## 1 Introduction
For example, when moving in a smart city environment, AR is an essential interaction technology for unobtrusively bringing context-sensitive information from IoT devices embedded in the surroundings. A practical use case is an automatic air pollution monitor that uses AR to insert visual indicators on the real-world view of the user's AR device (e.g., AR glasses) to show the air quality in the context of the user, which is measured by nearby IoT sensors in real-time [14; 15]. In the scenario, when the system can automatically provide context-sensitive notifications to the user, they do not explicitly need to search for the pollution information, as it is delivered to them in a contextually relevant manner. The combination of AR and IoT technologies makes it possible to bridge the real world, virtual world (i.e., digital content), and ambient environment (i.e., information from IoT sensors), thereby increasing the richness of information and opening possibilities for context-aware application development. For example, visualized data of an ambient environment can serve as digital content that is superimposed on the real world [10; 12; 13]. However, the use and development of multimodal interaction in applications that combine AR and IoT has remained a little researched area. Thus, we have identified several motivations for exploring the combination of AR and IoT from the perspective of MI  
Figure 1: General architecture of multimodal interaction that uses diverse modalities for the input and output channel. systems. Firstly, the diversity in the use of AR and the affordance of IoT to provide rich data on agents' contexts form a fertile ground for developing new interaction modalities [16] and, consequently, new MI systems. Secondly, previous research has shown that a complementary combination of AR and IoT enables smart and interactive environments where the agent's interaction with IoT devices can become more intuitive [17], which is essential when designing interaction with complex artifacts and unfamiliar devices [18]. Thirdly, AR and IoT enable otherwise unperceivable information (e.g., readings from sensors, ratings of a service from other users) to be expressed through perceivable modalities (e.g., visualizations, sounds, vibrations) [16]. Finally, based on our analysis of previous literature reviews on MI systems [5; 6; 19; 20], there is a lack of knowledge in the field on the use of AR and IoT in MI systems, as well as on the development of such systems. Because the process of integrating different modalities is complex, it is challenging for system developers to ensure high learnability (i.e., high usability) while providing a sufficiently positive experience (i.e., Quality of Experience [QoE]). In order to achieve this, it is necessary to perform a systematic review of previous MI system development efforts, which can help shed light on the optimal design of these systems. There have been several attempts [5; 6; 19; 20] to systematically review general research on MI systems; however, only several studies have reviewed the use of IoT and AR in MI systems [21; 22]. As the contributions of this study, we analyzed the various input/output modalities and modality combinations used in MI systems. Consequently, we identified modalities and modality combinations that had not yet been explored by researchers. We also reviewed 23 systems that used IoT and AR to identify patterns of MI system architecture that developers can refer to in designing MI systems. Additionally, we analyzed the use of AR and IoT in MI systems for identifying typical, rare, and unexplored ways of using these technologies. We also attempted to identify open research challenges in the field that may attract the attention of researchers in the field of human-computer interaction (HCI) as future research avenues. The results of this study complement the previous work on MI systems from the perspectives of AR and IoT, thereby contributing to the growing knowledge base of the industrial revolution 4.0 technologies.