## 4 Overview of Included Studies
's Vocal Museum system, which enhances museum visits through personalized voice-guided experiences [73]. The system integrates IoT and AI to provide personalized, context-aware experiences without overwhelming visitors with other digital distractions. It consists of three main components, a localization system based on Ultra-WideBand (UWB) antennas for position tracking, a mobile app for user interaction, and a server managing content delivery. Visitors pair their given UWB tags with the app and interact via voice or text chat, receiving context-aware information about nearby artworks. When a visitor enters an artwork zone, the system proactively engages the visitor with welcome messages and encourages interaction through conversational AI. Inside the museum there are multiple beacons that track the UWB tag location and based on that trigger certain interactions. The system was tested in a controlled lab environment with three famous artworks, demonstrating technical feasibility, but several limitations and open challenges were acknowledged. Their system could be improved with multilingual support and camera-based artwork recognition. Additionally, it lacks evidence regarding its scalability, and there is an absence of testing of the voice recognition in noisy museum environments or with non-technical users. Additionally, the system's usability requires validation through established metrics such as the System Usability Scale (SUS). A potential advancement could be the integration of AR or even VR for features like visualizations, gesture-based interactions, remote visits, and virtual artwork restoration. On the other hand, the authors stated that even though XR has many advantages, it can take the visitors' focus off of the real artworks, which did not align with their intentions. Despite these limitations, the Vocal Museum system represents a promising approach to enhancing museum experiences through voice interaction while maintaining focus on the physical artworks. The Sensor2 Scene framework presented in [74], highlights the potential of LLMs to interpret IoT sensor data and integrate it into AR environments for immersive, context-aware visualizations, with suggested applications in creative practices. The AI agent comprises Sensor Data Interpreter and Scene Producer modules, transforming IoT sensor readingsand spatial information into structured metadata, which is used to generate 3D visual elements via advanced text-to-3D models that align with real-world settings. Features like a user feedback loop and environmental scanning enhance adaptability by refining scene descriptions based on real-time sensor updates. The authors benchmarked Sensor2 Scene using two public and a self-collected dataset, assessing the system's performance in scene description and 3D model generation quality. Evaluation revealed strengths in scene fidelity and utilization, but challenges in integration and coherence. A user feedback mechanism was introduced to improve these aspects iteratively. In terms of 3D model generation, the system demonstrated the ability to produce contextually relevant AR scenes, though issues such as less detailed textures and occasional object collisions were reported. Additionally, heavy reliance on pre-defined prompts for LLMs may limit the system's flexibility. User feedback praised the dynamic adaptation of virtual elements to real-time sensor updates but emphasized the need for more refined interactions and texture quality. Proposed future research directions include the use of text-to-video technologies and 3D Gaussian splatting. There are also several smart city systems that enhance quality of life in urban environments that take advantage of the convergence of IoT, AI and VR. [PERSON] et al. in their 2023 research [75] developed an open-source platform to enhance drone fleet management. They distinguish AI drones, capable of running real-time DL algorithms, from AloT drones, which extend this functionality by connecting to the Internet and enabling for object detection and autonomous navigation.