Quality of Context: models and applications for context-aware systems in pervasive environments

Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.

The design and applications of a context service

Context awareness enables applications to adapt themselves to their computing environment in order to better suit the needs of the user and the tasks. This paper describes a general middleware infrastructure for context collection and dissemination, realized as a Context Service. By way of two example applications, this paper also illustrates how context information provided by our context service can be exploited to enhance the user experience. These two applications are built upon the abstraction provided by the Context Service and thus help validate the design of this service. The first application, a Notification Dispatcher, uses context to route messages to the most appropriate communication device for a recipient. The second application, a context-aware content distribution system, uses context to predict users' access to web content, and uses these predictions to pre-process and pre-distribute content in order to reduce the access latency.