabilities. An appropriate feedback mechanism is also needed to improve asset management operations.

PurposeThe Internet of Things (IoT) provides exciting opportunities for the construction industry to solve its time and resource constraints and frequent defaults. This study seeks to identify and rank the perceived importance level of principal research areas associated with the IoT and the construction industry by utilising a scientific mapping tool (i.e. VOSviewer). Such knowledge would enable key drivers for successful adoption of the IoT and digitisation technologies to be outlined. An analysis of key drivers and research trends that facilitates the development of a roadmap for applying the IoT and digital technologies in the construction sector is therefore much needed.Design/methodology/approachAn interpretivist philosophical lens was adopted to analyse published work as secondary data, where each publication represented a unit of analysis. A total of 417 peer-reviewed journal review articles covering the IoT within the construction domain were systematically reviewed using a mixed-methods approach, utilising qualitative-scientometric analyses techniques.FindingsThe results revealed a field of study in a fledgling stage, with a limited number of experts operating somewhat in isolation and offering single-point solutions instead of taking an integrated “holistic” approach. Key publication outlets were identified and the main focus of research undertaken being in the technical areas of smart buildings, smart construction objects and environmental sustainability. The major effects of adopting the IoT within the construction industry were identified as high-speed reporting, complete process control, data explosion leading to deep data analytics, strict ethical and legal expectations. Key drivers of the IoT adoption were outlined: interoperability; data privacy and security; flexible governance structures; proper business planning and models.Practical implicationsThe study benefits researchers and industry practitioners alike. For researchers, the identified gaps reveal areas of high priority in future research. For construction companies, particularly small to medium-sized businesses, the study raises awareness of the latest developments and potential applicability of the IoT in the industry. For government agencies and policymakers, this study offers a point of reference in directing the adoption of the IoT smoothly in the construction sector and provides guidelines and standards for maximising the potential benefits.Originality/valueThe study is the first scientometric review of the existing body of knowledge in the context of application of the IoT in the construction industry. Findings expose knowledge gaps in contemporary research, specifically, a broader consideration of organisational adjustments needed to accommodate the IoT usage, economic analyses and impediments to wider acceptance.

Industry 4.0 / Industrial Internet of Things - Related Technologies and Requirements for a Successful Digital Transformation: An Investigation of Manufacturing Businesses Worldwide

Hybrid platforms are based on the new technologies of the fourth industrial revolution such as IIoT and cloud computing, which allow local and remote operation. A hybrid system presents a low cost of operation and maintenance. This paper describes the development of a hybrid IIoT (Industrial Internet of Things) & Arduino IoT (Internet of Things) Cloud platform for the analysis of continuous industrial process data in real time through the cloud. This procedure offers a more stable and secure control through the new Cloud technology. The IIoT platform consists of industrial equipment and sensors that are controlled through Delta IIoT devices. An Arduino IoT Cloud with ESP32 module has the functionality of a router that allows sending data to the cloud, thereby achieving the control and monitoring of level and temperature variables of a continuous process. With this type of architecture, the industrial sector has great opportunities to improve its competitiveness by being able to analyze large amounts of data collected from sensors. In addition, the hybrid platform guarantees the visibility of the variables in real time from anywhere in the world where connectivity exists. The results are presented in graphs and are promising as there is no data loss in the cloud and data latency is almost negligible.KeywordsIndustrial Internet of Things (IIoT)Internet of Things (IoT)Industry 4.0Hybrid IIoT

This chapter aims to explore the managerial aspects of implementing artificial intelligence (AI) governance and digital twins (DTs) within the context of Industrial Internet of Things (IIoT). It focuses on establishing ethical and secure cybersecurity frameworks to enhance operational efficiency and protect critical infrastructure. The focus is on conducting systematic literature review. It begins with an overview of AI governance principles and the role of DTs in IIoT. The research then delves into the challenges and best practices for integrating these technologies, emphasising the importance of ethical considerations and robust cybersecurity measures. The study reveals that effective AI governance and the use of DTs can significantly improve the security and operational performance of IIoT systems. However, the integration of these technologies requires careful planning and adherence to ethical standards. For managers in the industrial sector, this chapter provides actionable insights into developing and implementing ethical and secure AI governance frameworks. It highlights the importance of fostering a culture of cybersecurity awareness and ethical responsibility, which are essential for safeguarding DTs and IIoT systems against cyber threats. This chapter contributes to the emerging discourse on AI governance and DTs by offering a managerial perspective on the ethical and secure deployment of these technologies in IIoT. It fills a gap in existing literature by providing a comprehensive analysis of the interplay between AI, DTs, and cybersecurity, and offering practical recommendations for industry leaders.

Blockchain and federated learning-based intrusion detection approaches for edge-enabled industrial IoT networks: a survey

Biopharma Is Going Digital … Bit by Bit

SummaryThe evolution of Internet of Things (IoT) has led to the development of Industrial Internet of Things (IIoT). IIoT is one the widely applied areas to facilitate people in the manufacturing world. The adoption of IIoT automates sensing, capturing, communicating, and processing in real time. To understand how rapidly IoT and IIoT are growing, this article examines the emergence of 5G‐enabled IIoT, current research trends in IIoT, key milestones achieved in IIoT, and IoT applications specific to 5G‐enabled IIoT. The paper presents the state‐of‐the‐art in networking layered framework of IIoT and comparing relationships of technologies of cloud computing as well as edge computing paradigms. We also explored the type of security attacks and their preventive measures in an IIoT‐driven 5G technology. We have also highlighted the revolution of IIoT‐driven 5G framework which satisfies the demands of IIoT applications.

Internet of Things (IoT) is a concept that proposes the inclusion of physical devices as a new form of communication, connecting them with various information systems. Nowadays, IoT cannot be reduced to smart homes as due to the recent technological advances, this concept has evolved from small to large-scale environments. There was also a need to adopt IoT in several business sectors, such as manufacturing, logistics or transportation in order to converge information technologies and technological operations. Due to this convergence, it was possible to arrive at a new IoT paradigm, called the Industrial Internet of Things (IIOT). However, in IIoT, it is also necessary to analyze and interact with a real system through a virtual production, which refers to the use of Augmented Reality (AR) as a method to achieve this interaction. AR is the overlay of digital content in the real world, and even play an important role in the life cycle of a product, from its design to its support, thus allowing greater flexibility. The adoption of interconnected systems and the use of IoT have motivated the use of Artificial Intelligence (AI) because much of the data coming from several sources is unstructured. Several AI algorithms have been used for decades aiming at “making sense” of unstructured data, and transforming it into relevant information. Therefore, converging IoT, AR, and AI makes systems become increasingly autonomous and problem solving in many scenarios.

Industrial Internet of Things (IIoT) refers to the extension of the Internet of Things and It is used for industrial purposes such as manufacturing, supply chain.IIoT can be connected with billions of industrial devices and machines that supported machine learning and big data technology. Because of its potential to authorize faster and better decision making, the IIoT becomes essential for supply chain processes. The IIoT is set to revolutionize the supply chain with both operational efficiencies and revenue opportunities made possible with just this type of transparency. This study attempts to fill this gap by developing a conceptual framework for predicting the advantage of the IIoT and supply chain performance. This chapter synthesizes existing literature reviews and making bibliometric analysis on the IIoT and supply chain. Additionally, It also outlines avenues and opportunities for future research aiming at the contribution of the IIoT to Supply Chain processes.