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

TL;DR

This paper introduces D2D Big Data, a platform leveraging large-scale data analysis to enhance content sharing and offloading in mobile networks through device-to-device communication, addressing limitations of prior small-scale studies.

Contribution

The paper presents a big data platform and analysis of a massive dataset to improve D2D content dissemination and offloading strategies in large-scale mobile networks.

Findings

Successful deployment of a big data platform for D2D sharing analysis

Analysis of 866 million sharing activities over 13 weeks

Insights into user behaviors and content propagation patterns

Abstract

Recently the topic of how to effectively offload cellular traffic onto device-to-device (D2D) sharing among users in proximity has been gaining more and more attention of global researchers and engineers. Users utilize wireless short-range D2D 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 impacts 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 emerging Big Data techniques, we propose to design a big data platform, named D2D Big Data, in order to encourage the wireless D2D 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 TBytes) from a popular D2D sharing application (APP), which contains 866 million D2D sharing activities on 4.5 million files disseminated via nearly 850 million users in 13 weeks. By abstracting and analyzing multidimensional 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 propose to unveil a promising upcoming future of wireless D2D communications.