Improving Information Dissemination in Vehicular Networks by Selecting Appropriate Disseminators

Information dissemination applications (e.g. weather, news, advertisements, traffic, and so on) are among the most promising ones for vehicular networks. However, disseminating information to all vehicles in vehicular networks is not a trivial task because of their specific characteristics like high dynamic topology and different density values along time and space. The proposals found in literature focused on improved flooding schemes that, in realistic scenarios, may cause unnecessary overhead and redundant data. This work addresses the dissemination to all vehicles problem by assuming that some vehicles are more appropriate to be good disseminators than others. This hypothesis was validated through a large-scale urban mobility scenario and two heuristics were proposed to select the appropriate disseminators. Results showed that the proposed heuristics were able to achieve high coverage (more than 97% of vehicles) with low redundant data.