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Caching popular contents in vehicle-to-vehicle (V2V) communication networksis expected to play an important role in road traffic management, therealization of intelligent transportation systems (ITSs), and the delivery ofmultimedia content across vehicles. However, for effective caching, the networkmust dynamically choose the optimal set of cars that will cache popular contentand disseminate it in the entire network. However, most of the existing priorart on V2V caching is restricted to cache placement that is solely based onlocation and user demands and does not account for the large-scalespatio-temporal variations in V2V communication networks. In contrast, in thispaper, a novel spatio-temporal caching strategy is proposed based on the notionof temporal graph motifs that can capture spatio-temporal communicationpatterns in V2V networks. It is shown that, by identifying such V2V motifs, thenetwork can find sub-optimal content placement strategies for effective contentdissemination across a vehicular network. Simulation results using real tracesfrom the city of Cologne show that the proposed approach can increase theaverage data rate by $45\%$ for different network scenarios.

Spatio-Temporal Motifs for Optimized Vehicle-to-Vehicle (V2V) Communications