## 2 Literature Review
[29] presented a cooperative air-borne system of unmanned aerial vehicles (UAVs) with high-level architecture to test their capabilities in disaster SAR operations and aerial surveillance. In addition, the author discusses certain crucial aspects in the UAV network system (sensing, coordination, and communication modules). The primary goal of the research was to support and assist firefighters in times of catastrophe, to give real-time video streaming, and to expand coverage capabilities to support the SAR mission. [PERSON] et al. [30] proposed forecasting, response, and evaluation of inland flooding systems. The proposed architecture enables multi-UAV dependability and security, as well as a UAV remote control system for usage with the smart cell and UAV infrastructure on-demand. [PERSON] et al. proposed a flying real-time network (FRTN) [31] for connecting the field's deployed static and mobile devices via flying units. Given the time constraints, the proposed network sends signals from the field to the incident commander and from the incident commander to the field. They also talk about digital communication in SAR missions using UAVs during the initial reaction to natural disasters. In [32], [PERSON] et al. proposed a search and rescue scheme by using a genetic algorithm for the UAV swarm. For optimizing the coverage area, UAVs are capable of a self-organizing mesh network. The communication strength of connected UAVs is constantly checked through RSSI, and UAVs adjust their position close to neighbor UAVs when the strength is lower than the threshold. The scheme is capable of maintaining group communication while moving together for searching. In [3], [PERSON] et al. proposed a technique focusing on achieving SAR missions in minimum time while rescuing the maximum disaster victims. The authors simulate the proposed work in a real scenario task distribution among UAV. They compared their proposed work with bio-inspired in the real scenario regarding the number of victims rescued with limited execution time for performance evaluation. [PERSON] et al. tackled the marine SAR mission problem employing cognitive and autonomous UAVs in their suggested work [33]. To enable the cognitive multi-UAVs and achieve their goal, they used cooperative game theory and a computer on board with the victim's last spotted location. In [34], extending the wireless coverage of the UAV unified framework of a UAV-assisted emergency network is reported based on the multi-hop ground device to device communication. In [35] serving the ground user, UAVs and the BS cooperatively introduced non-orthogonal multiple access. [PERSON] et al. presented a UAV-aided Wi-Fi direct network design in [36]. They suggested an effective approach for optimal UAV deployment and decreasing GO and client device distance. The planned effort focuses on keeping devices connected and enhancing network speed. The suggested system has a flaw in that it does not address UAV coordination and trajectory optimization to avoid collisions. In [37], [PERSON] et al. discussed improving wireless network coverage in disasters by replacing the traditional yet affected communication with a UAV network. They also present the optimized range while maximizing the user throughput. In [38], [PERSON] et al. devised the IoT UAV ecosystem for providing real-time data (multimedia) communication in affected-communication environments with the help of multi-UAVs. Additionally, this also offers on-demand usage of various available sensor mobile phone and UAV infrastructures. In [39], [PERSON] et al. proposed the usage of cache-enabled UAVs and provided the quality of experience to mobile word users using a cloud radio access network while minimizing and reducing the UAV transmitting power and transmission delay in the system. With the use of machine learning, centric-human information can be used to predict a user's content distribution and mobility. In [40], [PERSON] et al.