# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 2 Related Works  
There have been trials in establishing communication in a post-disaster region. AT&T is an American telecommunication company that provides AT&T Network Disaster Recovery (NDR) [12] units that restore their network in a disaster area by bringing in portable cellphone towers on trucks called _Cell on Wheels (COW)_[13]. The NDR team was a great aid during the California wildfires (2017), Hurricane Irene (2011), Hurricane Irma (2017), and Hurricane Maria (2017) [14], although transporting the COW is difficult when roads are damaged due to disaster impact. Furthermore, COW is unsuitable for disasters such as floods, landslides etc. Another method is the use of satellite phones or two-way radios as communication devices [15]; however, these devices are not affordable for the general public, and government regulations may prevent the general public from using these devices. In addition, it is not easy to track their locations. Using drones for communication is a growing research field due to their on-demand deployment and high flexibility [16]. [PERSON] et al. [17] proposed a formation control algorithm for a drone-based network in a planar region. The algorithm increases the network throughput by improving the network infrastructure. [PERSON] et al. [18] proposed deployment algorithms for a drone network that maximize the leftover energy of all drones. The algorithm takes account of any no-fly-zone constraints in the operational region. [PERSON] et al. [19] proposed a multi-drone coverage deployment model that focuses on efficient network coverage and energy conservation. [PERSON] et al. [20] provided a framework for a network that uses drones as a relay node to achieve good quality of service, better spectral efficiency, and improved diversity. [PERSON] et al. [21] proposed a novel framework for a drone-enabled antenna array system that provides better service to users. It considers each drone as an element in the antenna array system and optimizes the drone positions in the array to maximize the antenna gain and minimize the service time for the ground users. [PERSON] et al. [22] developed an algorithm for deploying a heterogeneous set of drones that provide maximum wireless coverage for ground users in a planar area. There have been attempts to improve the performance and efficiency of drone-based networks. [PERSON] et al. [23] derived the energy consumption of drones and determined the influence of various parameters that affect the energy of the drones. [PERSON] and [PERSON] [24] provided experimental results on the relationship between various factors of a quadcopter. [PERSON] et al. [25] developed the designs of low-altitude drone networks that provide Long-Term Evolution (LTE) connectivity. [PERSON] et al. [26] proposed a drone-based network that uses Wi-Fi as an emergency communication network for disaster management. [PERSON] et al. [27] proposed a deployment algorithm for drone-based networks that improves network throughput and coverage in the disaster area. [PERSON] and [PERSON] [28] proposed a method to improve drone network throughput by controlling the drones' height with respect to the number of users. [PERSON] et al. [29] proposed an algorithm for drone deployment that minimizes the number of drones required for the network. [PERSON] et al. [30] introduced a spiral algorithm that aims to minimize the number of base stations mounted on grounded vehicles. However, in situations where such vehicle access is limited, the technology is not applicable at all. A research paper by [PERSON] et al. [31] discussed the positioning of base stations in unmanned aerial vehicles. They developed an algorithm to find the optimal positions of the base stations through association of the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) evolutionary strategies. However, GA is computationally complex and PSO tends to fall into local optima. A paper by [PERSON] et al.