- Research Article
- Open Access
Energy-Efficient Transmission of Wavelet-Based Images in Wireless Sensor Networks
EURASIP Journal on Image and Video Processing volume 2007, Article number: 047345 (2007)
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.
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.
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
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.
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.
Tang C, Raghavendra CS: Compression techniques for wireless sensor networks. In Wireless Sensor Networks. Kluwer Academic, Boston, Mass, USA; 2004:207-231.
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.
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
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
Mallat S: A Wavelet Tour of Signal Processing. 2nd edition. Academic Press, New York, NY, USA; 1999.
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
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.
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
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.
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.
Crossbow Technology http://www.xbow.com
Atmel Corporation. http://www.atmel.com
Chipcon Products. http://www.chipcon.com
ATmega128(L) summary. Datasheet, Atmel Corporation, http://www.atmel.com
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.
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.
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.
UC Berkeley : TinyOS: an operating system for networked sensors. http://www.tinyos.net
Salomon D: Data Compression: The Complete Reference. 3rd edition. Springer, New York, NY, USA; 2004.
About this article
Cite this article
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
- Energy Consumption
- Image Quality
- Energy Efficiency
- Sensor Node
- Wireless Sensor Network