# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT
### UAV-IoT Frameworks  
Due to UAVs' high agility, they are now widely accepted as promising members of the IoT vision or even enablers of such a vision. They are capable of offering new value-added IoT services, while they can carry a variety of MTMC devices [23]. In more detail, according to the definition of IoT, \"things\" are expected to be able to be connected anywhere at any time providing any service. UAVs can fulfil this requirement, thanks to their autonomy, flexibility, and programmability. In this context, a number of UAV-enabled IoT frameworks supporting a variety of practical use cases have been proposed.  
Authors in Reference [1] introduced and demonstrated a UAV-based IoT platform for crowd surveillance. The respective platform adopts and applies face recognition techniques and performs efficient offload of video processing to a Mobile Edge Computing (MEC) node, considering the limited processing power and energy capacity of a UAV. The developed testbed collects video-surveillance data and performs face recognition to identify suspicious individuals by utilizing the Local Binary Pattern Histogram (LBPH) algorithm of the Open Source Computer Vision (OpenCV) library. The proposed platform considers central management of a fleet of UAVs through a system orchestrator.  
A communication framework for UAVs in urban IoT environments was proposed and evaluated in Reference [24]. It forms a multipath multihop infrastructure that is used to connect the UAVs to the ground control station. The conducted real-world experiments have shown that the introduced framework significantly enhances the control effectiveness and reliability against local congestion. It is noted that the specific work was inspired by the DARPA Hackest on Software Defined Radios.  
In Reference [25], a game-theory-based framework was introduced for allocating resources to UAVs, which enter the IoT ecosystems as platforms that assist terrestrial base stations. The access competition among the UAVs for bandwidth is modelled as a noncooperative evolutionary game. The evaluation of the two designed algorithms showed that Nash equilibrium can be quickly reached.  
An optimization framework for aerial sensing in the context of an IoT infrastructure was designed and presented in Reference [26]. The goal is to allow remote users to navigate in specific scenes of interest by using augmented-reality (AR)/virtual-reality (VR) devices over the captured data. The corresponding scenario is likened to virtual human teleportation. The conducted experiments effectively demonstrated the advantages of the proposed methods on visual sensing.  
Authors in Reference [27] conceived and presented a new MEC framework for IoT through an air-ground integration approach. Four use cases are presented to show how the proposed air-ground-integrated MEC framework supports high mobility, low latency, and high throughput for 5G applications. Through simulation-based and case-based evaluation, it was shown that the respective framework can support multiple IoT scenarios.  
A novel framework for deploying and efficiently moving UAVs to gather information from ground IoT devices is proposed in Reference [28]. This work focuses on the optimal deployment and mobility of UAVs, as well as the optimal clustering of IoT devices, toward minimizing transmission power while retaining reliability. In this manner, it was shown that IoT devices' energy consumption can be significantly reduced, whereas UAVs can serve as ground devices for longer.