Resilient Production Control Using Digital Twins in the Industrial Internet of Things

Biopharma Is Going Digital … Bit by Bit

Methodology to develop Digital Twins for energy efficient customizable IoT-Products

The convergence of Industrial Internet of Things (IIoT) and digital twin technology offers new paradigms for process automation and control. This paper presents an integrated IIoT and digital twin framework for intelligent control of a gas–liquid separation unit with interacting flow, pressure, and differential pressure loops. A comprehensive dynamic model of the three-loop separator process is developed, linearized, and validated. Classical stability analyses using the Routh–Hurwitz criterion and Nyquist plots are employed to ensure stability of the control system. Decentralized multi-loop proportional–integral–derivative (PID) controllers are designed and optimized using the Integral Absolute Error (IAE) performance index. A digital twin of the separator is implemented to run in parallel with the physical process, synchronized via a Kalman filter to real-time sensor data for state estimation and anomaly detection. The digital twin also incorporates structured singular value (μ) analysis to assess robust stability under model uncertainties. The system architecture is realized with low-cost hardware (Arduino Mega 2560, MicroMotion Coriolis flowmeter, pneumatic control valves, DAC104S085 digital-to-analog converter, and ENC28J60 Ethernet module) and software tools (Proteus VSM 8.4 for simulation, VB.Net 2022 version based human–machine interface, and ML.Net 2022 version for predictive analytics). Experimental results demonstrate improved control performance with reduced overshoot and faster settling times, confirming the effectiveness of the IIoT–digital twin integration in handling loop interactions and disturbances. The discussion includes a comparative analysis with conventional control and outlines how advanced strategies such as model predictive control (MPC) can further augment the proposed approach. This work provides a practical pathway for applying IIoT and digital twins to industrial process control, with implications for enhanced autonomy, reliability, and efficiency in oil and gas operations.

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.

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.

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.

Emerging technologies such as digital twins and 6th Generation mobile networks (6G) have accelerated the realization of edge intelligence in Industrial Internet of Things (IIoT). The integration of digital twin and 6G bridges the physical system with digital space and enables robust instant wireless connectivity. With increasing concerns on data privacy, federated learning has been regarded as a promising solution for deploying distributed data processing and learning in wireless networks. However, unreliable communication channels, limited resources, and lack of trust among users, hinder the effective application of federated learning in IIoT. In this paper, we introduce the Digital Twin Wireless Networks (DTWN) by incorporating digital twins into wireless networks, to migrate real-time data processing and computation to the edge plane. Then, we propose a blockchain empowered federated learning framework running in the DTWN for collaborative computing, which improves the reliability and security of the system, and enhances data privacy. Moreover, to balance the learning accuracy and time cost of the proposed scheme, we formulate an optimization problem for edge association by jointly considering digital twin association, training data batch size, and bandwidth allocation. We exploit multi-agent reinforcement learning to find an optimal solution to the problem. Numerical results on real-world dataset show that the proposed scheme yields improved efficiency and reduced cost compared to benchmark learning method.

The rapid development of industrial Internet of Things (IIoT) requires industrial production towards digitalization to improve network efficiency. Digital Twin is a promising technology to empower the digital transformation of IIoT by creating virtual models of physical objects. However, the provision of network efficiency in IIoT is very challenging due to resource-constrained devices, stochastic tasks, and resources heterogeneity. Distributed resources in IIoT networks can be efficiently exploited through computation offloading to reduce energy consumption while enhancing data processing efficiency. In this article, we first propose a new paradigm digital twin network to build network topology and the stochastic task arrival model in IIoT systems. Then, we formulate the stochastic computation offloading and resource allocation problem to minimize the long-term energy efficiency. As the formulated problem is a stochastic programming problem, we leverage Lyapunov optimization technique to transform the original problem into a deterministic per-time slot problem. Finally, we present asynchronous actor-critic algorithm to find the optimal stochastic computation offloading policy. Illustrative results demonstrate that our proposed scheme is able to significantly outperforms the benchmarks.

ABSTRACTThe fourth industrial revolution has driven the emergence of Digital Twins (DTs) and Industrial Internet of Things (IIoT) in manufacturing. However, the use of different definition has led to varied interpretations and inconsistent understanding of DTs. Thus, by exploring the gap between theoretical frameworks and practical implementations of IIoT‐based DTs in manufacturing, this paper aims to shed light on the DT phenomenon by considering the historical evolution and fundamental concepts of IIoT‐based DTs. Therefore, a systematic literature review was conducted to assess the ambiguity concerning DTs, particularly in distinguishing architectures and types. Therefore, this paper identifies IIoT‐based DTs in manufacturing by reviewing application‐oriented literature. As a result of a subsequent classification, this paper proposes a hierarchical classification based on communication dynamics (i.e., Uni‐directional and Bi‐directional) and information processing (i.e., use or non‐use of machine learning). Conclusively, this study proposes a comprehensive classification approach for IIoT‐based DTs and thus contributes to a more consistent understanding of the DT phenomenon. Moreover, this paper discusses key findings, as well as implications for research and practice. Finally potential avenues for future research are derived and the limitations of this study are discussed.

Digital equivalence is the main objective of Digital Twin (DT)s, and simulation is an integral part. DTs reach beyond traditional simulation with the help of real-time synchronization through Industrial Internet of Things (IIoT) technologies. Simulation supports off-line experimentations and planning, while DTs offer synchronous execution and modification. DTs help to understand “what may happen”. Also, they present “what is happening” and its management methodologies. In this paper, we present building aspects and an integration approach for a Digital Twin based Discrete-Event Simulation model. Our approach utilizes a data-driven agent-based simulation within a DT framework. It presents an integration layer that provides two essential features: reconfiguration and state initialization. It gives simulation models configurability and integrity that are required for operating within a DT. Our proposed approach is presented through a use case of a semiconductor manufacturing system. The proposed integration layer extends the usability of current Discrete-Event Simulation (DES) for a DT within a Cyber-Physical Production System.

Network slicing has been widely agreed as a promising technique to accommodate diverse services for the Industrial Internet of Things (IIoT). Smart transportation, smart energy, and smart factory/manufacturing are the three key services to form the backbone of IIoT. Network slicing management is of paramount importance in the face of IIoT services with diversified requirements. It is important to have a comprehensive survey on intelligent network slicing management to provide guidance for future research in this field. In this paper, we provide a thorough investigation and analysis of network slicing management in its general use cases as well as specific IIoT services including smart transportation, smart energy and smart factory, and highlight the advantages and drawbacks across many existing works/surveys and this current survey in terms of a set of important criteria. In addition, we present an architecture for intelligent network slicing management for IIoT focusing on the above three IIoT services. For each service, we provide a detailed analysis of the application requirements and network slicing architecture, as well as the associated enabling technologies. Further, we present a deep understanding of network slicing orchestration and management for each service, in terms of orchestration architecture, AI-assisted management and operation, edge computing empowered network slicing, reliability, and security. For the presented architecture for intelligent network slicing management and its application in each IIoT service, we identify the corresponding key challenges and open issues that can guide future research. To facilitate the understanding of the implementation, we provide a case study of the intelligent network slicing management for integrated smart transportation, smart energy, and smart factory. Some lessons learnt include: 1) For smart transportation, it is necessary to explicitly identify service function chains (SFCs) for specific applications along with the orchestration of underlying VNFs/PNFs for supporting such SFCs; 2) For smart energy, it is crucial to guarantee both ultra-low latency and extremely high reliability; 3) For smart factory, resource management across heterogeneous network domains is of paramount importance. We hope that this survey is useful for both researchers and engineers on the innovation and deployment of intelligent network slicing management for IIoT.

Participation and influence of citizens are crucial requirements to ensure sustainable and responsible land use planning. Notwithstanding, both documented and anecdotal evidence indicate that citizens’ influence in planning is still limited, especially in the agenda-setting phase. One explanation is that the design of geospatial tools and participation in land use planning is often limited to elites and experts and less to ordinary citizens. Recent studies propose that digital twins could ensure sustainable and responsible land use planning where the influence of citizens can be significantly observed. Adapting Kingdon's multiple streams framework to include elements of sociotechnical artefacts, this study aims to test this assertion and verify how citizens interacting with government institutions at different levels can employ digital twins to find agenda status for their land use issues and proposals. This study employed a systematic review following the PRISMA process to identify 34 articles about applying digital twins for citizen participation in planning decision-making. The study reveals that to improve the participation and influence of citizens, the development of digital twins needs to be citizen-centric. Data from digital twins should be presented in a form non-experts can understand. Qualities for digital twins to promote citizen participation include being interactive and user-friendly, smooth visualisation and immersive experience, inclusiveness, and privacy-oriented. In line with Kingdon’s framework, citizens could frame, convince, and propose land use interventions. The findings also reveal two ways of citizen participation: (1) providing data for developing digital twins and (2) utilising digital twins to analyse and identify key land use challenges and to push forward land use claims and proposals within a policy system.

In recent years, the Industrial Internet of Things (IIoT) came into being. IIoT connects sensors, industrial equipment, products, and staff in the factory, enabling context-awareness and industrial equipment automate control. The identity resolution system is a core infrastructure in IIoT. Similar to the role of Domain Name System (DNS) on the Internet, it is the entrance to the IIoT. The difference between them is their input and output. The input of the identity resolution system in IIoT is an identifier of an object. And the output is the mapping data attached to the identifier, including the product profile, a URL, or the identifier’s surrounding environment. However, how to deploy an identity resolution system in IIoT has not yet been conclusive. In this article, we provide a comprehensive survey on the potential identity resolution systems that may be used in IIoT. Firstly, an overview of the identity resolution system is introduced, including a reference framework that can be used to evaluate an identity resolution system. Then we review some influential identity resolution systems based on this reference framework. After that, we make a comparison from the perspective of whether they can meet IIoT requirements and technology selection. Finally, some challenges and broader perspectives are discussed.

Along with the fourth industrial revolution, smart factories are receiving a great deal of attention. Large volumes of real-time data that are generated at high rates, especially in industries, are becoming increasingly important. Accordingly, the Industrial Internet of Things (IIoT), which connects, controls, and communicates with heterogeneous devices, is important to industrial sites and is now indispensable. To ensure the fairness and quality of the IIoT with limited network resources, the network connection of the IIoT needs to be constructed more intelligently. Many studies are being conducted on the efficient use of the resources that are imposed on IIoT devices. Therefore, in this paper, we propose a collaboration optimization method for heterogeneous devices that is based on cloud–fog–edge architecture. First, this paper proposes a knowledge distillation-based algorithm that can collaborate on cloud–fog–edge computing on the basis of distributed control. Second, to compensate for the shortcomings of knowledge distillation, we propose a framework for combining a soft-label-based alarm level. Finally, the method that is proposed in this paper was verified through several experiments, and it is shown that this method can effectively shorten the response time and solve the problems of existing IIoT networks, and that it can be efficiently applied to heterogeneous devices.

The paper discusses industrial production management in an Industry 4.0 enterprise based on digital twins of manufacturing process and products. The principles of applying digital twins in the production cycle are defined. We present an example structure of an integrated automated production management system that includes the digital twins and a number of other systems. The functions of digital twins in such a system are indicated. The order of formation and development of digital twins in the course of the enterprise design and construction is considered. At the initial stage of collecting requirements and drawing up technical specifications for the enterprise design, we propose to create an approximate simulation model of the production cycle, which allows the feasibility assessment of key requirements. At the subsequent stages of the enterprise life cycle, the simulation model is enriched up to a full-fledged digital twin and augmented with other components of the management system. We show that the digital twin development can be performed at a high level of formal rigor and automation utilizing the mathematical device of category theory. For this purpose, the novel construction called flexible multicomma category is introduced, representing all possible enterprise architecture variants from a given viewpoint in the sense of the ISO/IEC/IEEE 42010 standard. A flexible multicomma is defined by a signature composed of functors that represent the architecture components from the selected viewpoint. We prove that a multicomma of any fixed shape derived from the signature is embedded into the flexible multicomma.