Table of Contents
ISRN Sensor Networks
Volume 2013, Article ID 253257, 18 pages
Research Article

Robust Data Compression for Irregular Wireless Sensor Networks Using Logical Mapping

1School of Engineering and Computer Science, Washington State University Vancouver, Vancouver, WA 98686, USA
2Department of Computer Science, Portland State University, Portland, OR 97201, USA

Received 12 March 2013; Accepted 28 March 2013

Academic Editors: C.-Y. Chow, T.-Y. Juang, B. Tavli, and Y. Yu

Copyright © 2013 Thanh Dang et al. 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 RIDA, a novel robust information-driven data compression architecture for distributed wireless sensor networks. The key idea is to determine the data correlation among a group of sensors based on the data values to significantly improve compression performance rather than relying solely on spatial data correlation. A logical mapping approach assigns virtual indices to nodes based on the data content, which enables simple implementation of data transformation on resource-constrained nodes without any other information. We evaluate RIDA with both discrete cosine transform (DCT) and discrete wavelet transform (DWT) on publicly available real-world data sets. Our experiments show that 30% of energy and 80–95% of bandwidth can be saved for typical multihop data networks. Moreover, the original data can be retrieved after decompression with a low error of about 3%. In particular, for one state-of-the-art distributed data compression algorithm for sensor networks, we show that the compression ratio is doubled by using logical mapping while maintaining comparable mean square error. Furthermore, we also propose a mechanism to detect and classify missing or faulty nodes, showing accuracy and recall of 95% when half of the nodes in the network are missing or faulty.