## 2 Related Work  
The authors in [9] proposed a solution for UAV networks for data collection, especially for the ground networks or devices. In this research, the authors presented a novel system for UAVs' deployment and data collection from IoT networks. The authors also discussed that when the UAVs' optimal position, uplink power control, and device linkage are combined then the total transmits of the power of devices are minimized within their Signal-to-Interference Plus-Noise-Ratio (SINR) limitations. In addition, the authors looked into the most efficient method for data collection which is a time-varying method in IoT networks. To complete this feature, the authors used a time-updating strategy, especially for a UAV's location update based on the device activation process. Furthermore, the authors also discovered the best UAV trajectories for dynamically serving IoT devices while consuming the least amount of energy. These results indicated the UAVs' deployment reduced the transmit power as compared with the pre-deployed fixed UAVs' BS. The trade-off is also observed in the UAVs' nodes' movements during data transmission. In [10], the authors merged both the technology of UAVs with fog computing features to support IoT applications. The proposed architecture provides a virtual environment that is used for the development and deployment of IoT applications. In the proposed architecture, the UAV nodes worked as fog nodes and integrated between the IoT devices and the cloud. Then, the UAV nodes were moved to different locations and they collected the data for different IoT applications. The authors recommended a plug and play interface for automatic discovery. Every UAV-Fog provides broker services and is considered a local broker. The broker only knows about the current services that are owned by that node or services for IoT devices. It has been revealed from simulation experiments that the call made by the UAV-Fog to another node or the cloud or the local IoT device is much faster than when the call is made remotely. Authors in [11] designed a UAV solution for data collection from IoT networks. The proposed solution is optimizing 3D (three-dimensional) placement and nodes' mobility, device-UAV association, and uplink power regulation to allow reliable uplink communications for IoT devices with minimum transmit power. First, the ideal UAV positions and associations are recognized by using the IoT device's locations at each time instant. The feasible mobility patterns of UAVs' nodes are further analyzed by using dynamic services in the IoT networks for time-varying networks. The UAVs' nodes must update their positions after calculating the IoT devices' activation procedure. Furthermore, the best 3D trajectory for the UAVs is determined whenever the total energy is consumed for UAV mobility. Finally, the findings of the experiment revealed that, compared with deploying stationary aerial Base Stations (BSs), the suggested strategy reduces the overall transmit power of IoT devices by 45%. Furthermore, compared with the stationary scenario, the suggested technique can increase system dependability by up to 28%. The findings also highlighted an intrinsic trade among IoT and UAV devices and between several updates and their time. In [12], the authors proposed a technique DSC-UAV (dynamic speed control algorithm for UAVs) to enhance data gathering efficiency and to reduce access traffic for UAV-based BSs. The connection setup procedure is investigated and developed in detail using a cellular network to allow communication between sensor instruments and drones. Furthermore, the data collection performance is calculated and analyzed. The appropriate speed for varied sensor device densities is calculated and confirmed by using analytical models. To maintain high data collection efficiency, UAVs can dynamically modify their speed based on the density of sensor devices inside their coverage areas.