Some computer science issues in ubiquitous computing
Communications of the ACM - Special issue on computer augmented environments: back to the real world
Intelligent Agents Meet Semantic Web in a Smart Meeting Room
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Intelligent Agents Meet the Semantic Web in Smart Spaces
IEEE Internet Computing
Ubiquitous Device Collaboration Infrastructure: Celadon
SEUS-WCCIA '06 Proceedings of the The Fourth IEEE Workshop on Software Technologies for Future Embedded and Ubiquitous Systems, and the Second International Workshop on Collaborative Computing, Integration, and Assurance (SEUS-WCCIA'06)
On the role of trust in collaborative Web search
Artificial Intelligence Review
Ontology-Based Context Modeling and Reasoning for U-HealthCare
IEICE - Transactions on Information and Systems
A Personalized Device Recommender System in Ubiquitous Environments
INCOS '09 Proceedings of the 2009 International Conference on Intelligent Networking and Collaborative Systems
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In a ubiquitous computing environment, service composition and collaboration among heterogeneous resources are required, thus, an infrastructure that supports these requirements is an essential factor in seamless service delivery. In this environment, users hope to get a variety of customized services by using only an individual mobile device. But the resource of the mobile device has limitations such as tiny display screens, limited input, less powerful processors, and limited storage. Moreover each user situation is different and user preferences are also various. Therefore it is one of new issues to provide a customized service for a user through resource collaboration based on various user preference and situation. To solve this issue, this paper proposes a resource collaboration system which infers customized resources for composing a user required service and collaborative with selected resources. For our collaboration system, this paper proposes the method to infer resources based on the context and user preferences including dynamic change of the preference. This paper also shows a reasonable execution environment for the proposed system through the performance evaluation in server-client and peer-to-peer environments.