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This article is part of the series Distributed Signal Processing Techniques for Wireless Sensor Networks.

Open Access Open Badges Research Article

Ring-Based Optimal-Level Distributed Wavelet Transform with Arbitrary Filter Length for Wireless Sensor Networks

Siwang Zhou1, Yaping Lin1* and Yonghe Liu2

Author Affiliations

1 School of Software, Hunan University, Changsha 410082, China

2 Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX 76019, USA

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EURASIP Journal on Advances in Signal Processing 2008, 2008:396126  doi:10.1155/2008/396126

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2008/1/396126

Received:1 May 2007
Revisions received:31 August 2007
Accepted:8 November 2007
Published:9 December 2007

© 2008 The Author(s).

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We propose an optimal-level distributed transform for wavelet-based spatiotemporal data compression in wireless sensor networks. Although distributed wavelet processing can efficiently decrease the amount of sensory data, it introduces additional communication overhead as the sensory data needs to be exchanged in order to calculate the wavelet coefficients. This tradeoff is explored in this paper with the optimal transforming level of wavelet transform. By employing a ring topology, our scheme is capable of supporting a broad scope of wavelets rather than specific ones, and the "border effect" generally encountered by wavelet-based schemes is also eliminated naturally. Furthermore, the scheme can simultaneously explore the spatial and temporal correlations among the sensory data. For data compression in wireless sensor networks, in addition to minimizing energy and consumption, it is also important to consider the delay and the quality of reconstructed sensory data, which is measured by the ratio of signal to noise (). We capture this with energydelay/ metric and using it to evaluate the performance of the proposed scheme. Theoretically and experimentally, we conclude that the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy and delay cost while still preserving high compared to other schemes.

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