Machine learning for resource management in smart environments

DOIResolve DOI: http://doi.org/10.1109/DEST.2012.6227910
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TypeArticle
Proceedings title2012 6th IEEE International Conference on Digital Ecosystem Technologies (DEST)
Conference6th IEEE International Conference on Digital Ecosystem Technologies, 18-20 June 2012, Campione d’Italia, Italy
Subjectmachine learning; resource management; energy savings; ambient assisted living; smart environment; semantic web
AbstractEfficient resource and energy management is a key research and business area in todays IT markets. Cyber-physical ecosystems, like smart homes (SHs) and smart Environments (SEs) get interconnected, the efficient allocation of resources will become essential. Machine Learning and Semantic Web techniques for improving resource allocation and management are the focus of our research. They allow machines to process information on all levels, inferring expressive knowledge from raw data, in particular resource predictions from usage patterns. Our aim is to devise a novel approach for a machine learning (ML) and resource Management (RM) framework in SEs. It combines ML and SemanticWeb techniques and integrates user interaction. The main objective is to enable the creation of platforms that decrease the overall resource consumption by learning and predicting various usage patterns, and furthermore making decisions based on user-feedback. For this purpose, we evaluate recent research and applications, elicit framework requirements, and present a framework architecture. The approach and components are assessed and a prototype implementation is described.
Publication date
LanguageEnglish
AffiliationInformation and Communication Technologies; National Research Council Canada; Information and Communication Technologies
Peer reviewedYes
NPARC number20494938
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Record identifier36c5d9b5-3e99-476d-abdc-c2edcf52c132
Record created2012-08-16
Record modified2016-05-09
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