An autonomic sensing framework for body sensor networks

Authors:
Surapa Thiemjarus;Guang-Zhong Yang
Affiliations:
Imperial College, London, England;Imperial College, London, England
Venue:
Proceedings of the ICST 2nd international conference on Body area networks
Year:
2007

Citing 6
Cited 3

The Vision of Autonomic Computing

Computer
Robust probabilistic inference in distributed systems

UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Correctness of Belief Propagation in Gaussian Graphical Models of Arbitrary Topology

Neural Computation
Loopy belief propagation for approximate inference: an empirical study

UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Loopy belief propagation as a basis for communication in sensor networks

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Factor graphs and the sum-product algorithm

IEEE Transactions on Information Theory

Optimal Feature Selection Applied to Multispectral Fluorescence Imaging

MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Recognition of user activity sequences using distributed event detection

EuroSSC'07 Proceedings of the 2nd European conference on Smart sensing and context
An eye-hand data fusion framework for pervasive sensing of surgical activities

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

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.