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Recently the topic of how to effectively offload cellular traffic ontodevice-to-device (D2D) sharing among users in proximity has been gaining moreand more attention of global researchers and engineers. Users utilize wirelessshort-range D2D communications for sharing contents locally, due to not onlythe rapid sharing experience and free cost, but also high accuracy ondeliveries of interesting and popular contents, as well as strong socialimpacts among friends. Nevertheless, the existing related studies are mostlyconfined to small-scale datasets, limited dimensions of user features, orunrealistic assumptions and hypotheses on user behaviors. In this article,driven by emerging Big Data techniques, we propose to design a big dataplatform, named D2D Big Data, in order to encourage the wireless D2Dcommunications among users effectively, to promote contents for providersaccurately, and to carry out offloading intelligence for operators efficiently.We deploy a big data platform and further utilize a large-scale dataset (3.56TBytes) from a popular D2D sharing application (APP), which contains 866million D2D sharing activities on 4.5 million files disseminated via nearly 850million users in 13 weeks. By abstracting and analyzing multidimensionalfeatures, including online behaviors, content properties, location relations,structural characteristics, meeting dynamics, social arborescence, privacypreservation policies and so on, we verify and evaluate the D2D Big Dataplatform regarding predictive content propagating coverage. Finally, we discusschallenges and opportunities regarding D2D Big Data and propose to unveil apromising upcoming future of wireless D2D communications.

D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing in Large Scale Mobile Networks