An evolutionary algorithm for roadside unit deployment with betweenness centrality preprocessing.

This paper presents a genetic algorithm strategy to improve the deployment of roadside units in VANETs. We model the problem as a Maximum Coverage with Time Threshold Problem and the network as a graph, and perform a preprocessing based on the betweenness centrality measure. Moreover, we show that by using a simple genetic algorithm with few interactions, we achieve better results when compared with other strategies. We consider five realistic datasets to evaluate our approach and the experiments show that it finds better results in all scenarios, mainly when compared with the greedy-based approach, which increased up to 20% of the vehicle coverage in specific scenarios. Additionally, the betweenness centrality preprocessing helps the solution convergence by selecting candidate intersections, which suggests that this preprocessing is a good measure to explore in these scenarios.