# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 1 Introduction  
UAVs are gaining traction as a useful tool in many different domains, including search and rescue [1, 2], surveillance [3], cargo transport [4], surveying and mapping [5, 6], disaster relief [7], and Internet of Things (IoTs) [8, 9]. This paper specifically focuses on the search and rescue application domain, where the challenge is to establish an on-demand UAV-based wireless network in remote areas or when existing communication infrastructure fails to operate due to natural calamity or stress on the existing network. In network deployment, a set of UAVs is deployed within a given AOI to provide wireless connectivity to ground users for an extended period. For known user distributions, centralized network deployment algorithms are used to generate wireless mesh networks. However, deploying networks to serve users with unknown distributions is challenging. While many techniques for network deployment in unknown environments have been proposed in the literature, generalizability and real-time adaptation to uniformly and non-uniformly distributed users remain challenges. This paper aims to present an approach that is generalizable to different types of user distributions and adaptable to users' requirements. The deployment of a UAVs-based ad hoc network in unknown environments is usually divided into two sub-challenges. The first challenge is to find the users' distribution. This is a relatively easier task and several geometrical-based algorithms [10, 11] have been proposed in the literature to deploy static networks. The second challenge is to adaptively re-deploy UAVs in real-time to connect users with the command center [12], which is an NP-hard problem. Compared to static networks, less work exists on the part of the re-configuration of networks that adapts to users' requirements in real-time. The reconfiguration of networksis difficult due to the limited communication range between UAVs, limited battery life of UAVs, and various environmental conditions. This problem needs more attention and has many real-world applications. This paper presents a two-phase approach to network deployment, where potential fields (PFs) govern the movement of UAVs. In the first phase, a set of potential field parameters is derived for static network deployment. For the second phase, a genetic optimization approach is presented, genetic adaptive network deployment (GANet), that optimally deploys UAVs to create an ad hoc wireless network. The problem has been formulated as an optimization problem with the aim of maximizing the sum of bandwidth coverage provided to users and the longevity of the deployed network. To provide more bandwidth coverage, UAVs move under the influence of a set of potential fields toward users in the AOI, and to increase the longevity of the deployed network, UAVs position themselves to share the bandwidth demanded by users while maintaining connectivity to the command center. In the simulation, each UAV's movement is controlled by a set of potential fields, where each potential field has two parameters [13; 14], and these parameters can be tuned to achieve the desired behavior from UAVs. However, searching for parameters that optimally control the movement of UAVs to create optimal wireless networks is difficult for two reasons: (1) due to the non-linearity of potential fields and (2) because of the large search space. In the literature, it has been shown that genetic algorithms (GAs) work well with non-linear problems and problems with a large search space. Thus, a genetic algorithm has been used to evolve solutions. The potential field parameters are encoded in a real-valued chromosome and the GA searches through the space of potential field parameters. Note that other search algorithms can also be applied for searching through this space, and, in the results section, the GAs performance is compared with two other search algorithms.