## 4 Overview of Included Studies
The data are rendered on the HMD in real-time visual feedback allowing users to interactively guide the drone to fill in missing areas. Simulation experiments took place in a 3D outdoor environment where a virtual drone performed 3D reconstruction tasks. Participants were asked to reconstruct virtual objects with and without visual feedback. Results showed that the VR-aided approach significantly improved model completeness and accuracy while demonstrating that the VIO-based tracking outperformed traditional methods. To further showcase the feasibility of the system the authors created a real-world UAV prototype forming a unified system with the HMD used in the simulation phase. In this system all three technologies assist in improving 3D reconstruction significantly. Challenges identified in the study included the need for precise UAV control to ensure effective scanning as well as latency issues in data transmission (approximately 500 ms) primarily stemming from the communication between the ground station and the UAV, and the response time of the mechanical system. The authors highlighted that while their system achieved real-time scanning, the computational load on the ground station limited the UAV's autonomy, suggesting potential areas for future improvement and a transition to AIoT with the drone carrying out the entirety of the computational tasks. [PERSON] et al. in [82] presented an Internet-of-Vehicles (IoV) traffic condition system developed using AIoT for improving road safety and traffic management through real-time multimedia data transmission. Their system utilized Faster Region-based Convolutional Neural Networks (R-CNN) for the detection of objects such us road obstacles or construction works by analyzing video feeds from vehicles' dashboard cameras. In addition to that the model analyzes GPS information from the cameras to realize vehicle speed and level of traffic congestion. The system operates over a 6G Network, which facilitates high-speed data transmission and QoS while communication can take place between vehicles' cameras as well as RSUs. When new information about road conditions is detected, for example a crash, the system uses push notification to notify social groups instantly and inform the proper authorities. A proof-of-concept prototype using hardware tools like Raspberry Pi, GPS modules, and cameras was showcased and tested regarding the detection and notification process. One of the most important parts of this work is the incorporation of federated learning which allows dashboard cameras to update their recognition models locally without sharing raw data thus ensuring better privacy and security. In addition to that, the system uses bilinear pairing techniques to establish secure communication. Each vehicle's 6G SIM is encrypted with a public key from the base station and a session key is generated for data transmission. This means that only authorized entities can decrypt the messages making communications more secure against attacks. Also the system uses anonymous IDs that change with each message so hackers can't track or forge identities. The study shows AIoT can be adopted in IoV applications especially in smart cities without the need of extensive infrastructure changes. However the system relies heavily on the widespread adoption of 6G networking which is still in developmental stage and not globally available. Interoperability issues as well as standardization concerns also arise. The accuracy of the model in large scale implementations could vary depending on several factors and real-life road conditions which may have not been represented adequately in the tested dataset. Moreover the cost of integration for such system in a real world scenario is not discussed and the legal and ethical considerations for the use of video feed for model training need to furtherly be discussed too. Continuing with the set of articles retrieved from IEEE Xplore, authors of [83] focused on the application of AI, VR, and IoT to combat gender-based violence.