## 4 Overview of Included Studies
The authors emphasize the need for robust Internet connectivity, identifying 4G/5G as optimal for achieving sufficient transmission distance and bandwidth. However, existing commercial solutions often rely on proprietary protocols, limiting customizability. This gap underscores the necessity for an open-source drone cloud to support advanced algorithmic research and development. To address these challenges \"AI Wings\" was developed, providing a versatile, cost-effective system that transforms DIY unmanned aerial vehicles (UAVs) into AloT drones. It is an open-source platform [76] leveraging Android-based devices as embedded modules to avoid the high costs and complexity associated with alternatives such as NVIDIA Jetson. The system incorporates an ArduPilot-compatible MCU, a drone cloud server for secure management, and elliptic-curve cryptography for enhanced security. VR is integrated through Microsoft AirSim to test and train AI models in simulations before real-world deployment. Experiments were conducted under different conditions, with the drones operating at low altitudes and the quantized MobileNet SSD model was tested using TensorFlow Lite on various Android platforms to detect humans. The model achieved real-time performance, with results such as 33 FPS on a Snapdragon 855 and 16 FPS on a Snapdragon 665. Three different types of AloT drones where constructed and tested to further validate the platform. The drones autonomously navigate, dodge obstacles, and perform tasks directed by the cloud server. Their real-world practicality was demonstrated through an emergency delivery service where using AI Wings automated external defibrillators (AEDs) were quickly delivered to people in emergency situations. This innovative system represents a significant step forward in making AloT-enabled drones both practical and scalable in smart city environments while also embracing an open approach. Continuing in the phasm of smart city applications and drones authors of [77] specifically focus on smart public safety surveillance (SPSS) framework using UAVs and employing both AI, VR and IoT. The study introduces the concept of a \"microverse\", which serves as a task-oriented, edge-scale DT for localized, real-time decision-making as a practical alternative to fully immersive metaverses. The Microverse aims to integrate technologies such as DTs, blockchain, and lightweight network slicing to overcome challenges such as interoperability, and real-time data handling in complex IoT environments. The hierarchical architecture of the microverse consists of four interconnected layers, the PhysicalLayer (IoT devices and sensors), Slicing Layer (managing communication and Quality of Service (QoS)), Microverse Layer (creating virtual realms with semantic models and intelligent services), and Application Layer (user-facing VR and AR experiences). The SPSS Microverse prototype was tested through real small scale testing and simulations. It was developed with Unreal Engine 5 (UE5) and utilized UAVs and edge computing for real-time surveillance with YOLOv8 being used for object detection and tracking. Feedback data from drones, controlled via a custom Android app, is being processed through the microverse engine. The virtual environment allowed for immersive visualization and interaction while monitoring through a VR-headset was also possible. The system seemed to effectively mirror real-world conditions in a digital space by synchronizing physical surveillance drones with virtual environments, enabling tasks such as object detection and trespassing alerts in real-time. The low latency and high throughput recorded through the tests show feasibility for a smart city application but there are still enhancements to be done and the framework is not yet implemented in real testing as a whole, given that the slicing layer and certain security measures weren't integrated during testing. Additionally ethical considerations about the surveillance drones weren't discussed thoroughly and the accuracy of their detections wasn't disclosed either.