This paper proposes Travel Prediction-based Data forwarding (TPD), tailored and optimized for multihop vehicle-to-vehicle communications. The previous schemes forward data packets mostly utilizing statistical information about road network traffic, which becomes much less accurate when vehicles travel in a light-traffic vehicular network. In this light-traffic vehicular network, highly dynamic vehicle mobility can introduce a large variance for the traffic statistics used in the data forwarding process. However, with the popularity of GPS navigation systems, vehicle trajectories become available and can be utilized to significantly reduce this uncertainty in the road traffic statistics. Our TPD takes advantage of these vehicle trajectories for a better data forwarding in light-traffic vehicular networks. Our idea is that with the trajectory information of vehicles in a target road network, a vehicle encounter graph is constructed to predict vehicle encounter events (i.e., timing for two vehicles to exchange data packets in communication range). With this encounter graph, TPD optimizes data forwarding process for minimal data delivery delay under a specific delivery ratio threshold. Through extensive simulations, we demonstrate that our TPD significantly outperforms existing legacy schemes in a variety of road network settings.
Bibliographical noteFunding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning ( 2014006438 ). This research was supported in part by the ICT R&D programs of MSIP/IITP [14-824-09-013, Resilient Cyber-Physical Systems Research] and by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) [10041244, Smart TV 2.0 Software Platform].
- Data forwarding
- Vehicular network