Adaptive distributed transforms for irregularly sampled Wireless Sensor Networks

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-Lo`eve 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.

• Type: Conference
• Year: 2009
• Where: ICASSP
• Authors: Godwin Shen, Sunil K. Narang, Antonio Ortega
• Keywords: Adaptive Distributed Transforms, Communication Overhead, ICASSP 2009, Signal Processing, Unidirectional Transforms