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Data Forwarding Techniques Based on Graph Theory Metrics in Vehicular Social Networks

Abstract : Intelligent inter-vehicle communication is a key research field in the context of vehicular networks that applies in real-life applications (e.g., management of accidents, intelligent fuel consumption, smart traffic jams, etc.). Considering different roles of nodes based on their " social aptitude " to relay information could provide a social component in the vehicular structure that can be useful in getting a clear prediction of the topological evolution in time and space proving to be very effective in managing intelligent data forwarding. In this work we characterize a vehicular network as a graph using the link layer connectivity level and we classify nodes on the basis of specific attributes characterizing their " social aptitude " to forward data. Two forwarding approaches are presented, based on different socialites that allow to (i) select the most social node (i.e., a social hub) or (ii) choose among various social nodes.
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Submitted on : Friday, June 29, 2018 - 10:54:32 AM
Last modification on : Monday, May 10, 2021 - 5:32:03 PM
Long-term archiving on: : Thursday, September 27, 2018 - 8:25:09 AM


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  • HAL Id : hal-01826237, version 1



Anna Maria Vegni, Valeria Loscrì, Pietro Manzoni. Data Forwarding Techniques Based on Graph Theory Metrics in Vehicular Social Networks. IEEE PIMRC 2018 - 29th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Sep 2018, Bologna, Italy. ⟨hal-01826237⟩



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