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Machine learning for resource management in smart environments
Author(s):
Affiliation:
Information and Communication Technologies; National Research Council Canada; Information and Communication Technologies
Language:
English
Type:
Conference publication
Conference:
6th IEEE International Conference on Digital Ecosystem Technologies, 18-20 June 2012, Campione d’Italia, Italy
Proceedings
Title:
2012 6th IEEE International Conference on Digital Ecosystem Technologies (DEST)
Date:
2012
NPArC #:
20494938
Keywords:
machine learning; resource management; energy savings; ambient assisted living; smart environment; semantic web
Program(s):
Intelligent Internet Applications; Applications intelligentes pour l'internet
Group(s):
Internet Logic; Logique Internet
Abstract:
Efficient 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.
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