# Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
## 1 Introduction
These solutions are feasible with more cost-effective solutions, especially where the cellular networks suffer from shadowing, interference, coverage, and other service issues. Single-tier or multiple-tier drones in cellular networks have a significant impact on the desired communication between UAVs and users on the ground. Most of the researchers used similar power and similar altitude in the case of multiple UAVs, which is not realistic as user densities may vary, and the UAVs need to adjust their altitude based on these densities. Whenever the UAV density increases, the distance among nodes also decreases and causes interference. The coverage area must be maximum, where the user's probability is covered with a specified threshold and the optimal number of DBS to cover a larger area. Authors in [16] discussed the service scheduling issues in the Internet of drones network. Due to the high mobility of nodes and limited resources in terms of communication range, bandwidth, and limited zone service providers, service scheduling becomes a critical issue, especially for downloading and uploading data. Authors in [17] discussed the security issues in the Internet of drones network and proposed a lightweight and privacy-preserving mutual authentication scheme for session establishment. The existing studies have suffered from computational complexities in deploying security. In another study [18], authors discussed the lightweight digital solution for drones, tackling the man-in-the-middle attack. This protocol uses a digital signature based on command messages by using a chaotic system. Authors in [19] discussed the flooding attacks and presented a solution to handle this attack in drone networks. The lightweight solution is presented where each drone counts the number of packets which are sent within a predefined time interval. Different solutions have been designed to address these issues in [20]; numerical algorithms are used as an alternative. Some other studies divided the area based on user distribution data, calculating distance, allocation of DBS, cluster formation, establishing the DBS at the optimal location, and coverage area specification in 2D and 3D planes [21; 22]. These studies obtain the signals from source nodes because of effective diversions and reflections. In another study [23], the authors considered the probability of having LoS connections between receiver and transmitter. There is a need to design a solution to find the best position of the UAVs-BS in a microcell environment by using better techniques and providing optimal coverage and increased capacity throughout the service area. This paper aims to improve the cellular network services by optimally using the UAVs and DBS placement. To achieve the aim of this research, the following objectives are in line. * To design a solution for optimal cellular coverage by establishing optimal numbers of BSs in the cellular network area. * To design a controlling solution to manage and reallocate the nodes as per requirements. * To evaluate the solution for better resource allocations in the cellular network. The rest of the paper has the following sections. Section 2 reviews the literature in the field of routing. Section 3 discusses the design and development phases of the proposed solutions. Section 4 illustrates the evaluation steps and results, whereas the last section concludes the paper with future work.