Characterizing Urban Vehicle-to-Vehicle Communications for Reliable Safety Applications

The IEEE 802.11p-based dedicated short range communication (DSRC) is essential to enhance driving safety and improve road efficiency by enabling rapid cooperative message exchanging. However, there is a lack of good understanding on the DSRC performance in urban environments for vehicle-to-vehicle (V2V) communications, which impedes its reliable and efficient application. In this paper, we first conduct intensive data analytics on V2V performance, based on a large amount of real-world DSRC communications trace collected in Shanghai city, and obtain several key insights as follows. First, among many context factors, the non-line-of-sight (NLoS) link condition is the major factor degrading V2V performance. Second, the durations of line-of-sight (LoS) and NLoS transmission conditions follow power law distributions, which indicate that the probability of experiencing long LoS/NLoS conditions both could be high. Third, the packet inter-reception (PIR) time distribution follows an exponential distribution in the LoS conditions but a power law in the NLoS conditions, which means that the consecutive packet reception failures rarely appear in the LoS conditions but can constantly appear in the NLoS conditions. Based on these findings, we propose a context-aware reliable beaconing scheme, called CoBe, to enhance the broadcast reliability for safety applications. The CoBe is a fully distributed scheme, in which a vehicle first detects the link condition with each of its neighbors by machine learning algorithms, then exchanges such link condition information with its neighbors, and finally selects the minimal number of helper vehicles to rebroadcast its beacons to those neighbors in bad link condition. To analyze and evaluate the CoBe performance, a two-state Markov chain is devised to model beaconing behaviors. The extensive trace-driven simulations are conducted to demonstrate the efficacy of CoBe.