4 resultados para communication protocol

em University of Queensland eSpace - Australia


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Pervasive computing applications must be engineered to provide unprecedented levels of flexibility in order to reconfigure and adapt in response to changes in computing resources and user requirements. To meet these challenges, appropriate software engineering abstractions and infrastructure are required as a platform on which to build adaptive applications. In this paper, we demonstrate the use of a disciplined, model-based approach to engineer a context-aware Session Initiation Protocol (SIP) based communication application. This disciplined approach builds on our previously developed conceptual models and infrastructural components, which enable the description, acquisition, management and exploitation of arbitrary types of context and user preference information to enable adaptation to context changes

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Arguably, the world has become one large pervasive computing environment. Our planet is growing a digital skin of a wide array of sensors, hand-held computers, mobile phones, laptops, web services and publicly accessible web-cams. Often, these devices and services are deployed in groups, forming small communities of interacting devices. Service discovery protocols allow processes executing on each device to discover services offered by other devices within the community. These communities can be linked together to form a wide-area pervasive environment, allowing processes in one p u p tu interact with services in another. However, the costs of communication and the protocols by which this communication is mediated in the wide-area differ from those of intra-group, or local-area, communication. Communication is an expensive operation for small, battery powered devices, but it is less expensive for servem and workstations, which have a constant power supply and 81'e connected to high bandwidth networks. This paper introduces Superstring, a peer to-peer service discovery protocol optimised fur use in the wide-area. Its goals are to minimise computation and memory overhead in the face of large numbers of resources. It achieves this memory and computation scalability by distributing the storage cost of service descriptions and the computation cost of queries over multiple resolvers.