Context-aware Mobile Internet Quality Model
Mobile Internet (MI) enables its users to access diverse Internet applications 'on the move', for example on a smartphone or a laptop in a car or a train. On a growing scale, these applications contribute to achieving important professional or personal tasks when out and mobile. For example, having an important call, accessing information or finalize documents before a deadline. However, the quality of the MI the user experiences vary depending on the user's location, time, mobility level and other context factors. If it happens that a user does not manage to achieve the task due to a low quality offered by the MI, he/she may become frustrated. Additionally user's professional or personal life may get negatively influenced. It is necessary to understand individual's needs and expectations for MI. We need to provide them the best possible MI service, or at least inform them about the low quality, such that they can adjust their expectations and manage their tasks differently.
Subject and Objective
Our principal aim is to understand the needs and expectations of the users for Mobile Internet (MI) quality and to adapt the mobile applications and services to best meet these expectations and needs. In case these cannot be assured we need to inform the user, ideally ahead of time (i.e., in a predictive manner). The objective of this project is to build a prediction model for MI quality based on quality data collected by many MI users being in the same location, time and using the same application in the past.
For example, you are commuting every day from Bern to Zurich by train. Today, you wish to participate in a videoconference happening while you are in that train. The service may proactively adapt the video and audio quality based on the MI quality prediction model. It can manage to provide you the best possible quality while compensating for disconnections and network handovers. Alternatively, it may inform you that the videoconference participation is impossible to be achieved and suggest you either to participate via an audio call only, or stay in café in Bern and take a later train for the best quality.
Our research will help users to manage better their use of diverse mobile applications and services, while not getting frustrated with the MI quality to better meet their professional or personal goals. The users will better understand what MI quality is provided to them and what they can expect in which context. Understanding users behaviour and needs will also help to better define policies for providers of MI, protecting the users and assuring their quality, for example at some minimum requested level.