Abstract
With the limited resources, it is difficult for wireless sensor network to achieve a long time, continuous, high-speed acquisition of multimedia information and a real-time, reliable transmission of high-volume sampling data. This article proposed the novel data acquisition strategy based on compressed sensing theory, which can perfectly achieve the long time, real-time, reliable transmission of high-volume multimedia data in wireless sensor network. Reasonable experiments were designed to verify the effectiveness of the algorithms, and the experiment results show that: the proposed multimedia signal acquisition strategy is reasonable, practicable, and more suitable for the wireless multimedia sensor networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Grimberg, R., Savin, A.: Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors. Sensors and Actuators 81(3), 248–250 (2000)
Odeberg, H.: Distance measure for sensor opinions. Measurement Science and Technology 4(8), 808–815 (1993)
Donoho, D.: Compressed sensing. IEEE Trans. Information Theory 52, 1289–1306 (2006)
Donoho, D., Tsaig, Y.: Extensions of compressed sensing. Signal Processing 86, 533–548 (2006)
Candes, E., Romberg, J., Tao, T.: Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Information Theory 52(4), 489–509 (2006)
Candes, E., Tao, T.: Decoding by linear programming. IEEE Trans. Information Theory 51, 4203–4215 (2005)
Gastpar, M., Vetterli, M.: Power, spatio-temporal bandwidth, and distortion in large sensor networks. IEEE Journal Select. Areas Communication 23, 745–754 (2005)
Candes, E., Tao, T.: Near optimal signal recovery from random projections: universal encoding strategies. IEEE Trans. Information Theory 52, 5406–5425 (2006)
Boyle, F., Haupt, J., Fudge, G.: Detecting signal structure from randomly sampled data. In: Proceeding of 2007 IEEE Workshop on Statistical Signal Processing, Madison, Wisconsin, USA, pp. 326–330 (2007)
Zainul, C., Young, H.K., Sadaf, Z.: Energy efficient sampling for event detection in wireless sensor network. In: 2009 International Symposium on Low Power Electronics and Design, San Francisco, California, USA, pp. 587–593 (2009)
Davenport, M., Duarte, M., Wakin, M.: The smashed filter for compressive classification and target recognition. In: Proceeding of 2007 Computational Imaging V at SPIE Electronic Imaging, San Jose, California, USA, pp. 326–330 (2007)
He, T., Stankovic, J.A., Marley, M.: Feedback control-based dynamic resource management in distributed real-time systems. Journal of Systems and Software 80, 997–1004 (2007)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, X. (2012). Research on Multimedia Signal Acquisition Strategy Based on Compressed Sensing. In: Wang, Y., Zhang, X. (eds) Internet of Things. Communications in Computer and Information Science, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32427-7_49
Download citation
DOI: https://doi.org/10.1007/978-3-642-32427-7_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-32426-0
Online ISBN: 978-3-642-32427-7
eBook Packages: Computer ScienceComputer Science (R0)