A novel vehicular location prediction based on mobility patterns for routing in urban VANET

Abstract

Abstract Location information is crucial for most applications and protocol designs in high-speed vehicular ad-hoc networks (VANETs). In traditional approaches, this is obtained by object tracking techniques that keep tracking the objects and publish the information to the users. In highly dynamic environments, however, these approaches are not efficient as the target objects in VANETs are typically vehicles that present high mobility. Their locations keep changing in a large range so that the tracking and information publication algorithms have to be frequently invoked to obtain the instant locations of the objects. To deal with this problem, we propose a novel approach based on the observation that in high-speed VANET environment, the target objects are strictly constrained by the road network. Their mobilities are well patterned and many patterns can clearly be identified. These patterns can smartly be leveraged so that a large amount of control overhead can be saved. Towards this end, in this article we adopt Variable-order Markov model to abstract Vehicular Mobility Pattern (VMP) from the real trace data in Shanghai. We leverage VMP for predicting the possible trajectories of moving vehicles which help to keep the timely effectiveness of the evolutional location information. To reveal the benefits of VMP, we propose a Prediction-based Soft Routing Protocol (PSR), taking VMP as an advantage. The experimental results show that PSR significantly outperforms existing solutions in terms of control packet overhead, packet delivery ratio, packet delivery delay. In certain scenarios, the control packet overhead can be saved by up to 90% compared with DSR, and 75% compared with WSR.