Semantic-Based Context-Aware Service Discovery in Pervasive-Computing Environments
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Recent technological advancements are enabling the vision of pervasive or ubiquitous computing to become a reality. Service discovery is vital in such a computing paradigm, where a great number of devices and software components collaborate unobtrusively and provide numerous services. Current service-discovery protocols do not make use of contextual information in discovering services, and as a result, fail to provide the most appropriate and relevant services for users. In addition, current protocols rely on keyword-based search techniques and do not consider the semantic description of services. Thus, they suffer from poor precision and recall. To address the need for a discovery architecture that supports the envisioned scenarios of pervasive computing, we propose a context-aware service-discovery protocol that exploits meaningful contextual information, either static or dynamic, to provide users with the most suitable and relevant services. The architecture relies on a shared, ontology-based, semantic representation of services and context to enhance precision and recall, and to enable knowledge sharing, capability-based search, autonomous reasoning, and semantic matchmaking. Furthermore, the architecture facilitates a dynamic service-selection mechanism to filter and rank matching services, based on their dynamic contextual attributes, which further enhances the discovery process and saves users time and effort. Our empirical results indicate the effectiveness and feasibility of the proposed architecture.