## 1 Introduction  
Smart mobile devices are becoming the most common tool in everyday life. The performance and functionalities of such mobile devices have also highly improved. Various smart systems based on mobile devices such as smart parking [1, 2], navigation [3, 4], and rescue [5, 6] have been come up with accordingly. In particular, smart mobile services based on mobile-mobile interaction, e.g., food delivery, e-commerce, ride-hailing, and so on, have grown rapidly. This is because mobile devices have the advantage of providing online interaction easily through mobile applications. Matching users who interact with each other is an important process in a digital service. In the case of the food delivery service, a user who requests food is matched with a delivery man. In the case of ride-hailing services, a user who reserves a car is matched with a driver. In this way, the user can be matched with someone using the digital service. However, the digital service does not guarantee that the user finds a matching person in the physical world. Although the digital service provides information about the matching person, the responsibility for finding the matching person in the physical world falls on the user. It creates a situation where the user should physically contact an unidentified someone to check whether someone matches the user. Before the user contacts an unidentified person to find the matching person, it is hard for the user to know whether the person is a criminal, a patient with an infectious disease, or a matching person. In this situation, some problems could occur when the user physically contacts the person [7]. Let us assume the user waits for the package they ordered. Someone rings the bell of his house. The person who rings the bell says that they have a package. The user then opens the door to receive the package. Unfortunately, the person who rang the bell was a criminal deceiving as a delivery man. In the end, the user is put in danger. There are also health concerns due to infectious diseases such as COVID-19. When people communicate face to face to interact, infection may occur through contact. In this way, physical contact with the unidentified person cannot ensure the user's safety. Digital twin architecture can be a solution to resolve problems that may arise from interaction in the physical world. The characteristic of the digital twin is that activities in the physical world are also performed in the digital world because the digital twin is an online abstraction that reflects physical objects. Various physical objects are transformed into digital twins such as city [8], vehicle [9], robot [10], and so on. So, it can be said that interaction between digital twins equals interaction between physical objects. A method of representing people as digital twins is also used [11]. Instead of unsafe interactions in the physical world, users can interact without physical contact by using the digital twin. Consequently, the system relying on digital twin architecture can provide secure interaction to the user. In interaction between the users, mutual recognition is required to find the other person. Mutual recognition should provide functions that allow users to confirm the identity of the matching person they want to interact with and ensure that the matching person is within the interaction range to interact. In mutual recognition, proximity detection is used to check the interaction range. This paper proposes a novel smart interaction system using digital twin architecture to prevent physical interaction. The contributions of the paper are as follows:  
1. To convert physical interaction to digital interaction, the system uses digital twin architecture. Based on representing physical objects to digital twins, the system provides physically contactless interaction to the users. 2. Dynamic beaconing and proximity detection allow the users to find the matching user in the physical world.