Improving flow delivery with link available time prediction in software-defined high-speed vehicular networks

Due to the high-speed mobility of vehicles, reliable data delivery in vehicular networks is still a challenge. In this paper, we propose a link available time prediction based backup caching and routing (LBR) scheme in software-defined high-speed vehicular networks. In order to improve flow delivery performance, several modules, such as information awareness and link prediction modules, are designed in this software-defined architecture. Taking advantage of the embedded modules, the controller predicts the link remaining duration for each vehicle timely by the location and velocity information. The controller also establishes routing policies for flows with different duration by using LBR algorithm. Finally, the simulation scenario is set to a high-speed vehicle-to-infrastructure (V2I) network. The vehicle is a high-speed train. The roadside infrastructures belong to cellular networks. For comparison, greedy perimeter stateless routing with lifetime (GPSR-L) is introduced. The results demonstrate that our scheme outperforms GPSR-L with an improvement in the successful transmission. Further, we explore the benefit and cost of the backup caching compared with link prediction based routing (LR) algorithm. The results show that the backup caching of LBR enhances LR algorithm with the sacrifice of a reasonable cost.