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Service Discovery with Personal Awareness in Smart Environments

Service Discovery with Personal Awareness in Smart Environments
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Author(s): Kobkaew Opasjumruskit (Friedrich Schiller University Jena, Germany), Jesús Expósito (Friedrich Schiller University Jena, Germany), Birgitta König-Ries (Friedrich Schiller University Jena, Germany), Andreas Nauerz (IBM Germany Research and Development GmbH, Germany) and Martin Welsch (Friedrich Schiller University Jena, Germany & IBM Germany Research and Development GmbH, Germany)
Copyright: 2014
Pages: 22
Source title: Creating Personal, Social, and Urban Awareness through Pervasive Computing
Source Author(s)/Editor(s): Bin Guo (Northwestern Polytechnical University, China), Daniele Riboni (University of Milano, Italy) and Peizhao Hu (NICTA, Australia)
DOI: 10.4018/978-1-4666-4695-7.ch004

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Abstract

Web service descriptions with Semantic Web annotations can be exploited to automate dynamic discovery of services. The approaches introduced aim at enabling automatic discovery, configuration, and execution of services in dynamic environments. In this chapter, the authors present the service discovery aspect of MERCURY, a platform for straightforward, user-centric integration and management of heterogeneous devices and services via a Web-based interface. In the context of MERCURY, they use service discovery to find appropriate sensors, services, or actuators to perform a certain functionality required within a user-defined scenario (e.g., to obtain the temperature at a certain location, book a table at a restaurant close to the location of all friends involved, etc.). A user will specify a service request, which will be fed to a matchmaker, which compares the request to existing service offers and ranks these offers based on how well they match the service request. In contrast to existing works, the service discovery approach the authors use is geared towards non-IT-savvy end users and is not restricted to single service-description formalism. Moreover, the matchmaking algorithm should be user-aware and environmentally adaptive (e.g. depending on the user’s location or surrounding temperature), rather than specific to simple keywords-based searches, which depend on the users’ expertise and mostly require several tries. Hence, the goal is to develop a service discovery module on top of existing techniques, which will rank discovered services to serve users’ queries according to their personal interests, expertise, and current situations.

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