A New Compressed Sensing Image Transmission Scheme in Wireless Sensor Networks

Article Preview

Abstract:

If the nodes in a multi-media data transmission system can not meet low power in traditional wireless sensor networks (WSNs), it will greatly reduce the network life cycle. On the basis of quantized compressive sensing (QCS), this paper presents a communication model which can analyze the impact of uniform quantization coding and Hoffman coding on image’s transmission and reconstruction. According to the prior probability of image measurement, this paper proposes a novel scheme for non-uniform quantizer.The simulation results show that the multi-media data transmission system can obtain images effectively and reduce the distortion by using the proposed scheme.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 945-949)

Pages:

1968-1972

Citation:

Online since:

June 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Ramalingam, K.C., Subramanian, Venkatachalam, Uluagac, A. Selcuk, Beyah, Raheem, SIMAGE: Secure and Link-Quality Cognizant Image Distribution for wireless sensor networks, Global Communications Conference (GLOBECOM), pp.616-621, (2012).

DOI: 10.1109/glocom.2012.6503181

Google Scholar

[2] Justin Romberg, Sensing by Random Convolution, 2nd IEEE Int. Work. Comp. Adv. Multi- Sensor Adap. Proc., pp.137-140, (2007).

Google Scholar

[3] D Donoho, Compressed sensing, IEEE Trans. on Information Theory, vol. 52(4), pp.1289-1306, (2006).

DOI: 10.1109/tit.2006.871582

Google Scholar

[4] E Candès, Compressive sampling, Proceedings of the International Congress of Mathematicians, pp.1433-1452, March. (2006).

DOI: 10.4171/022-3/69

Google Scholar

[5] W. Dai and O. Milenkovic, Subspace pursuit for compressive sensing signal reconstruction, IEEE Trans. Inform. Theory, 55(5), p.2230–2249, (2009).

DOI: 10.1109/tit.2009.2016006

Google Scholar

[6] Wei Dai, Olgica Milenkovic, Information Theoretical and Algorithmic Approaches to Quantized Compressive Sensing, IEEE Trans. Inform. vol. 59(7), pp.1857-1866, July. (2011).

DOI: 10.1109/tcomm.2011.051711.100204

Google Scholar

[7] C.S. Gunturk, A. Powell, R. Saab, O. Yilmaz, Sobolev Duals for Random Frames and Quantization of Compressed Sensing Measurements, Foundations of Computational Mathematics, vol. 13(1), pp.1-36, February (2013).

DOI: 10.1007/s10208-012-9140-x

Google Scholar