Epidemic-like proximity-based traffic offloading

Cellular networks are overloaded due to the mobile traffic surge, and mobile social networks (MSNets) can be leveraged for traffic offloading. In this paper, we study the issue of choosing seed users for maximizing the mobile traffic offloaded from cellular networks. We introduce a gossip-style social cascade (GSC) model to model the epidemic-like information diffusion process in MSNets. For static-case and mobile-case networks, we establish an equivalent view and a temporal mapping of the information diffusion process, respectively. We further prove the submodularity in the information diffusion and propose a greedy algorithm to choose the seed users for traffic offloading, yielding a sub-optimal solution to the NP-hard traffic offloading maximization (TOM) problem. Experiments are carried out to study the offloading performance, illustrating that the greedy algorithm significantly outperforms the heuristic and random algorithms, and user mobility can help further reduce cellular load.