Framework for intelligent service adaptation to user's context in next generation networks

Abstract

Context-aware applications aim at providing personalized services to end users. Sensors and context sources are able to provide enormous amounts of valuable information about individuals that can be used to drive the behavior of services and applications, and adapt them to the specific conditions and preferences of each user. Thanks to advances in mobility, convergence and integration, increasingly larger amounts of these data are available in the Internet. However, this context information is usually fragmented, and traditionally applications have had to take care of context management themselves. This work presents a solution for a converged context management framework and how it can be employed in a future Internet to integrate data from all context sources and serve it to client applications in a seamless and transparent manner. This framework takes advantage of the intelligent and convergent features of next-generation networks, allowing seamless integration, monitoring, and control of heterogeneous sensors and devices under a single context-aware service layer. This layer is centered on a context intelligence module, capable of combining clustering algorithms and semantics to learn from user usage history and take advantage of that information to infer missing or high-level context data.