DMP: Content Delivery With Dynamic Movement Pattern in Vehicular Networks

Weiyi Huang, Peng Li, Bo Li, Tao Zhang

Abstract: Vehicular ad-hoc networks (VANETs) have been widely studied in intelligent transportation. Content delivery is an important topic that attracts many researchers. Due to vehicles that may have intermittent connections and uncertain routes, it is difficult to select an appropriate node. In this paper, we analyze the movement pattern of vehicles from real taxis’ trajectories and propose a framework for delivery prediction, which aims to select appropriate nodes. First, we propose the framework which consists of a contact clique model, a social clique model, and a prediction model based on Markov chains, to characterize the movement pattern of vehicles. Second, we capture dynamic movement patterns by dividing the time requirement into equal length slots and construct clique sequences. Based on the fact that the sociality of nodes has strong temporal correlations, we utilize the prediction model to derive future cliques and evaluate two kinds of delivery performance in the future. Finally, we design a content delivery algorithm with dynamic movement pattern (DMP) to select the appropriate node. In our experiment, DMP performs better than that of other methods in terms of overhead, average hops. Also, as the number of nodes increases, our algorithm keeps small fluctuations in node sociality.