## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.3.5 Tools and Platforms
Secure communication is key in AIoT systems especially inXR environments where continuous communication of data between devices takes place and where personally identifiable information is constantly extracted from devices such as HMDs. At the same time, many ethical considerations also arise, especially in cases of monitoring in scenarios such as smart cities. Ensuring data privacy and security is essential to gain user trust and facilitate the widespread adoption of these technologies. Ongoing advancements should seriously take this into account and propose modern cybersecurity measures while also maintaining transparency and ensuring data are collected with the users' consent. Regarding the training of ML models, the issue of ethical use of AI also arises, particularly with respect to bias in training datasets. This bias often stems from the data collection processes, where incomplete or uneven data for certain groups can lead to their under-representation or misrepresentation. This issue became more prominent with the increasing use of GenAI for creating synthetic data [94]. To avoid that, it is important to ensure that AI models are trained on diverse and representative (bias-free) datasets that accurately reflect the current state in reality. This can be achieved with techniques such as oversampling of underrepresented classes, undersampling of overrepresented ones, the use of synthetic data as well as the pre-processing, but this can be a time-consuming task [94; 95; 96]. Apart from the representation and generative bias, algorithmic bias is one of the most common issue that occurs when AI algorithms are designed or implemented with emphasis on specific features during training, thus resulting in unrealistic/unfair conclusions. The move to explainable AI (XAI) will assist in identifying bias sources with techniques like feature importance analysis, and promote fairness by having transparent decision-making processes. Post-processing of AI-generated content as well as adversarial training are some of the methods that can identify and mitigate bias in fair AI systems [94]. Along with the process of data collection, there is a significant need for data-availability. Setting up real-life experiments and collecting real data is challenging,while large-scale implementations are scarce. There are limited datasets available for training in very specific applications, and privacy concerns, as well as limited trust and lack of knowledge of new technologies from users, further hinder the creation of such datasets by smaller research teams. Due to that, it is hard to train highly accurate AI models as they are strongly depend on data, while the quality and variations of the collected data also plays a major role in the accuracy. It is important to highlight that data collection and limited real-life testing are among the most significant limitations. Low-accuracy readings and generalizability of the models were a common challenge that some experiments faced and it could partly relate to the aforementioned factors. As indicated by quantitative research and the overview of eligible studies, there is a notable lack of real-life testing for many systems, which should be addressed more thoroughly in future work. Interoperability is another significant challenge due to the lack of standardized communication protocols and data formats. Integrating new technologies with existing legacy systems is often complex and time-consuming. Developing universal standards and adopting open-source solutions can help address these interoperability issues. Semantics, which were not usually employed in the systems reviewed, can assist in this matter and overcome problems like the heterogeneity of sensor data. In addition to all of the above, integrating VR, AI, and IoT systems often requires substantial computational resources. The continuous increase of data stored along with data considered by LLMs can lead to an unmanageable computational overhead, particularly on lower-end devices.