Research On Strategy Of Mobile Data Offloading Based On Opportunistic Connection

With the rapid development of mobile Internet and the popularity of mobile terminal devices such as smart phones,tablet computers,personal computers and so on,the rapid growth of mobile data traffic has brought tremendous traffic pressure to cellular networks.Traditional solutions such as building more base stations or upgrading cellular network configurations have been unable to cope with the continuous growth of data traffic.The current research hotspot shows that mobile data offloading is one of the effective measures to solve the load pressure of cellular networks.Mobile data offloading based on opportunistic connection is an effective solution to cope with the surge of mobile data,taking advantage of opportunistic connection between users.Based on the real data provided by the mobile communication operator,this paper study the strategy of mobile data offloading based on opportunistic connection.We design the mobile data offloading algorithms respectively for two application scenarios of complete information and incomplete information.The main achievements are listed as follows:1.We construct the network based on the encounter relations between users.The network topology features,user mobility characteristics and user internet behavior characteristics are analyzed.We exploit random forest algorithm to predict the opportunistic connection between users.The prediction model based on random forest is applied to the real data and has achieved great performances.2.We propose a mobile data offloading algorithm based on Stackelberg game in a complete information scenario.Based on the results of the opportunistic connection prediction,we model the interaction characteristics among the operator,the content provider and demander as a Stackelberg game.We analyze the existence of Nash equilibrium in the Stackelberg game model constructed in this paper.An iterative algorithm is exploited to design mobile data offloading strategies.Experiment results show that the algorithm proposed in our paper can effectively reduce the traffic load in the cellular network.3.We propose a mobile data offloading algorithm based on the reinforcement learning in an incomplete information scenario.Based on the analysis of users' mobile data traffic demand,we design the system utility function.The Gradient Bandit algorithm in reinforcement learning is exploited to solve the optimal mobile data offloading strategies which maximize the system utility function.The experiments verified the effectiveness of the algorithm proposed in our paper.Taking advantage of the opportunistic connection between users,this paper propose mobile data offloading algorithms in the complete and incomplete scenarios respectively.The algorithms can effectively relieve the traffic load pressure of the operator,provide guidance for mobile data offloading decisions in two application scenarios,and well handle the continuous growth of data traffic.The research in this paper not only has practical significance,but also provides a new insight of mobile data offloading based on opportunistic connection.