Distributed wavelet transofrm for sensor networks with irregular grids
Wavelet-based distributed data processing holds much promise for sensor networks. However, irregular sensor node placement precludes the direct application of standard wavelet techniques. In this paper, we develop a new distributed wavelet transform based on lifting that takes into account irregular sampling and provides a piecewise-planar multiresolution representation of the sensed data. We develop the transform theory; outline how to implement it in a multi-hop, wireless sensor network; and illustrate with several simulations. The new transform performs on par with conventional wavelet methods for comparison on a regular grid of sensor nodes.