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Energy-Efficient Transmission of Wavelet-Based Images in Wireless Sensor Networks

EURASIP Journal on Image and Video Processing volume 2007, Article number: 047345 (2007) Cite this article

Abstract

We propose a self-adaptive image transmission scheme driven by energy efficiency considerations in order to be suitable for wireless sensor networks. It is based on wavelet image transform and semireliable transmission to achieve energy conservation. Wavelet image transform provides data decomposition in multiple levels of resolution, so the image can be divided into packets with different priorities. Semireliable transmission enables priority-based packet discarding by intermediate nodes according to their battery's state-of-charge. Such an image transmission approach provides a graceful tradeoff between the reconstructed images quality and the sensor nodes' lifetime. An analytical study in terms of dissipated energy is performed to compare the self-adaptive image transmission scheme to a fully reliable scheme. Since image processing is computationally intensive and operates on a large data set, the cost of the wavelet image transform is considered in the energy consumption analysis. Results show up to 80% reduction in the energy consumption achieved by our proposal compared to a nonenergy-aware one, with the guarantee for the image quality to be lower-bounded.

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References

  1. 1.

    Rahimi M, Baer R, Iroezi OI, et al.: Cyclops: in situ image sensing and interpretation in wireless sensor networks. Proceedings of the 3rd ACM International Conference on Embedded Networked Sensor Systems (SenSys '05), November 2005, San Diego, Calif, USA 192-204.

    Google Scholar 

  2. 2.

    Culurciello E, Andreou AG: CMOS image sensors for sensor networks. Analog Integrated Circuits and Signal Processing 2006,49(1):39-51. 10.1007/s10470-006-8737-x

    Article  Google Scholar 

  3. 3.

    Magli E, Mancin M, Merello L: Low-complexity video compression for wireless sensor networks. Proceedings of International Conference on Multimedia and Expo (ICME '03), July 2003, Baltimore, Md, USA 585-588.

    Google Scholar 

  4. 4.

    Wagner R, Nowak R, Baraniuk R: Distributed image compression for sensor networks using correspondence analysis and super-resolution. Proceedings of International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 1: 597-600.

    Google Scholar 

  5. 5.

    Tang C, Raghavendra CS: Compression techniques for wireless sensor networks. In Wireless Sensor Networks. Kluwer Academic, Boston, Mass, USA; 2004:207-231.

    Google Scholar 

  6. 6.

    Song B, Bursalioglu O, Roy-Chowdhury AK, Tuncel E: Towards a multi-terminal video compression algorithm using epipolar geometry. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '06), May 2006, Toulouse, France 2: 49-52.

    Google Scholar 

  7. 7.

    Wu H, Abouzeid AA: Energy efficient distributed image compression in resource-constrained multihop wireless networks. Computer Communications 2005,28(14):1658-1668. 10.1016/j.comcom.2005.02.018

    Article  Google Scholar 

  8. 8.

    Wu H, Abouzeid AA: Error resilient image transport in wireless sensor networks. Computer Networks 2006,50(15):2873-2887. 10.1016/j.comnet.2005.09.039

    Article  MATH  Google Scholar 

  9. 9.

    Mallat S: A Wavelet Tour of Signal Processing. 2nd edition. Academic Press, New York, NY, USA; 1999.

    Google Scholar 

  10. 10.

    Antonini M, Barlaud M, Mathieu P, Daubechies I: Image coding using wavelet transform. IEEE Transactions of Image Processing 1992,1(2):205-220. 10.1109/83.136597

    Article  Google Scholar 

  11. 11.

    Le Gall D, Tabatabai A: Sub-band coding of digital images using symmetric short kernel filters and arithmetic coding techniques. Proceedings IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '88), April 1988, New York, NY, USA 761-764.

    Google Scholar 

  12. 12.

    Calderbank AR, Daubechies I, Sweldens W, Yeo B-L: Wavelet transforms that map integers to integers. Applied and Computational Harmonic Analysis 1998,5(3):332-369. 10.1006/acha.1997.0238

    MathSciNet  Article  MATH  Google Scholar 

  13. 13.

    Kimura N, Latifi S: A survey on data compression in wireless sensor networks. Proceedings of International Conference on Information Technology: Coding and Computing (ITCC '05), April 2005, Las Vegas, Nev, USA 2: 8-13.

    Google Scholar 

  14. 14.

    Lee D-G, Dey S: Adaptive and energy efficient wavelet image compression for mobile multimedia data services. Proceedings of IEEE International Conference on Communications (ICC '02), April-May 2002, New York, NY, USA 4: 2484-2490.

    Article  Google Scholar 

  15. 15.

    Crossbow Technology http://www.xbow.com

  16. 16.

    Atmel Corporation. http://www.atmel.com

  17. 17.

    Chipcon Products. http://www.chipcon.com

  18. 18.

    ATmega128(L) summary. Datasheet, Atmel Corporation, http://www.atmel.com

  19. 19.

    Shnayder V, Hempstead M, Chen B-R, Allen GW, Welsh M: Simulating the power consumption of large-scale sensor network applications. Proceedings of the 2nd ACM International Conference on Embedded Networked Sensor Systems (SenSys '04), November 2004, Baltimore, Md, USA 188-200.

    Google Scholar 

  20. 20.

    Polastre J, Hill J, Culler D: Versatile low power media access for wireless sensor networks. Proceedings of the 2nd ACM International Conference on Embedded Networked Sensor Systems (SenSys '04), November 2004, Baltimore, Md, USA 95-107.

    Google Scholar 

  21. 21.

    Marhur G, Desnoyers P, Ganesan D, Shenoy P: Ultra-low power data storage for sensor networks. Proceedings of IEEE/ACM Conference on Information Processing in Sensor Networks (IPSN '06), April 2006, Nashville, Tenn, USA 374-381.

    Google Scholar 

  22. 22.

    UC Berkeley : TinyOS: an operating system for networked sensors. http://www.tinyos.net

  23. 23.

    Salomon D: Data Compression: The Complete Reference. 3rd edition. Springer, New York, NY, USA; 2004.

    Google Scholar 

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Affiliations

  1. Centre de Recherche en Automatique de Nancy (CRAN UMR 7039), Nancy-Université, CNRS, Faculté des Sciences et Techniques, BP 239, Vandoeuvre lès Nancy Cedex, 54506, France

    Vincent Lecuire, Cristian Duran-Faundez & Nicolas Krommenacker

Authors
  1. Vincent Lecuire
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  2. Cristian Duran-Faundez
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  3. Nicolas Krommenacker
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Correspondence to Vincent Lecuire.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lecuire, V., Duran-Faundez, C. & Krommenacker, N. Energy-Efficient Transmission of Wavelet-Based Images in Wireless Sensor Networks. J Image Video Proc 2007, 047345 (2007). https://doi.org/10.1155/2007/47345

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Keywords

  • Energy Consumption
  • Image Quality
  • Energy Efficiency
  • Sensor Node
  • Wireless Sensor Network