Abstract
In the traditional approach for centrality measures, also known as sociocentric, a network node usually requires global knowledge of the network topology in order to evaluate its importance. Therefore, it becomes difficult to deploy such an approach in large-scale or highly dynamic networks. For this reason, another concept known as egocentric has been introduced, which analyses the social environment surrounding individuals (through the ego-network). In other words, this type of network has the benefit of using only locally available knowledge of the topology to evaluate the importance of a node. It is worth emphasizing that in this approach, each network node will have a sub-optimal accuracy. However, such accuracy may be enough for a given purpose, for instance, the vehicle selection mechanism (VSM) that is applied to find, in a distributed fashion, the best-ranked vehicles in the network after each topology change. In order to confirm that egocentric measures can be a viable alternative for implementing a VSM, in particular, a case study was carried out to validate the effectiveness and viability of that mechanism for a distributed information management system. To this end, we used the egocentric betweenness measure as a selection mechanism of the most appropriate vehicle to carry out the tasks of information aggregation and knowledge generation. Based on the analysis of the performance results, it was confirmed that a VSM is extremely useful for VANET applications, and two major contributions of this mechanism can be highlighted: (i) reduction of bandwidth consumption; and (ii) overcoming the issue of highly dynamic topologies. Another contribution of this work is a thorough study by implementing and evaluating how well egocentric betweenness performs in comparison to the sociocentric measure in VANETs. Evaluation results show that the use of the egocentric betweenness measure in highly dynamic topologies has demonstrated a high degree of similarity compared to the sociocentric approach.
Highlights
Centrality is a concept widely employed in social network analysis (SNA) to classify nodes as central or, more importantly, in the network [1,2]
The first set of experiments investigated how accurately egocentric betweenness scores correlated with the sociocentric betweenness scores in a vehicular ad hoc networks (VANETs) scenario; in other words, how accurate the results were when using only the local knowledge of the network topology to compute the betweenness score in highly dynamic networks, instead of using global knowledge of the topology
The results of this approach are shown in the scatter diagram set in Figure 7, which compares the two approaches for each vehicle traffic density
Summary
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]; the three most commonly-used approaches in SNA are degree centrality, closeness centrality and betweenness centrality [3,4]. 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. Medial measures assess the geodesic distance that crosses through a given node [2], which includes all variations of the betweenness centrality
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