# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 2 Related Work
In this proposed model, the demand for wireless sensor networks increased rapidly for data collection and provision of more services and the demand for infrastructure-based cellular networks for a more efficient and cost-effective solution. These models used density and cost functions. Due to this model, achieving better capacity, throughput, reliability, and longtime connectivity may be possible. Based on multiple UAVs, deployment models are presented and also provide an algorithm for area mapping (for UAVs). This study adopted a neural-based cost function where the UAV is matched with a particular geographical area. The multiple UAV deployment not only solves the range coverage issue but also offers load balancing and traffic offload solutions. The authors designed a system model by using the macro and small cell user equipment. This model is demand-based and uses density and cost function and then computes the area. In [36], the authors proposed a drone-as-a-gateway (DaaG) solution by using the mobile gateway and sink system or delay-tolerant system. This solution is based on a drone as a gateway where the actuator is visited by drones, reducing the service delay. The authors also discussed the solution of optimal height for a drone which can be optimized by using multiple algorithms to increase spectral efficiency. This study used three algorithms, Equal Bandwidth Division, Nearest User First, and Least Buffer First, for the dynamic repositioning of the drone BS. In equal bandwidth division, all the active users equally share the resources. In the nearest user first, the nearest user to the BS has the highest level of spectral efficiency. The last buffer drone finds the user with the least remaining data to send and allocates all the resources to that user. The usage of mobile sensing devices with their functionalities is increasing rapidly. However, managing these types of devices in an integrated manner and handling the heterogeneous networks is a huge challenge and problem, especially in wireless sensor and actuator networks. Sensors and actuators are tightly coupled and integrated and not suitable for heterogeneous types of networks. The basic problem in these networks is a combination of heterogeneous networks. In the recent past, there have been many aspects of a drone BS. This solution, such as a dynamic BS, has already been considered and studied by many researchers to improve UAV performance. The problem in dynamic BS allocation is a fixed area which has a minimum transmission range. In [37], the authors presented a self-healing neural model network and the concept of matrix colouring (in the form of adjacent and isolated cells) for achieving the optimum utilization of UAVs. This study discussed the matrix approach and proper method for the deployment of UAVs as a low energy consumption mechanism. The proposed solution also considered the memory and cost of UAV deployment. The two network techniques are used in this model, including backhaul (unidirectional) and fronthaul (umbrella form). The network model consists of microcells and several BSs, which are capable of maximizing the user equipment (UEs) and connections. The area is subdivided based on two algorithms: (1) for isolated cell identification and (2) for UAV deployment. The proposed approach provides high throughput coverage, optimizes the utilization of UAVs, and also solves the placement problem in a 5G network with low energy consumption and less memory utilization. In [38], a higher level of an abstraction energy-aware communication protocol based on power management is presented. Power management techniques also included are called power harvesting. The key challenge is the deployment of sustainable batteries to achieve the optimum utilization of energy. The environmental harvesting technique for energy harvesting is trustable, but solar energy supply is a highly time-intensive source. Harvesting components have various protocols for charging characteristics; these characteristics must be matched with each other.