  Caching popular contents in vehicle-to-vehicle (V2V) communication networks
is expected to play an important role in road traffic management, the
realization of intelligent transportation systems (ITSs), and the delivery of
multimedia content across vehicles. However, for effective caching, the network
must dynamically choose the optimal set of cars that will cache popular content
and disseminate it in the entire network. However, most of the existing prior
art on V2V caching is restricted to cache placement that is solely based on
location and user demands and does not account for the large-scale
spatio-temporal variations in V2V communication networks. In contrast, in this
paper, a novel spatio-temporal caching strategy is proposed based on the notion
of temporal graph motifs that can capture spatio-temporal communication
patterns in V2V networks. It is shown that, by identifying such V2V motifs, the
network can find sub-optimal content placement strategies for effective content
dissemination across a vehicular network. Simulation results using real traces
from the city of Cologne show that the proposed approach can increase the
average data rate by $45\%$ for different network scenarios.
