  Cellular networks are overloaded due to the mobile traffic surge, and mobile
social network (MSNets) carrying information flow can help reduce cellular
traffic load. If geographically-nearby users directly adopt WiFi or Bluetooth
technology (i.e., leveraging proximity-based communication) for information
spreading in MSNets, a portion of mobile traffic can be offloaded from cellular
networks. For many delay-tolerant applications, it is beneficial for traffic
offloading to pick some seed users as information sources, which help further
spread the information to others in an epidemic-like manner using
proximity-based communication. In this paper, we develop a theoretical
framework to study the issue of choosing only k seed users so as to maximize
the mobile traffic offloaded from cellular networks via proximity-based
communication. We introduce a gossip-style social cascade (GSC) model to model
the information diffusion process, which captures the epidemic-like nature of
proximity-based communication and characterizes users' social participation as
well. For static networks as a special-case study and mobile networks, we
establish an equivalent view and a temporal mapping of the information
diffusion process, respectively, leveraging virtual coupon collectors. We
further prove the submodularity in the information diffusion and propose a
greedy algorithm to choose the seed users for proximity-based traffic
offloading, yielding a solution within about 63% of the optimal value to the
traffic offloading maximization (TOM) problem. Experiments are carried out to
study the offloading performance of our approach, illustrating that
proximity-based communication can offload cellular traffic by over 60% with a
small number of seed users and the greedy algorithm significantly outperforms
the heuristic and random algorithms.
