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Energy-efficient data representation and routing for wireless sensor networks based on a distributed wavelet compression algorithm
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Source Information Processing In Sensor Networks archive
Proceedings of the 5th international conference on Information processing in sensor networks table of contents
Nashville, Tennessee, USA
POSTER SESSION: Main track table of contents
Pages: 309 - 316  
Year of Publication: 2006
ISBN:1-59593-334-4
Authors
Alexandre Ciancio  University of Southern California, Los Angeles, California
Sundeep Pattem  University of Southern California, Los Angeles, California
Antonio Ortega  University of Southern California, Los Angeles, California
Bhaskar Krishnamachari  University of Southern California, Los Angeles, California
Sponsor
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

We address the problem of energy consumption reduction for wireless sensor networks, where each of the sensors has limited power and acquires data that should be transmitted to a central node. The final goal is to have a reconstructed version of the data measurements at the central node, with the sensors spending as little energy as possible, for a given data reconstruction accuracy. In our scenario, sensors in the network have a choice of different coding schemes to achieve varying levels of compression. The compression algorithms considered are based on the lifting factorization of the wavelet transform, and exploit the natural data flow in the network to aggregate data by computing partial wavelet coefficients that are refined as data flows towards the central node. The proposed algorithm operates by first selecting a routing strategy through the network. Then, for each route, an optimal combination of data representation algorithms i.e. assignment at each node, is selected. A simple heuristic is used to determine the data representation technique to use once path merges are taken into consideration. We demonstrate that by optimizing the coding algorithm selection the overall energy consumption can be significantly reduced when compared to the case when data is just quantized and forwarded to the central node. Moreover, the proposed algorithm provides a tool to compare different routing techniques and identify those that are most efficient overall, for given node locations. We evaluate the algorithm using both a second-order autoregressive (AR) model and empirical data from a real wireless sensor network deployment.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
C. Chong and S. P. Kumar. Sensor networks: Evolution, opportunities, and challenges. Proceedings of the IEEE, 91(8):1247--1256, August 2003.
 
2
A. Ciancio and A. Ortega. A distributed wavelet compression algorithm for wireless sensor networks using lifting. In International Conference on Acoustics, Speech and Signal Processing - ICASSP04, Montreal, Canada, May 2004.
 
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A. Ciancio and A. Ortega. A distributed wavelet compression algorithm for wireless multihop sensor networks using lifting. In International Conference on Acoustics, Speech and Signal Processing - ICASSP05, Philadelphia, USA, March 2005.
 
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A. Ciancio and A. Ortega. A dynamic programming approach to distortion-energy optimization for distributed wavelet compression with applications to data gathering in wireless sensor networks. International Conference on Acoustics, Speech and Signal Processing - ICASSP06, 2006.
 
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Collaborative Colleagues:
Alexandre Ciancio: colleagues
Sundeep Pattem: colleagues
Antonio Ortega: colleagues
Bhaskar Krishnamachari: colleagues