Networked Data Transaction in Mobile Networks: A Prediction-based Approach Using Auction

Currently, The unprecedented increasing of mobile data traffic challenges the performance of current cellular networks. To meet this explosive demands of mobile traffic, the mobile data offloading technology has been proposed to alleviate the traffic load by moving traffic load of cellular networks to other wireless networks provided by infrastructures such as small-cell base stations. In this work, an infrastructure-free offloading method is proposed, which realizes the data transaction among mobile users by applying the hotspot function of smartphones. In this transaction, mobile users with redundant data perform as accessible Wi-Fi hotspots, and sell their mobile data to users with data requirements. Considering the scenarios with multiple data sellers, a networked auction model is introduced to model the process of data transaction. Additionally, high efficient data allocation mechanisms are designed in this work, which decide how to schedule the data transaction in different time slots, based on the established auction model. Simulation results indicate that introducing the prediction information of user behaviors can effectively improve the performance of data allocation, and achieve a high efficient data transaction operation.