Adaptive distributed transforms for irregularly sampled Wireless Sensor Networks

Authors:
Godwin Shen;Sunil Kumar Narang;Antonio Ortega
Affiliations:
Signal and Image Processing Institute, University of Southern California, USA;Signal and Image Processing Institute, University of Southern California, USA;Signal and Image Processing Institute, University of Southern California, USA
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

We develop energy-efficient, adaptive distributed transforms for data gathering in wireless sensor networks. In particular, we consider a class of unidirectional transforms that are computed as data is forwarded to the sink along a given routing tree and develop a tree-based Karhunen-Loàve Transform (KLT) that is optimal in that it achieves maximum data de-correlation among this class of transforms. As an alternative to this KLT (which incurs communication overhead in order to learn second order data statistics), we propose a backward adaptive filter optimization algorithm for distributed wavelet transforms that i) achieves near optimal performance and ii) has no communication overhead in learning statistics.