## 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  
The convergence of VR, AI, and IoT in various domains is supported by a suite of tools and platforms. Unity [87] and Unreal Engine [88] are prominent for VR development, offering robust environments for creating high-fidelity, interactive virtual experiences. These platforms provide extensive libraries and support for various VR hardware, streamlining the development process. Regarding VR headsets, the most common one seems to be the Quest family while for AR most of the systems used Holdens glasses. Python, paired with frameworks such as TensorFlow [89] and Keras [90], is very widely used for implementing AI models, again due to its simplicity and the extensive range of machine learning libraries available. These tools facilitate the development of sophisticated AI algorithms that can analyze and adapt to data in real-time. For IoT prototyping, Arduino and Raspberry Pi are popular choices, providing versatile and cost-effective platforms for building and testing IoT devices. These platforms support a range of sensors and actuators, enabling the integration of physical devices with digital systems. Smartphones are also used as a sensor power-house while offering efficient computational power in certain scenarios. RQ4: What Are the Current Limitations and Challenges in Combining VR, AI, and IoT, Particularly Regarding Data Collection, Interoperability, and User Experience? Despite significant advancements, integrating VR, AI, and IoT presents numerous challenges and limitations which are revealed through the literature. These limitations hinder the seamless operation of integrated systems and require continuous research to address them effectively, stemming for future work. Data collection in AIoT systems heavily relies on the accuracy and reliability of IoT sensors. However, environmental factors, such as lighting conditions, temperature, and physical obstructions, can compromise sensor performance, leading to inconsistent or incomplete data. The high cost of some sensors hinders scalability and constitutes a significant limitation as the prohibitive cost does not make them attractive options for use in large-scale real-life scenarios. Privacy and security of collected data is a critical issue. AIoT solutions must balance the need for accurate data collection with users' privacy, which often necessitates secure data handling and compliance with data protection regulations. Privacy concerns are prevalent in domains such as healthcare and smart cities, where sensitive user data are collected in real time. It is of great importance to address these concerns by implementing non-invasive, privacy-preserving IoT solutions. As aforementioned, after data is collected, ML models require these large datasets in order to perform sufficiently, however this is tied to an increasing risk of privacy violations. Specifically in metaverse environments and XR systems, privacy threats arise from the single point of failure architectures, the extensive use of biometric sensors, eye-tracking data, and behavioral analytics, which can expose personally identifiable information. To mitigate these risks, integrating privacy-enhancing technologies (PETs) such as homomorphic encryption (HE), blockchain technology, differential privacy (DP), and federated learning (FL) is essential and is a recommended way to combating those issues [91; 92]. For example, HE allows systems to act on encrypted data without exposing raw information thus ensuring that sensitive data remains safe. DP introduces noise into the system to improve data anonymization. Finally, with hybrid techniques and while combining the above FL a safer and robust decentralized architecture can be created. FL enables edge devices to locally train models using their own datasets, transmitting only aggregated model updates (e.g., weights and biases) to central servers instead of raw data, thus making systems less susceptible to poisoning attacks and unauthorized data access [93].