# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 1 Introduction  
Vehicular networks provide connectivity among vehicles and also among vehicles and traffic management authorities. In spite of the wide variety of applications for Intelligent Transport Systems (ITS) that are under the umbrella of vehicular networks, traffic safety applications have been undoubtedly one of the most researched in the last few years, particularly due to the benefits that they clearly offer to drivers and passengers [1; 2].  
Regarding traffic safety applications within the vehicular networks context, message propagation is the utmost important task since it assists vehicles involved in an accident to inform nearby vehicles and emergency services about the emergency situation. However, these communications are affected by obstacles, in particular when using the licensed ITS band at 5.9 GHz, as well as by the high mobility of vehicles, which constantly changes the network topology.  
Vehicular networks include Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications. In fact, the use of infrastructure improves the communication capabilities, which arevery important concerning safety applications, where the warning messages should reach the authorities, as well as the surrounding vehicles that can be affected by accidents, other vehicles' malfunction, among other issues.  
Roadside Units (RSUs) are infrastructure communication nodes within vehicular networks. RSUs play an important role in vehicular communications due to their capabilities of: (i) delivering important information to vehicles, (ii) forwarding received messages to final recipients or (iii) providing Internet access to vehicles. To sum up, RSUs are deployed to extend vehicle coverage and to improve network performance in vehicular networks [3].  
Vehicles' communication capabilities highly depend on the number of RSUs deployed and their coverage. However, RSUs are usually costly to install and to maintain; thus, there is a trade-off between full coverage (in terms of connectivity) through RSUs and their deployment cost. Consequently, authorities tend to limit the number of RSUs, especially in suburbs and less populated areas, making RSUs a scarce resource in vehicular environments. We consider that it is important to optimally deploy a limited number of RSUs in the most pertinent locations (i.e., those places that clearly allow RSUs to extend coverage and to improve the overall network performance). In fact, it is necessary to find an automatized method to obtain the best locations for the RSU to be deployed in any scenario, due to the great variation of urban street layouts.  
In this work, we propose a genetic algorithm for roadside unit deployment (GARSUD) in vehicular ad hoc networks (VANETs), and compare it against other deployment policies. We compare it against both geographic-based (i.e., the uniform mesh deployment Policy) [4] and the Density-based Road Side Unit deployment policy (D-RSU) [4] approaches. Our goal is three-fold: (i) to automate the deployment decision relying in our genetic-based algorithm, (ii) to minimize the deployment cost by only allocating the required number of RSUs to provide the necessary coverage, as well as (iii) to improve the communication capabilities of the vehicles in the scenario, in terms of reduced warning notification time, which is the time required to send warning messages to the emergency authorities.  
This paper is organized as follows: In Section 2, we present works that propose some approaches closely related to the topic presented in this paper. Section 3 details GARSUD, our approach, which is capable of determining the best location for every RSU, increasing the coverage provided while reducing the notification time of warning messages in any given scenario. Section 4 presents the simulation parameters that we considered to assess our proposal. In Section 5, we discuss the simulation results obtained. Finally, in Section 6, we present the conclusions drawn from the analysis made throughout this paper.