How to Download More Data from Neighbors? A Metric for D2D Data Offloading Opportunity

Abstract

Mobile devices in close proximity can be connected in a device-to-device (D2D) manner to transfer digital objects (e.g., videos) to each other. By using D2D data offloading, mobile users can reduce the cost for data service from wireless cellular networks. However, due to users’ mobility, the opportunity for a user to obtain his interested objects via D2D communication is transient. In this paper, we first propose an expected available duration (EAD) metric to evaluate the opportunity that an object can be downloaded by a user via D2D data offloading. The EAD metric takes into account the pairwise connectivity of users, social influence between users, diffusion of digital objects, and the time that users would like to wait for D2D data offloading. We then propose a distributed algorithm for a mobile device to determine the EAD of each object. Given a set of available objects in the neighborhood, a mobile device will first download the object that has the smallest EAD. We validate our model via trace-driven simulations. Results show that our proposed algorithm can effectively find the object that should be first downloaded. Comparing with existing schemes, our work can help users download more data via D2D data offloading.