A study of adaptive beacon transmission on Vehicular Ad-Hoc Networks

Vehicular Ad-Hoc Networks (VANETs) are special forms of Mobile Ad-Hoc Networks (MANETs). Due to vehicles moving with high speed, network topology frequently changes. Communicating and exchanging information between vehicles could be done by beaconing. Nevertheless, most of previously-proposed protocols for VANETs are designed to use constant beaconing rate. This results in an increase of beacon overhead and a degradation of protocols´ performances in dense area. Therefore, in this paper, we study parameters that affect adaptive beaconing, and then propose two methods to apply to adaptive beaconing. One is based on a statistical technique and the other is based on the machine learning technique. Simulation results indicate that our proposed methods can reduce beacon overhead while maintaining the reliability and speed of data dissemination of the protocol.