Transient data delivery using fine-grained mobility data in spontaneous smartphone networks

The commercial success of smartphones increases the feasibility of mobile ad hoc networking in daily life; we define such networks as spontaneous smartphone networks (SSNs). Efficient data delivery in SSNs is challenging because of the low node density, ambiguous contact opportunities, and short message lifetime. The existing schemes attempt to select optimal relays via various cumulative metrics (e.g.; encounter history, social centrality, or contact distribution), whose effectiveness is ambiguous and suboptimal. In this paper, we introduce a Markov predictor-based transient delivery scheme that quantifies the regularity of small time scale movement for forwarding decisions. Unlike previous works, we utilized fine-grained mobility data to reduce errors of estimating contact opportunities and contact duration. On the basis of this forwarding strategy, we developed a multi-copy routing scheme. The evaluation using real traces indicates that the proposed approach outperforms compared alternatives in terms of delivery rate and cost.