Understanding mobility to improve D2D communication

Abstract

Device-to-Device (D2D) communication is already considered a fundamental technology for the next generation mobile networks. This new type of communication enables the offloading of the base station download demands by directly transmitting the content when devices are sufficiently near to each other. In this work, we analyze the role of different human mobility features to improve the cost-effectiveness of opportunistic forwarding in multi-hop D2D communication networks. We propose two algorithms, SAMPLER, which combines individuals' mobility patterns, points of interest, and social awareness, and GROUPS-NET, which employs the knowledge about the regularity of group mobility as a measure of social context, instead of detecting communities. The proposed algorithms use different strategies and were validated with real-world publicly available data sources. Both algorithms achieved better cost-effectiveness in multi-hop D2D forwarding when compared to the state-of-art solution.