# Genetic Algorithms Optimized Adaptive Wireless Network Deployment
## 2 Related Work  
Many challenges arise when deploying a wireless mesh network in unknown environments. Optimal UAV placement, fast deployment, maintaining a mesh network connected to a command center, minimizing the number of deployed UAVs, maximizing the lifetime of deployed networks, routing [16], and channel allocation all present significant challenges [12; 17]. In the literature, several techniques have been introduced for positioning UAVs based on Delaunay triangulation (DT) [15], the circle packing theorem (CPT) [10], and Voronoi diagrams (VDs) [11] to create static networks. A network deployed using DT has no coverage gap while having minimum overlap between UAV's sensing areas. CPT computes the locations of UAVs while ensuring no overlap and a minimum coverage gap. A VD divides the entire AOI into different segments and places a UAV in each segment to provide bandwidth coverage to users. Such static network deployment is primarily used for area coverage. [PERSON] [18] deployed a heterogeneous sensor network using circle packing by filling the given AOI with circles of different radii corresponding to different UAV types. [PERSON] [19] proposed a placement optimization technique that first deploys a network using CPT and then adjusts the altitude of deployed UAVs to cover the entire AOI with fewer UAVs while providing wireless coverage to ground terminals. These are computational geometry model-based techniques used to compute the position of UAVs and they do not adapt to users' locations or bandwidth requirements. Our proposed GANet, on the other hand, adapts to both while maximizing network longevity. Researchers have presented modifications to make static networks more adaptive to users' requirements. [PERSON] [15] introduced the state-of-the-art adaptive triangular network deployment algorithm, ATRI, by taking inspiration from Delaunay triangulation. Using one-hop neighbor's state information, ATRI generates a mesh network by adjusting the distance between neighboring UAVs in order to better share bandwidth within a given AOI. Unlike our approach, ATRI, however, does not work well when users are distributed non-uniformly or clustered in groups. In [20], the authors initially deployed UAVs using CPT and presented a mathematical model to adjust the altitude of deployed UAVs for near-optimal coverage. Increasing the altitude of UAVs increases the coverage range but decreases the signal strength, resulting in a poorer coverage quality. In this work, UAVs were deployed at a fixed altitude and only moved in two-dimensional space. [PERSON] [21] proposed a Voronoi polygon-based adaptive network deployment algorithm to deploy heterogeneous mobile sensors. The algorithm computes different polygons for different sensors and moves a sensor only when the sensor does not detect any users on the ground. [PERSON] [22] presented a dynamic algorithm to serve a sub-region within the AOI that requires more bandwidth. Both of these papers assume a uniform users' distribution whereas this paper deals with non-uniform users' distribution as well. Section 5 of this paper describes how to use potential fields to deploy networks in the first phase to mimic DT and then re-deploy UAVs using a set of potential fields optimized by a genetic algorithm to maximize the bandwidth coverage and longevity during the second phase. The potential-field-based real-time control of mobile agents was first introduced by [PERSON] [23]. Owing to their simplicity, many researchers used potential fields to control UAVs and other autonomous agents in different domain-specific tasks [14; 24; 25]. [PERSON] [26] used potential fields to deploy mobile sensor networks to cover an area. In their approach, an agent experienced repelling potential fields based on the distance from other agents and obstacles and moved toward unexplored areas. [PERSON] [27] introduced a mobile network deployment algorithm with the constraint that each agent has at least \(K\) neighbors, where \(K\) is a user-defined number.