COSMOS A Context Sensitive Model For Dynamic Configuration Of Smartphones Using Multifactor Analysis
This addresses the need for automated smartphone configuration for users, but it is incremental as it applies existing methods like decision trees to a specific domain.
The paper tackles the problem of configuring smartphones effectively by proposing COSMOS, a context-sensitive model that uses multifactor analysis and decision trees to adjust settings based on user context without manual input, achieving a settings relevancy metric of 90.95% in validation.
With the prolific growth in usage of smartphones across the spectrum of people in the society it becomes mandatory to handle and configure these devices effectively to achieve optimum results from it. This paper proposes a context sensitive model termed COSMOS (COntext Sensitive MOdel for Smartphones) for configuring the smartphones using multifactor analysis with the help of decision trees. The COSMOS model proposed in this paper facilitates the configuration of various smartphone settings implicitly based on the user's current context, without interrupting the user for various inputs. The COSMOS model also proposes multiple context parameters like location, scheduler data, recent call log settings etc to decide the appropriate settings for the smartphones. The proposed model is validated by a prototype implementation in the Android platform. Various tests were conducted in the implementation and the settings relevancy metric value of 90.95% confirms the efficiency of the proposed model.