Temporal complex networks modeling applied to vehicular ad-hoc networks

VANETs solutions use aggregated graph representation to model the interaction among vehicles and different complex network measures quantify some topological characteristics. This modeling ignores temporal interactions between cars, causing loss of information or unrealistic behavior. work proposes both graphs applications. To verify viability this model, we initially perform a comparative analysis considering five real datasets. shows that is inefficient in aspects networks. After that, evaluation through simulation by impact applied deployment RSUs. First, compare solution based on our with greedy algorithm an choose positions In scenario 70 RSUs, have 77% 65% coverage (greedy algorithm), respectively. Second, evaluate as features genetic algorithm. The approach betweenness had better result 90% area against 61% one same scenario.