Intermittently Connected Vehicle-to-Vehicle Networks: Detection and Analysis

Vehicular Adhoc Networks (VANETs) are dedicated to improve the safety and efficiency of transportation systems through vehicle to vehicle or vehicle to road side communications. VANETs exhibit dynamic topology and intermittent connectivity due to high vehicle mobility. These distinguished features declare a challenging question: how to detect on the fly vehicular networks such that we can explore mobility-assisted message dissemination and topology control in VANETs. As being closely related to network dynamics, vehicle mobility could be explored to uncover network structure. In this paper, we have observed that mobility of vehicle, rather than being random, shows temporal locality (i.e., frequently visiting several communities like home and office), and spatial locality (i.e., velocity constrained by road layout and nearby vehicles). We first examine temporal locality using a campus trace, then measure temporal locality similarity between two vehicles based on the relative entropy of their location preferences. By further incorporating spatial locality similarity, we introduce a new metric, namely dual locality ratio (DLR), which represents the mobility correlation of vehicles. Simulation results show that DLR can effectively identify dynamic vehicular network structures. We also demonstrate applications of DLR for improving performances of data forwarding and clustering in vehicle-to-vehicle networks.