Geographic routing based on predictive locations in vehicular ad hoc networks

Many geographic routing algorithms have been proposed for vehicular ad hoc networks (VANETs), which have the strength of not maintaining any routing structures. However, most of which rely on the availability of accurate real-time location information. It is well known that vehicles can be intermittently connected with other vehicles. Thus, in such networks, it is difficult or may incur considerable cost to retrieve accurate locations of moving vehicles. Furthermore, the location information of a moving vehicle available to other vehicles is usually time-lagged since it is constantly moving over time. Fortunately, we observe that the short-term future locations of vehicles can be predicted. Based on the important observation, we propose a novel approach for geographic routing which exploits the predictive locations of vehicles. Thus, we have developed a prediction technique based on the current speed and heading direction of a vehicle. As a result, the request frequency of location updates can be reduced. In addition, we propose two forwarding strategies and three buffer management strategies. We have performed extensive simulations based on real vehicular GPS traces collected from around 4,000 taxis in Shanghai, China. Simulation results clearly show that geographic routing based on predictive locations is viable and can significantly reduce the cost of location updates.