Real Time Context Aware Adaptation for Mobile Devices

Abstract

Mobile device and there content applications are increasingly use in todays. Therefor people need to use their device in different situations and different places, need to highly interact with the device and change the device parameters in each situation. Intend of this dissertation is presenting new interaction method between mobile device and its user with reduce the complexity of it. In this work propose device adapt to the user current situation by identifying the situation and response by itself. In this work context are captured and identified by available sensors in mobile device. This project proposes architecture for context aware adaptations with three layers context layer, adaptation layer and application layer. In the adaptation and learning process use Decision tree learning algorithm used to identify the current situation. This decision tree learning use real time context recognition and adaption to user context while application use in real environment. According to user location and environment it identified the current situation. Implemented a prototype that demonstrates this context awareness using J2ME and current situation was identified by the system under demonstrate context. This research shows that it is feasible to recognize current situation and it is lead to new interaction methodology with less interaction with the user.