## 1 Introduction
On the other hand, once having identified the highest-betweenness centrality node in the network, it can be used as a facilitator node to spread the information flow [9]. This measure has been frequently applied in the design of efficient data forwarding algorithms, for instance, in wireless sensor networks [8]. A distributed approach to calculate the egocentric betweenness score was implemented and evaluated with the socioeconomic metric in order to prove the feasibility of the egocentric betweenness measure in VANETs. To this end, we use a beaconing mechanism to broadcast one-hop messages about its local information. Once local information is received, each vehicle can compute its egocentric betweenness score. The main goal is to present the similarity of betweenness centrality considering two approaches: local knowledge-based (egocentric) and global knowledge-based (sociocentric). In addition, we apply the egocentric betweenness measure as a vehicle selection mechanism (VSM) to find the best-ranked vehicles in the network, for each topology change, in order to carry out the tasks of information aggregation and knowledge generation. The goal is to demonstrate the need of a VSMin distributed VANET applications. Furthermore, we want to prove that this mechanism can reduce bandwidth consumption taking into account the challenges of VANETs. The proposed approach was experimentally validated by an extensive set of simulations with different traffic densities in a real urban scenario. Simulation results have shown that egocentric betweenness scores do not correspond perfectly to the sociocentric betweenness scores in high dynamic topologies. However, it is worth highlighting the results about the similarity ranking of nodes because in most solutions, the ranking of the nodes is more important than their absolute scores [8; 9; 10]. In addition, our results have also demonstrated that the use of the egocentric betweenness measure as a VSM is very useful to reduce bandwidth consumption in a distributed VANET application. To the best of our knowledge, this is one of the first experimental studies that applies egocentric betweenness centrality in VANETs, in addition to demonstrating one of the applications in VANETs. The contributions of this paper can be summarized as follows:  
* The proposal of a distributed approach to compute egocentric betweenness scores over VANETs, in which vehicles only use local knowledge of the network topology;
* The experimental evidence that our proposed approach is scalable to a large number of vehicles and can handle high mobility of vehicles;
* A method to characterize the importance of a node in highly dynamic networks using the egocentric betweenness measure;
* Experiment results demonstrate that the use of the egocentric betweenness measure can be a viable option as a VSM in highly dynamic networks. The remainder of this paper is organized as follows. Section 2 provides a brief review of the related works from the literature that use centrality measures in several research areas, besides works that we used in our study case. Section 3 details socioeconomic and egocentric centrality measures. Our egocentric betweenness measure study in VANETs is described in Section 4. Some numerical results and comparisons between sociocentric and egocentric are presented in Section 5. Section 6 describes a case study that assesses the egocentric betweenness measure as a VSM for the knowledge generation about traffic congestion. Finally, Section 7 concludes this work and looks toward future work.