## 1 Introduction  
Centrality is a concept widely employed in social network analysis (SNA) to classify nodes as central or, more importantly, in the network [1, 2]. Several approaches have been developed to compute node centrality [2]; however, the three most commonly-used approaches in SNA are degree centrality, closeness centrality and betweenness centrality [3, 4]. Although there are different centrality metrics in the literature, most of them fit into two categories such as radial and medial measures [2]. Radial measures assess information flow that originates from, or ends at, a given node. It includes degree and closeness centrality. On the other hand, medial measures assess the geodesic distance that crosses through a given node [2], which includes all variations of the betweenness centrality. The calculation of centrality measures requires global knowledge of the network topology [5], and more often than not, this knowledge is not available. Besides, it is usually difficult to obtain this information in large-scale or highly dynamic networks. Taking this into consideration, the concept of the ego-network has attracted great attention in the scientific community. This stems from the fact that its topological analysis can be carried out locally by individual nodes without the need for global knowledge of the network [5; 6; 7]. Another advantage of the ego-network is the simple structure to collect data compared to collecting data from the entire network. By definition, the ego-network is a subnetwork centred on a single node, called the ego, whereas one-hop nodes are called alters [5; 7]. In an ego-network, only the nodes that are directly connected to the ego belong to the subnetwork [5; 7]. Based on the idea of ego-networks and the betweenness centrality measure, another perspective in SNA has emerged, named egocentric betweenness [5]. The egocentric betweenness measure has been adapted for several types of networks such as wireless sensor networks [8], delay-tolerant networks [9] and wireless mesh networks [10]. However, this measure has not been systematically investigated in vehicular ad hoc networks (VANETs), which have unique characteristics such as high mobility of nodes, short connection time and frequent network partitioning. One of the aspects of VANETs that sets them apart from other networks is their highly dynamic topology [11]. The nodes in these networks are mainly composed of vehicles, and their mobility is restricted and imposed by traffic conditions and road pathways [12; 13; 14]. Moreover, the technologies commonly used in VANETs are sensing, storage, computing and wireless communication devices. In this type of network, any external interaction is done through communication links either between a pair of vehicles (vehicle-to-vehicle) or between the vehicle and roadside unit (vehicle-to-infrastructure) [12; 15]. The development of services over VANETs has attracted researchers from both academia and industry due to the wide diversity of applications. They can range from vehicle traffic monitoring, system-aided navigation and cooperative collision warning, to infotainment [16; 17; 18; 19]. Many of these services need to be aware of the local situation [16; 17]. To reach this awareness, one can take advantage of either cooperative awareness message (CAM) [20] (European standard) or the basic safety message (BSM) [21] (American standard). In both standards, the messages contain information regarding vehicle status such as position, speed, direction, location coordinates and other vehicle information [22]. The process of acquiring local awareness is usually performed by broadcasting one-hop messages. As a result, each vehicle will be aware of its neighbour's vehicles within its transmission range. The periodic exchange of one-hop messages is known as beaconing [22]. Due to the instabilities in the communication links induced by the highly dynamic topology, calculating the betweenness centrality scores in a VANET is a challenging task.