## 5 Discussion
### RQ 5. Which Open Research Challenges Remain in MI Systems with IoT and AR? We found that 13 of the 23 reviewed studies explicitly mentioned research challenges regarding various topics. We categorized the research challenges in Table 9, based on five keywords: (1) technology, (2) standardization, (3) scalability, (4) multi-agent, and (5) multidisciplinary. 1. Solutions to the identified research challenges that were related to hardware and technical limitations generally involve the development of technology and hardware. For example, the limited field of view in HoloLens, as noted by [PERSON] et al. [45], has been solved in HoloLens 2, which has a larger field of view than the original HoloLens (i.e., 30\({}^{\circ}\) in HoloLens versus 52\({}^{\circ}\) in HoloLens 2). As another example, instability in vision-based tracking of object features caused by insufficient features can be corrected by a tracking recovery process [48]. These types of research challenges rely on the development of technology unless alternative approaches are developed. The standardization of MI systems is key in achieving high scalability [11; 50]. When MI system developers begin to use unified forms of both software components (e.g., data structures, data communication protocols, and UI structures) and hardware components, MI systems will gain the ability to handle different types of IoT devices, interaction devices, and interaction modalities while being able to communicate with other MI systems. Therefore, standardized MI system development can reduce the burden on MI system developers, who must otherwise consider the diversity of IoT devices, interaction devices, and interaction modalities when building an MI system for use in a real scenario outside the testbed. 3. Scalability is a research challenge that must be solved to establish a large-scale environment that can manage multiple types of IoT devices and agents that are easily and automatically registered. The absence of standardized data structures for IoT devices is the reason for the difficulty of this research challenge. Moreover, the increase in the number of IoT devices in MI systems may lead to significant computational costs on central servers. These challenges can occur when adding not only IoT devices, but also input/output modalities and interaction devices to MI systems. Therefore, considering the technology added to standardized MI systems is essential in achieving scalability. 4. A multi-agent scenario is another critical case that MI system developers must take into account to achieve not only high scalability, but also high QoE. In the multi-agent scenario, the MI system must manage all interaction devices while maintaining quality of service, such as timely synchronization of data to all agents [44]. Moreover, the diversity of interaction devices and UIs can also enhance the QoE because agents have different preferences. The development of adaptive UIs is a challenge that can achieve a personalized experience for agents by providing customized interaction modalities, context-aware feedback, and even offering a specific interaction device, depending on the agent's preferences. Our findings suggested that 12 of the 23 reviewed studies conducted a user evaluation regarding usability and/or QoE. However, none of the reviewed studies evaluated their MI system in a multi-agent scenario. This indicates that an in-depth study of the on QoE of an MI system in the context of multiple agents is essential. 5. The research challenges in the multidisciplinary category demonstrate the importance of collaboration with different disciplines. This importance can be observed, even in the development of MI systems utilizing AR without IoT [63]. The development of an MI system requires understanding the relevant technology and content to achieve a sufficient degree of QoE. For example, the use of emojs by [PERSON] et al. [33] to represent the state of IoT devices require psychological knowledge to avoid unintended results on various agents with different cultural backgrounds.