Nowadays the number of smartphone users is rising worldwide at a rapid pace. The current rate of expansion is generally higher than that of the development of wireless networks designed to transmit data generated by these devices. Bandwidth availability is too low compared to the users’ needs and bandwidth allocation is not managed optimally. This usually leads to network congestions, mostly in cases when there are too many mobile devices connected to the same Wi-Fi access point or mobile base station. These congestions are likely to cause minor inconveniences for those involved, or at worst, they can diminish the user experience greatly.
Under ideal conditions, users always receive just as much bandwidth as they need. In case of a network congestion, higher priority will be given to data traffic that can improve the quality of service to a great extent.
My solution to be shown provides an answer on how to prioritize mobile data traffic. In this thesis I will present a framework designed for iOS-based devices that collects information about the usage habits and the actual activity of iPhone and iPad users and stores it in a remote database while running in the background. On a web-based evaluation interface the stored data can be analyzed to draw conclusions about the temporal distribution of content consumption habits. The network operator, with this knowledge in hand, can handle network congestions more effectively in the future by allocating different priorities for users with different needs.