Trajectory improves data delivery in vehicular networks

Abstract

Efficient data delivery is a great challenge in vehicular networks because of frequent network disruption, fast topological change and mobility uncertainty. The vehicular trajectory knowledge plays a key role in data delivery. Existing algorithms have largely made predictions on the trajectory with coarse-grained patterns such as spatial distribution or/and the inter-meeting time distribution, which has led to poor data delivery performance. In this paper, we mine the extensive trace datasets of vehicles in an urban environment through conditional entropy analysis, we find that there exists strong spatiotemporal regularity. By extracting mobile patterns from historical traces, we develop accurate trajectory predictions by using multiple order Markov chains. Based on an analytical model, we theoretically derive packet delivery probability with predicted trajectories. We then propose routing algorithms taking full advantage of predicted vehicle trajectories. Finally, we carry out extensive simulations based on real traces of vehicles. The results demonstrate that our proposed routing algorithms can achieve significantly higher delivery ratio at lower cost when compared with existing algorithms.