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By Roman Obermaisser, Yunmook Nah, Peter Puschner, Franz J. Rammig
This publication constitutes the completely refereed post-proceedings of the fifth IFIP WG 10.2 foreign Workshop on software program applied sciences for destiny Embedded and Ubiquitous platforms, SEUS 2007, hung on Santorini Island, Greece, in may possibly 2007 along with ISORC 2007, the tenth IEEE overseas Symposium on Object/component/service-oriented Real-time allotted Computing.
The 31 revised complete papers and four revised brief papers provided have been conscientiously reviewed and chosen from 102 submissions. The papers are geared up in topical sections on ubiquitous computing frameworks, safety-critical structures, validation of embedded and ubiquitous platforms, ubiquitous computing purposes, scheduling and non practical homes, self-organization and reconfiguration, provider discovery and improvement platform, instant networks, middleware architectures and virtualization, and atmosphere interaction.
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Additional info for Software Technologies for Embedded and Ubiquitous Systems: 5th IFIP WG 10.2 International Workshop, SEUS 2007, Santorini Island, Greece, May 7-8, 2007, ... Applications, incl. Internet/Web, and HCI)
Sample text
For a context aggregation rule, decomposition algorithm generates a sub task rule tree with the result rule of the duplicated random value. Each result rule of a sub task is represented as RDF triple, SubTaskRule(ip, random value). And after finishing above sequences, sub task rules are inserted into sub task rule table, and a subscription tree is generated for a context aggregator. A Task Decomposition Scheme for Context Aggregation in Personal Smart Space 25 Fig. 3. Flowchart of task decomposition algorithm Fig 3 shows the algorithm for task decomposition.
Section 2 explains the motivation of the proposed scheme. Section 3 introduces the requirements for context aggregation in personal smart space. We discuss design consideration and describe the context management architecture for a personal smart space in Section 4. The implementation details of the proposed scheme are described in Section 5. Section 6 shows the performance analysis of our approach. The related work is presented in Section 7. Finally, conclusion follows in Section 8. 2 Motivation We develop an example scenario for an over running status.
Fig 1 displays the hierarchical context ontology designed in this paper. Fig. 1. Hierarchical Context Information Every type of ontology is inherited from the context class for creation. The common ontology comprises Person, CompEntity, Location and Environment and Activity Class inherited from the highest Context class, as well as Service, Device and Network inherited from CompEntity. Context may include multiple attributes to describe the corresponding situation, as well as other situations as properties.