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Recently the topic of how to effectively offload cellular traffic onto device-to-device sharing among users in proximity has been gaining more and more attention from global researchers and engineers. Users utilize wireless short-range device-to-device communications for sharing contents locally, due to not only the rapid sharing experience and free cost, but also high accuracy on deliveries of interesting and popular contents, as well as strong social impact among friends. Nevertheless, the existing related studies are mostly confined to small-scale datasets, limited dimensions of user features, or unrealistic assumptions and hypotheses on user behaviors. In this article, driven by the emerging big data techniques, we propose to design a big data platform, named D2D big data, in order to encourage wireless device-to-device communications among users effectively, to promote contents for providers accurately, and to carry out offloading intelligence for operators efficiently. We deploy a big data platform and further utilize a large-scale dataset (3.56 TB) from a popular device-to-device sharing application that contains 866 million device-to-device sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multi-dimensional features, including online behaviors, content properties, location relations, structural characteristics, meeting dynamics, social arborescence, privacy preservation policies, and so on, we verify and evaluate the D2D big data platform regarding predictive content propagating coverage. Finally, we discuss challenges and opportunities regarding D2D big data and unveil the promising upcoming future of wireless device-to-device communications.

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