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  • Research Article
  • Open Access

Energy-Constrained Optimal Quantization for Wireless Sensor Networks

EURASIP Journal on Advances in Signal Processing20072008:462930

https://doi.org/10.1155/2008/462930

  • Received: 28 May 2007
  • Accepted: 2 November 2007
  • Published:

Abstract

As low power, low cost, and longevity of transceivers are major requirements in wireless sensor networks, optimizing their design under energy constraints is of paramount importance. To this end, we develop quantizers under strict energy constraints to effect optimal reconstruction at the fusion center. Propagation, modulation, as well as transmitter and receiver structures are jointly accounted for using a binary symmetric channel model. We first optimize quantization for reconstructing a single sensor's measurement, and deriving the optimal number of quantization levels as well as the optimal energy allocation across bits. The constraints take into account not only the transmission energy but also the energy consumed by the transceiver's circuitry. Furthermore, we consider multiple sensors collaborating to estimate a deterministic parameter in noise. Similarly, optimum energy allocation and optimum number of quantization bits are derived and tested with simulated examples. Finally, we study the effect of channel coding on the reconstruction performance under strict energy constraints and jointly optimize the number of quantization levels as well as the number of channel uses.

Keywords

  • Wireless Sensor Network
  • Quantization Level
  • Fusion Center
  • Transmission Energy
  • Multiple Sensor

Publisher note

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Authors’ Affiliations

(1)
Qualcomm Inc., San Diego, CA 92121, USA
(2)
Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455, USA

Copyright

© X. Luo and G. B. Giannakis. 2008

This article is published under license to BioMed Central Ltd. 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.

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