This letter presents an approach to schedule observations from different sensors in an environment to ensure their timely delivery and build a digital twin (DT) model of the system dynamics. At the ...cloud platform, DT models estimate and predict the system's state, then compute the optimal scheduling policy and resource allocation strategy to be executed in the physical world. However, given limited network resources, partial state vector information, and measurement errors at the distributed sensing agents, the acquisition of data (i.e., observations) for efficient state estimation of system dynamics is a non-trivial problem. We propose a Value of Information (VoI)-based algorithm that provides a polynomial-time solution for selecting the most informative subset of sensing agents to improve confidence in the state estimation of DT models. Numerical results confirm that the proposed method outperforms other benchmarks, reducing the communication overhead by half while maintaining the required estimation accuracy.
The automation of digital twinning for existing reinforced concrete bridges from point clouds remains an unresolved problem. Whilst current methods can automatically detect bridge objects in point ...clouds in the form of labelled point clusters, the fitting of accurate 3D shapes to point clusters remains largely human dependent largely. 95% of the total manual modelling time is spent on customizing shapes and fitting them correctly. The challenges exhibited in the fitting step are due to the irregular geometries of existing bridges. Existing methods can fit geometric primitives such as cuboids and cylinders to point clusters, assuming bridges are comprised of generic shapes. However, the produced geometric digital twins are too ideal to depict the real geometry of bridges. In addition, none of the existing methods have explicitly demonstrated how to evaluate the resulting Industry Foundation Classes bridge data models in terms of spatial accuracy using quantitative measurements. In this article, we tackle these challenges by delivering a slicing-based object fitting method that can generate the geometric digital twin of an existing reinforced concrete bridge from four types of labelled point cluster. The quality of the generated models is gauged using cloud-to-cloud distance-based metrics. Experiments on ten bridge point cloud datasets indicate that the method achieves an average modelling distance of 7.05 cm (while the manual method achieves 7.69 cm), and an average modelling time of 37.8 s. This is a huge leap over the current practice of digital twinning performed manually.
•An object fitting method that can digitally twin bridges is proposed.•The method can rapidly twin bridge concrete elements.•Local configurations offer characterization to approximate the global topology.•The resulting geometric digital twins are evaluated using quantitative metrics.
Mobile edge computing is one of the key enabling technologies of smart industry solutions, providing agile and ubiquitous services for mobile devices (MDs) through offloading latency-critical tasks ...to edge service providers. However, it is challenging to make optimal decisions of computation offloading and resource allocation while ensuring the privacy and information security of MDs. Consequently, we consider a new vision of digital twin (DT) empowered edge networks, where the optimization problem is formulated as a two-stage incentive mechanism. First, the resource allocation strategy is determined by the interaction among DTs according to the credit-based incentives. Afterward, a distributed incentive mechanism based on the Stackelberg-based alternating direction method of multipliers is opted to obtain the optimal offloading and privacy investment strategies in parallel. Numerical results show that the proposed two-stage incentive mechanism achieves effective resource allocation and computation offloading while simultaneously improving the privacy and information security of MDs.
As a key service of the future 6G network, healthcare digital twin is the virtual replica of a person, which employs Internet of Things (IoT) technologies and AI-powered models to predict the state ...of health and provide suggestions to a range of clinical questions. To support healthcare digital twins, the right cyber resilience technologies and policies must be applied and maintained to preserve cyber resilience. Vulnerability detection is a fundamental technology for cyber resilience in healthcare digital twins. Recently, deep learning (DL) has been applied to address the limitations of traditional machine learning in vulnerability detection. However, it is important to consider code context relationships and pay attention on the vulnerability related keywords for searching an IoT vulnerability in healthcare digital twins. Due to massive software and complexity of healthcare digital twin, a full automatic solution is really needed for assisting cyber resilience check in the real-world scenarios. This article presents a novel scheme for recognising potential vulnerable functions to support healthcare digital twins. We develop a new deep neural model to capture bi-directional context relationships among the risky code keywords. A number of well-designed experiments are carried out on a large ground truth, which consists of tens of thousands of vulnerable and non-vulnerable functions from IoT related software. The results show our new scheme outperforms the state-of-the-art DL-based methods for vulnerability detection.
Road infrastructure systems have been suffering from ineffective maintenance strategies, exaggerated by budget restrictions. A more holistic road-asset-management approach enhanced by data-informed ...decision making through effective condition assessment, distress detection and future condition predictions can significantly enhance maintenance planning, prolonging asset life. Recent technology innovations such as digital twins have great potential to enable the needed approach for road condition predictions and proactive asset management. To this end, machine learning techniques have also demonstrated convincing capabilities in solving engineering problems. However, none of them has been considered specifically within a digital twin context. There is therefore a need to review and identify appropriate approaches for the usage of machine learning techniques with road digital twins. This paper provides a systematic literature review of machine learning algorithms used for road condition predictions and discusses findings within the road digital twin framework. The results show that existing machine learning approaches suitable and mature for stipulating successful road digital twin development. Moreover, the review, while identifying gaps in the literature, indicates several considerations and recommendations required on the journey to road digital twins and suggests multiple future research directions based on the review summaries of machine learning capabilities.
Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation, optimisation and accurate forecasting. ...However, the theoretical framework and practical implementations of digital twin (DT) are yet to fully achieve this vision at scale. Although an increasing number of successful implementations exist in research and industrial works, sufficient implementation details are not publicly available, making it difficult to fully assess their components and effectiveness, to draw comparisons, identify successful solutions, share lessons, and thus to jointly advance and benefit from the DT methodology. This work first presents a review of relevant DT research and industrial works, focusing on the key DT features, current approaches in different domains, and successful DT implementations, to infer the key DT components and properties, and to identify current limitations and reasons behind the delay in the widespread implementation and adoption of digital twin. This work identifies that the major reasons for this delay are: the fact the DT is still a fast evolving concept; the lack of a universal DT reference framework, e.g. DT standards are scarce and still evolving; problem- and domain-dependence; security concerns over shared data; lack of DT performance metrics; and reliance of digital twin on other fast-evolving technologies. Advancements in machine learning, Internet of Things (IoT) and big data have led to significant improvements in DT features such as real-time monitoring and accurate forecasting. Despite this progress and individual company-based efforts, certain research and implementation gaps exist in the field, which have so far prevented the widespread adoption of the DT concept and technology; these gaps are also discussed in this work. Based on reviews of past work and the identified gaps, this work then defines a conceptualisation of DT which includes its components and properties; these also validate the uniqueness of DT as a concept, when compared to similar concepts such as simulation, autonomous systems and optimisation. Real-life case studies are used to showcase the application of the conceptualisation. This work discusses the state-of-the-art in DT, addresses relevant and timely DT questions, and identifies novel research questions, thus contributing to a better understanding of the DT paradigm and advancing the theory and practice of DT and its allied technologies.
•We study various research and industrial works and identify the reasons for delay in widespread adoption of digital twin despite its potential. A major shortcoming identified is the lack of universal consensus on the definition and components of digital twin. To this end, we present a conceptualisation and discuss the best practices for its implementation.•We discuss the current state of machine learning and big data in digital twin, and find that the advancement in these technologies has impacted the adoption and ideal implementation of digital twin.•We assess the implementation of digital twins in various domains, and find that the architecture is highly impacted by the application domain. This calls for a specialised digital twin for each domain. However, the basic concept of digital twins should still be universal, it is only the prioritising of components that is affected.•Since digital twins might be implemented across various collaborators or industries, regulations and techniques for data sharing and security need to be implemented. This area is not yet well researched and explored.•Evaluation and resilience metrics are required to fulfil the ‘self-evolution’ property of digital twins. This is again domain-dependent, and lacking in the current literary works.
Digital twin (DT) technology, as one of the top strategic technology trends for 2020, has received widespread attention and has gradually been widely used in smart manufacturing equipment. However, ...there is a lack of systematic evaluation guidance, which accesses the applicability of DT technology for specific applications of smart manufacturing equipment. At the same time, CNC machine tool (CNCMT) as an essential part of smart manufacturing equipment also faces the above dilemma. Motivated by this need, a digital twin technology applicability evaluation method for CNCMT is proposed in this paper. This method firstly analyzes the application-oriented requirements of DT-based CNCMT to obtain the optimal evaluation index and structure model. And then, the DT technology applicability evaluation of CNCMT based on the optimal evaluation model as well as system engineering algorithms is researched. With this effort, DT technology applicability of CNCMT is quantified starting from the initial stage aiming at its specific application. Then, the applicability of DT technology to address specific applications of CNCMT can be clarified. At last, the applicability evaluation for DT-based CNCMT cutting tool life prediction is carried out as an application case to show the implementation flow of the proposed method and verify its operability and effectiveness.
The integration of Digital Twin technology in small and medium‐sized enterprises (SMEs) has brought forth a transformative paradigm in engineering practices. This paper presents a comprehensive ...overview of the use of Digital Twin in SMEs and its implementation of advanced digital technologies in an engineering context. The paper highlights key benefits, such as the ability to simulate, analyze, and predict the behavior of products or processes before their physical instantiation. By leveraging advanced modeling and simulation techniques, SMEs can efficiently explore multiple design iterations, identify potential issues, and optimize engineering solutions without costly physical prototyping. Furthermore, the paper discusses integrating other advanced digital technologies, such as the Internet of Things (IoT) and Artificial Intelligence (AI), with Digital Twin to create intelligent and interconnected systems. The implementation of Digital Twin technology in SMEs extends beyond product design to encompass various aspects of the product lifecycle, fostering efficiency, accuracy, and innovation. However, challenges such as data integration, cybersecurity, and skill requirements are addressed. With proper planning and investment, SMEs can unlock the full potential of Digital Twin technology to gain a competitive edge in the dynamic engineering domain.
In conclusion, this paper demonstrates that the use of Digital Twin in SMEs and its integration with advanced digital technologies revolutionizes engineering practices. By offering virtual representations of physical assets and processes, Digital Twin enables SMEs to make informed decisions, optimize performance, and drive innovation. As the technology evolves, it becomes an indispensable tool for SMEs seeking to thrive in the ever‐evolving engineering landscape.
Over the past years, the concept of human-machine interaction has received ample attention to achieve hybrid automation in manufacturing. A form of hybrid automation is taking benefit from the ...synergistic effect of human-robot collaboration. When used in assembly, the requirement of flexibility, adaptability and safety makes the design and redesign of human-robot collaborative (HRC) systems a complex and prone to error process. The use of time-based continuous simulations can offer safe virtual space for testing and validation thus easing the design of complex HRC systems. However, conventional simulations don't allow to experience the future production system as an end-user in an immersive environment. This paper explores the technological development in virtual reality (VR) for design of human-centred production systems and develops a unified framework to integrate human-robot simulation with VR. The simulation as an event-driven simulation helped in estimating the human-robot cycle times, developing process-plan, layout optimisation and robot control program. The same simulation is used in VR to interact with the production equipment and particularly with the robot. Additionally, AWS Sumerian environment is used to create a virtual robot to assist VR user in the design process.
In next-generation Internet of Things (IoT) deployments, every object such as a wearable device, a smartphone, a vehicle, and even a sensor or an actuator will be provided with a digital counterpart ...(twin) with the aim of augmenting the physical object’s capabilities and acting on its behalf when interacting with third parties. Moreover, such objects can be able to interact and autonomously establish social relationships according to the Social Internet of Things (SIoT) paradigm. In such a context, the goal of this work is to provide an optimal solution for the social-aware placement of IoT digital twins (DTs) at the network edge, with the twofold aim of reducing the latency (i) between physical devices and corresponding DTs for efficient data exchange, and (ii) among DTs of friend devices to speed-up the service discovery and chaining procedures across the SIoT network. To this aim, we formulate the problem as a mixed-integer linear programming model taking into account limited computing resources in the edge cloud and social relationships among IoT devices.
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