An Adaptive Context Acquisition Framework to Support Mobile Spatial and Context-Aware Applications

The increasing number of mobile devices allows users to access applications anytime and anywhere. In such applications, location is a key information to improve the interaction between user and services. Existing applications combine location with other context information, such as weather, user’s activity, temperature, among others. However, developing context-aware applications is still a non-trivial task due to the complexity to implement context management. Additionally, existing context management infrastructures are too brittle to handle changes in the underlying execution infrastructure. In this scenario, this work proposes a context acquisition framework, which tries to reduce the development complexity of mobile spatial and context-aware applications. The framework uses tuples space and OSGi to promote uncoupling and to adapt itself according to application requirements. A proof of concept was developed in order to show how spatial and context filters can be easily implemented during the development of a tracking application.