Research Article
An autonomic sensing framework for body sensor networks
@INPROCEEDINGS{10.4108/bodynets.2007.1009, author={Surapa Thiemjarus and Guang-Zhong Yang}, title={An autonomic sensing framework for body sensor networks}, proceedings={2nd International ICST Conference on Body Area Networks}, publisher={ICST}, proceedings_a={BODYNETS}, year={2007}, month={6}, keywords={multi-objective feature selection factor graphs pervasive monitoring context sensing activity recognition}, doi={10.4108/bodynets.2007.1009} }- Surapa Thiemjarus
Guang-Zhong Yang
Year: 2007
An autonomic sensing framework for body sensor networks
BODYNETS
ICST
DOI: 10.4108/bodynets.2007.1009
Abstract
This paper presents an autonomic sensing framework for distributed inferencing, which consists of several self-contained machine learning techniques. A multi-objective Bayesian framework for feature selection is used for learning the relationship of the variables. To cater for fault tolerance and minimal resource utilisation, feature redundancy and network complexity measures have been introduced. We demonstrate how factor graphs and the sum-product algorithm can be used for model representation and inferencing. We will also show how they can be used to facilitate the mapping of model architecture onto the physical sensor networks.
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