This research explores the existing definitions, concepts and applications surrounding the efficient implementation and use of digital twins (DTs) within civil infrastructure systems (CISs). The CISs within the scope of this research are as follows: transportation, energy, telecommunications, water and waste, as well as Smart Cities, which encompasses all of the previous. The research methodology consists of a review of current literature, a series of semi-structured interviews and a detailed survey. The outcome of this work is a refined definition of DTs within CISs, in addition to a set of recommendations for both future academic research and industry best practice.

Digital Twins in Civil Infrastructure Systems

In the process of developing digital twin enabling applications, a lack of reference to standards related to digital twin terms, architecture and models leads to differences between users' understanding of digital twin, and it is difficult to realize the interconnection of data, models, and services between different enterprises or fields. Therefore, digital twin, by its nature of interoperability between multiple domains, requires standardization as a pilot tool for implementation. This paper provides the background and introduction of digital twin technology based on the digital twin five-dimension model, then refers to the latest developments of digital twin standardization. We further analyze the challenges and provide suggestions of future digital twin standardization. The analysis of the standards landscape for digital twin consolidates information from governing bodies such as the International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), International Telecommunication Union (ITU), and Institute of Electrical and Electronics Engineers (IEEE)

A review of the technology standards for enabling digital twin

This study reviews the research works published in the last five years on Digital Twin (DT) technology for intelligent transportation systems, focusing on the use of DT in electromobility and autonomous vehicles. The review is carried out systematically, considering specific domains within intelligent transportation in which DT technology is applied in combination with Internet of Thing and 5G technologies. In addition, the paper discusses the current issues in electric vehicle services, such as tracking, monitoring, battery management systems, and connectivity, and how they can be addressed effectively through DT approaches.

Digital Twin in Intelligent Transportation Systems: a Review

Digital twins are beginning to revolutionize many industries in the last decade, offering a plethora of benefits to optimize the performance of industrial systems. They aim to create a continuously synchronized model of the physical system that allows for rapid adaptation to dynamics, primarily to unpredicted and undesired changes. A vast range of industrial domains have already benefited from digital twin technology, such as aerospace, manufacturing, healthcare, city management and shipping. In addition, recent research is beginning to explore the integration of digital twins into computer networks to enable more innovation and intelligent management. One of the building blocks of digital twin technology is the Internet of Things, where wireless sensors and actuators are deployed to provide interaction between the physical and digital worlds. This type of network is complex to manage due to its strong constraints, especially when controlling critical industrial applications, which gave rise to the Industrial Internet of Things (IIoT). We believe that optimizing IIoT will lead to effective integration of digital twins in Industry 4.0. In this paper, we design a Digital Twin Network (DTN) for IIoT where sensors, actuators, and communication infrastructure are replicated in the digital twin to enable real-time intelligent management of these networks. By taking advantage of Eclipse Hono which allows an efficient connectivity for the network devices and Eclipse Ditto for representing the devices states in a digital form and also providing easy access to these states for the DTN. In this way, cognitive network services such as predictive maintenance, sustainability features, network diagnostics, security management, resource allocation, energy optimization can be efficiently integrated and operated in the network lifecycle. We validate the proposed architecture by providing a resource allocation case study where we explain how the Time Slotted Channel Hopping mechanism is exploited in our architecture. 

Digital Twin Network for the IIoT using Eclipse Ditto and Hono

Digital twin (DT) has attracted a lot of attention from both industry and academia since it was proposed over a decade ago. A DT can be viewed as a virtual implementation of a real physical system (PS) and used as a representation of the PS for various applications. Despite the great potential of DTs in various fields, implementing DTs to obtain the desired functionality is not always straightforward. Specifically, accurate real-time synchronization between the features at a PS and its DT is essential for the DT to represent the PS. In this case, appropriate networking support is a key component to enable future DT development and applications. Currently, the research on DTs from a networking standpoint is still at an early stage, and only limited work has been done on DT implementation in practical systems. To fill this gap, this article investigates networking-related issues for DTs. Based on the existing literature, a feature-based method is provided for describing the desired properties and quality of DTs from the networking perspective. A stage-based implementation framework is presented for creating large-scale DTs for complex PSs by considering various networking constraints. Networking-related challenging issues and open research topics are discussed at the end. 

Digital Twins From a Networking Perspective

Most of the methods in operators' current 5G networks use expert knowledge assisted by machine learning algorithms to generate optimization decisions. However, these methods are inadaptive to the dynamic changes of high-dimensional network states, thus the result is often suboptimal. Reinforcement learning can better cope with high-dimensional network state space and parameter space. However, when applied to real network, challenges arise such as difficult to obtain data samples, time-consuming and risky to explore real networks during model training. To solve these problems, this paper proposes a combined approach of expert knowledge, reinforcement learning and digital twin for the self-optimization of mobile networks. By constructing a digital twin of the current network, the future network state is predicted based on which optimization decisions are generated by expert knowledge and reinforcement learning respectively, and then input into the digital twin. Digital twin simulates their rewards and decides a final action for execution. Simulation results have confirmed that the proposed scheme can achieve higher rewards than either expert knowledge or reinforcement learning, and can avoid negative impact on real network performance. This paper also describes several potential application scenarios for the proposed approach in 6G networks and discusses key issues for future research.

Juan Deng

Papers

A Digital Twin Approach for Self-optimization of Mobile Networks