In recent years, many researchers have studied context-awareness to support non-intrusive adaptability of context-aware applications. Context-aware applications benefit from emerging technology that connects everyday objects and provides opportunities to collect and use context information from various sources. Context-awareness helps to adapt continuously to new situations and to turn a static computing environment into a dynamic ecology of smart and proactive applications. In this chapter, we present our framework that manages and uses context information to adapt applications and the content they provide. We show how application adaptation can be handled at the composition level, by reconfiguring, redeploying and rewiring components, e.g. to fall back into reduced functionality mode when redeploying an application on a handheld. The key features of our context-aware adaptation framework notonly include local adaptations of context-aware applications and content, but also the addressing of context in large scale networks and the contextaware redeployment of running applications in a distributed setting. We discuss how adaptation is handled along various levels of abstraction (user, content, application, middleware, network) and illustrate the flexibility of context-aware content and application adaptation by means of a realistic use case scenario.
Due to the proliferation of small networked mobile devices, the number of (indirectly) interconnected services in pervasive computing environments may grow without bound. The network contains a potentially enormous amount of context aware services that sense, gather and distribute context information. Without a central context repository, or a central server that locates the context information, it is a challenge to address parts of the environment that contain relevant context information. In this paper, we propose a model for addressing context and an algorithm for context gathering and distribution that imposes a virtual structure on the network, that aligns with the actual context information within a pervasive computing environment. Distribution of context uses an adapted form of flooding, that is context aware. Our evaluation shows that the algorithm performs substantially better than bounded flooding, if address accuracy is at least 30%.
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