## 2 Literature Review
They created ground station control software to help with the relay system. They ran the test in two scenarios: inside and outdoor. Their effort was primarily focused on delivering communication relays via a single drone (node). [PERSON] et al. [19] discussed the work based on the algorithm that helped relay the network base station and the ground terminals. These works help in optimizing the relay system performance. [PERSON] et al. [20] proposed the novel dynamic approach for maintaining the communication for vehicles in long-range relay communication infrastructure. The artificial potential was their base for path planning. [PERSON] et al. [21] analyzed the use of UAVs for providing relay services for cellular networks. The throughput and ping of the upload and download were tested in both the rural and urban environments. [PERSON] et al. [22] developed two techniques for distinct scenarios in their work: centralized deployment and dispersed motion. The information of the UE on the ground is already known in a centralized algorithm, which aids in the deployment of the minimum number of UAVs in that area, whereas in a distributed algorithm, the UAVs take flight toward the area on demand, control their motion, and also find the UE on run time, which aids in the on-demand coverage problem. However, in both instances, the algorithm is designed to keep the UAV networks connected. The two network techniques presented by [PERSON] et al. [23] are for maximizing communication and network efficiency. They employ the greedy method to locate the hovering spot in the area. They separated the global optimization problem into sub problems, and the solution to each subgroup problem was derived using the range determination approach. Multiple UAVs may collaborate to build a UAV network (UAVNet) to execute tasks more efficiently and economically than a single UAV. [PERSON] et al. [24] proposed a method to gather the information of the network from the air about delay-sensitive data. In the context of UAVs' usage, some recent studies have designed optimal path planning using a UAVs. [PERSON] et al. [25] aimed to solve a traveling salesman problem for environmental monitoring based on a genetic algorithm that calculated fitness functions to maximize coverage. [PERSON] et al. [26] conducted substantial research on the problem of path planning and space exploration. Although most of them were designed to reduce duplicate coverage across agents, they cannot be immediately transferred to the UAV context because networking capability and lightweight computation are not taken into account. Through their ad hoc deployment, UAVs can be employed as suitable communication resources to quickly rejoin separated networks. UAVs can help to solve the network hole problem by acting as temporary relay nodes [20; 21]. [PERSON] et al. [27] proposed using an unmanned surface vehicle (USV) and an UAV to provide rescue services in a flooded area. The USV transports the UAVs to the spot, obtains information, and assists the survivors. USV is in charge of gathering environmental information using a laser and a camera, and with such capabilities, USV can generate a map and provide rescue materials to catastrophe victims. The combination of USV with UAVs allows the USV to obtain general area information for path planning while also being capable of designing a local path using its local information. In [28], focused on network and communication technologies that can aid drone disaster management and early warning systems, SAR operations, data collection, and emergency communication and network provision. Their report also investigated network technologies that play a crucial role in drone deployment during disaster management. The author also discusses the design and difficulties (antenna structure, drone cooperation, path planning, channel modelling disaster areas, and energy efficiency). [PERSON] et al.