The industrial landscape is about to undergo a revolution thanks to the convergence of emerging technologies. Specifically, the integration of blockchain with the digital twin-enabled industrial internet of things (IIoT) within mixed reality environments has the potential to do just that. This chapter presents a thorough analysis of the applications, advantages, and difficulties of various technologies while examining their potential for synergy. The digital twin provides real-time data monitoring, analysis, and predictive maintenance capabilities. The industrial internet of things establishes connections between tangible objects and sensors, enabling smooth communication and interchange of data. This chapter investigates the use of mixed reality (MR) technology to integrate blockchain technology with the IIoT that is enabled by digital twins. The potential for improving data security, trust, and transparency in industrial applications through the integration of blockchain with IIoT and MR could aid in the development of the Industry 4.0 paradigm.
TopIntroduction
In addition to advancements in communication technology, the emergence of information technologies like the Internet of Things (IoT), Cloud Computing (CC), and Cyber-Physical Systems (CPS) has revolutionized the system approach to information transmission between multiple sources. Digitalization is undoubtedly to blame for the revolution that has occurred in all facets of modern life (Rasheed et al., 2020). One of the brilliant ideas that have evolved as a result of the advancement and revolt in Information-Communication Technology (ICT) is the Digital Twin (DT). A digital depiction of an actual system from the viewpoint of CPS is called the DT. As a result, a convinced system's virtual complement simulates its real performance. The information from the physical system is characterized during whole lifecycle development by the digital data from the virtual system combined with a real system (Jones et al., 2019) As a result, when digital and physical counterparts are combined, an efficient method for managing, regulating, and enhancing coordination is produced when the system functions. In addition, the execution system reaction and all data gathered from physical sensors are recorded by the DT. Therefore, DT's crucial job is to forecast and diagnose the physical system's behavior in order to identify any faults or malfunctions and to provide the system with data so that it may receive the best maintenance possible (Boyes & Watson, 2022; Fuller et al., 2020).
The fundamental objective of DT is to save costs and maintenance efforts while improving productivity and operating flexibility through the use of a virtual representation of the system. On the other hand, to solve the difficulties of Industry 4.0 that come from the manufacture of linked components, DT might be performed, depending on the requirements, either at the edge layer or on a system housed in the cloud. Conversely, DT advocates for both elucidating the behavior of the real system and identifying optimal solutions for the physical model (Tao et al., 2019). DT makes use of a basic modeling system, transparent simulation, and simulation processes to enhance control action, anticipate system performance, and support decision-making. Grieves presented the idea of digital twins in 2002, but NASA was the first to use it to build models of virtual spacecraft (Yu et al., 2022). The literature indicates that current DT works and implementations are still in their infancy and demand a great deal of work. Nonetheless, a multitude of applications, including biomedical systems, manufacturing, aerospace, agriculture, smart cities, and weather forecasting, have successfully incorporated it. In addition, to develop an effective digital twin system for any physical system, more specialized engineers and computer scientists are required in this crucial field. Their duties will encompass constructing and developing the fundamental product prototype in addition to producing an elaborate description of the virtual system (Singh et al., 2022).
Background and Motivation
The rapid evolution of Industry 4.0 has transformed manufacturing and industrial processes. This revolution is enabled by the Industrial Internet of Things (IIoT), a network of networked devices and systems that gather, analyze, and share industrial data. Real-time monitoring, predictive maintenance, and process optimization have increased industrial production and efficiency thanks to the IIoT (Patel et al., 2022; Uhlemann et al., 2017). In industrial applications, Digital Twins have become popular. A Digital Twin recreates a physical object or system for real-time monitoring, modeling, and analysis. Creating digital copies of real assets and processes helps firms gather insights, optimize operations, and make better decisions (Alam & El Saddik, 2017; El Saddik, 2018).
Despite their potential, IIoT and Digital Twins suffer data security, trust, and transparency issues. IIoT devices' interconnectedness and reliance on digital representations of physical systems presents vulnerabilities that bad actors can exploit. Managing data integrity and trustworthiness with various stakeholders is difficult. This research paper proposes integrating blockchain technology into the IIoT ecosystem and using Mixed Reality (MR) technology to bridge the digital and physical worlds. Blockchain, a decentralized and immutable ledger, is used in finance and supply chain management to secure data and build trust. Its use to IIoT and Digital Twins could improve industrial data security, trust, and transparency, making it an intriguing area of research (Schleich et al., 2017).