MOVES: A MemOry-based VEhicular Social forwarding technique

Recently, the new paradigm of Vehicular Social Networks (VSNs) has stimulated a lot of interest in the research community. The rationale behind this interest relies on the integration of social relations in the Internet of Vehicles, taking into account of the human component – in terms of preferences and interests of individuals – and then allowing to distinguish nodes based on social ties. This feature affects the content dissemination procedures in VSNs, so that the most social node within a transmission range is expected to be the most appropriate next-hop forwarder, for higher network performance achievement. Leveraging on such premises, in this paper we propose a MemOry-based VEhicular Social forwarding approach, namely MOVES, that builds its packet forwarding logic by considering both the past and present “social” pattern of the nodes. MOVES is inspired by a previous forwarding mechanism, namely SCARF, that integrates the social components and physical features in its forwarding mechanism. MOVES has been compared to SCARF, and other existing forwarding approaches, in different scenarios and with real data in terms of delivery ratio, overhead and latency; results show its effectiveness of including the “social memory” in the selection of the next-hop forwarder.