Mobile App Usage Patterns Aware Smart Data Pricing

The explosive growth of traffic-consumption by mobile devices is leading to severe cellular network congestion, which is posing challenges for Internet Service Providers (ISPs) to provide good quality services with limited cellular capacity and impacting the user's experience. Data pricing has been proven to be an effective way to enhance both the service quality and ISP's profit. However, traditional data pricing schemes do not consider the real Mobile Application (App) Usage Patterns (MAUPs) among large scale cellular networks. In this paper, MAUPs aware smart data pricing scheme is proposed. In our work, we firstly extract and model the users' app usage behaviors of approximately 9,600 cellular towers as two-dimensional MAUPs (time, app category). Then 7 distinct derived MAUPs are considered to be incorporated into the user satisfaction model and ISP's profit model. The performance of our proposal is evaluated and verified by numerical experiments from the aspects of ISP's profit, consumption surplus, capacity utilization and traffic efficiency. The MAUPs based pricing scheme can be periodically updated according to the operational conditions and therefore significantly instructive for ISPs.