Dynamic Periodic Location Encounter Network Analysis for Vehicular Social Networks

Abstract

Vehicular social networks (VSNs) are considered as an emerging class of vehicular ad hoc networks (VANETs) with social attributes. In contrast to the traditional randomly distributed human social networks, vehicles normally only move along fıxed paths. As such, information sharing and forwarding become challenging because of the high speed of vehicles, complicated propagation environment, and quickly changing network topologies. The social characteristics and human behavior features are used to improve the effıciency of information delivery among vehicles in VANETs. In this paper, we propose a dynamic periodic location encounter network (DPLEN) model that considering both time-varying and location-varying characteristics of vehicles. We calculate the line similarity for each encountered pair of vehicles based on the periodic time and location information of the encounter event, and analyze social attributes based on the network model, such as the Community structure, Betweenness Centrality and K-shell. We also use the similarity to design two routing protocols for unicast and multicast patterns. After testing the proposed model on empirically collected datasets of mobility traces, the results show that these two routing protocols are able to greatly improve the efficiency of information forwarding in the VSNs.