Research of quality based autonomic context processing for pervasive applications

With the rapid development of the information technology, it is inevitable that the distributed mobile computing will evolve to the pervasive computing gradually whose final goal is fusing the information space composed of computers with the physical space in which the people are working and living in. Therefore, pervasive applications need a middleware that can detect and act upon any context changes created by the result of any interactions between users, applications, and surrounding computing environment for applications without users, interventions. To achieve this goal, one of the problems is how to continuously monitor/capture and interpret the environment related information efficiently to assure high context awareness by the middleware. Many attentions have been paid to the research of the context-aware pervasive applications. Existing context information that supports service (component) adaptations is provided by the context-aware infrastructure, which gathers, pre-processes and provisions context information from a variety of context information sources. However, the context-aware applications is usually prone to failures and disconnections that negatively impact on the ability of them to adapt (and therefore dramatically impact on their usability). So we should take the Quality of Context (QoC) into account. And the procession of gathering, pre-processing and providing context information must be autonomic to the end users. Therefore, we propose an autonomic quality management middleware to support QoC management based context provision. By this middleware, we can configure different strategies to refinery raw context, discard duplicate and inconsistent context adaptively so as to protect and provide QoS-enriched context information of users to context-aware applications and services.