# Improving Roadside Unit Deployment in Vehicular Networks by Exploiting Genetic Algorithms
## 2 Related Work  
The application of evolutionary algorithms for optimizing practical issues has been widely extended in different fields, such as the real estate and investment sectors. For example, [PERSON] et al. [5] compared two different approaches based on evolutionary algorithms to assist with real estate appraisals. The experiments were conducted using training and testing sets prepared on the basis of 150 actual sale transactions made in a residential section of a Polish city. [PERSON] et al. [6] applied simulation and a polyploidy genetic algorithm to help in the development of resort projects, which require a continuous and larger investment of resources compared to conventional real estate projects. The proposal allows investors to achieve increased profits, as well as maintaining the cash flow at a secure level. Regarding the energy and environmental sector, [PERSON] et al. [7] performed an econometric analysis through an Evolutionary Polynomial Regression (EPR) technique that combines the effectiveness of genetic programming with the advantage of classical numerical regression. In particular, they studied the functional relationships between the aerodynamic parameters and the economic variables considered for a rapid evaluation of the convenience of an investment in roof-top wind turbine plants. As for the RSU deployment policies, several works have been proposed so far, including different approaches to determine the number of RSUs required to provide a functional RSU deployment in terms of connectivity within a given scenario [8]. This sort of approach is focused on reducing the number of RSUs required [9; 10], improving the overall network performance [11; 12; 13] or guarantying the Quality-of-Service (QoS) in vehicular networks when delivering data [14]. Other works also consider additional issues such as reducing the energy consumption of RSUs [3; 15; 16], improving vehicles' content downloading capabilities [9], selecting the most suitable configurations for RSUs deployed [17] or estimating the maximum deployment distance between two neighboring RSUs [18]. Moreover, those works rely on different techniques such as clustering [3], cumulative weight [8], time continuous homogeneous Markov chains [9], game theoretic tools [19] and Integer Linear Programming (ILP) [15]. The works presented in [12; 20] used an evolutionary-based approach to solve the RSU deployment problem. In particular, [PERSON] et al. [12] modeled the problem as a Maximum Coverage with Time Threshold Problem (MCTTP) and used a genetic algorithm to solve it. The algorithm was tested on four real-world datasets (i.e., Zurich and Winterthur downtowns and the rural areas of Baden and Baar) and compared to a greedy approach. However, the authors did not provide details about how they modeled communications between vehicles and RSUs. Regarding the proposal of [PERSON] et al. [20], they used a multiobjective evolutionary algorithm that accounts for the number of vehicles and their speed for locating roadside infrastructure. This approach is oriented to maximize the coverage, in terms of the time that vehicles are connected to the RSUs, and minimizes the deployment cost. Unlike our proposal, the authors did not study the effect on warning message delivery, and although they used real mobility traces, they only simulated a one-lane road of 1 km with one RSU and 10 moving cars at 40 km/h. Therefore, their results cannot be extrapolated to more complex roadmap layouts. To the best of our knowledge, the majority of proposals are focused on very specific scenarios (i.e., roadmap layouts and vehicle densities) and consider too simplified assumptions. In general, existing approaches are able to obtain good results under a single vehicle density or in simple layouts. However, using GARSU, we can determine suitable deployments for a wide range of densities and roadmap topologies.