# Digital Twin and Internet of Things--Current Standards Landscape
###### Abstract  
Industry 4.0 is revolutionizing industrial production by bridging the physical and the virtual worlds and further improving digitalization. Two essential building blocks in industry 4.0 are digital twins (DT) and the internet of things (IoT). While IoT is about connecting resources and collecting data about the physical world, DTs are the virtual representations of resources organizing and managing information and being tightly integrated with artificial intelligence, machine learning and cognitive services to further optimize and automate production. The concepts of DTs and IoT are overlapping when it comes to describing, discovering and accessing resources. Currently, there are multiple DT and IoT standards covering these overlapping aspects created by different organizations with different backgrounds and perspectives. With regard to interoperability, which is presumably the most important aspect of industry 4.0, this barrier needs to be overcome by consolidation of standards. The objective of this paper is to investigate current DT and IoT standards and provide insights to stimulate this consolidation. Overlapping aspects are identified and a classification scheme is created and applied to the standards. The results are compared, aspects with high similarity or divergence are identified and a proposal for stimulating consolidation is presented. Consensus between standards are found regarding the elements a resource should consist of and which serialization format(s) and network protocols to use. Controversial topics include which query language to use for discovery as well as if geo-spatial, temporal and historical data should be explicitly supported. digital twin; internet of things; interoperability; standardization +
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thereby enable better decision-making and increasing the level of automation as well as overall productivity. DTs with such enhanced capabilities are also referred to as cognitive twins (CT) [5]. The importance of industry 4.0 and its related concepts and technologies has been widely acknowledged [6; 7; 8; 9]. Between 2012 and 2016, IoT (or the similar 'internet of everything') was considered one of the ten most important technology trends of the year by Gartner, followed by DT as one of the five most important technology trends between 2017 and 2019 [10; 11; 12; 13; 14; 15; 16; 17]. In 2020, the most important technology trend according to Gartner is hyperautomation, which refers to the totality of automation efforts across an entire organization and is tightly coupled to industry 4.0 and DT as 'hyperautomation often results in the creation of a digital twin of the organization' [18]. It seems obvious that 'the rise of digital twins coincides with the rise of the IoT' [19] as only the vast amount of sensor data and device metadata from the IoT creates the requirement for organizing and managing all that information in an adequate way, which is realized by the concept of DTs. Although the relation between DT and IoT seems to be clear at first glance--IoT connects devices to the internet and collects data while the concept of DTs is used to structure and manage that data to be further used for optimization and automation with the help of AI and ML algorithms--they have some overlap and therefore are not clearly separable in some aspects. A fundamental aspect of both IoT and DT is the fact that they center on resources and require a formal and machine-readable representation of these resources. Resources can be devices, sensors, actuators, or machines in the context of IoT or assets, processes, or systems in the context of DT. Figure 1 provides a visual representation of further resource-related functionality that is part of both domains. Besides a formal and machine-readable resource description created by a provider, they share the functionality for potential consumers to discover such resource descriptions and access the resources.