Over the past decade, there has been a significant increase in interest in digital twin (DT) technology in a variety of domains. While research on DTs of single assets was initially prevalent, there has been a notable shift towards distributed systems of DTs, which connect to each other to collaborate. Typically, collaboration is enabled by DTs providing services that can be consumed by other DTs. In service-oriented systems, a service is typically identified by type information. However, this is not sufficient in distributed DT systems, where DTs associated with different physical entities may provide the same type of service. Consequently, selecting the appropriate service depends not only on the service type, but also on the associated physical entity. However, requiring DTs to know the mapping of services to their physical environment is not feasible for large dynamic systems. This paper presents a novel proximity-based service discovery method that allows DTs to select services based on service type and their proximity to other objects. That is, service specifications are fully abstracted from the mapping of services to physical objects, relieving DTs from maintaining information about this mapping. Furthermore, service discovery is robust to changes in the physical environment and service population. The proposed service discovery method has been implemented on top of a spatial DBMS. We argue that this implementation is optimal in terms of network utilization and latency, and perform comprehensive evaluations to show the performance of discovery queries as a function of their complexity. 

Over the last decade, digital twin technology has gained increasing interest in various fields, such as industrial engineering and manufacturing, healthcare, transportation, energy, logistics, or city operations. While digital twins of single assets dominated in the beginning, we are seeing more and more digital twins for systems of assets, where digital twins need to collaborate with other digital twins to accomplish their objectives. As in service-oriented systems, digital twins provide services that can be used by other twins. While in service-oriented systems a service is typically identified by type information, this is not sufficient in digital twin systems, where there may be a large number of service instances of the same type that are not interchangeable. In this paper, we present a proximity model that allows digital twins to specify the required services by so-called neighborhoods, whose definition is based on spatial relationships between client and service instances. This concept not only simplifies service discovery in digital twin systems, but also allows for efficient implementations. To support this, we illustrate how service discovery based on the proposed proximity model can be implemented on top of a spatial DBMS.