Energy-Effective Communication Based on Compressed Sensing

Wang Yin-yin

doi:doi:10.11648/j.ajnc.20160506.11

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Energy-Effective Communication Based on Compressed Sensing

Wang Yin-yin

Published in American Journal of Networks and Communications (Volume 5, Issue 6)

Received: 5 November 2016 Accepted: 22 November 2016 Published: 8 December 2016

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Abstract

In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.

Published in	American Journal of Networks and Communications (Volume 5, Issue 6)
DOI	10.11648/j.ajnc.20160506.11
Page(s)	121-127
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Collaborative Routing, Compressed Sensing, Communication, WSN

References

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[3]	H. Yang, K. Tang, et al, “An adaptable CS-based transmission scheme validated on the real-world system,” IEEE INFOCOM 2016
[4]	Z. Charbiwala, S. Chakraborty, S. Zahedi, Y. Kim, M. B. Srivastava, T. He and C. Bisdikian. Compressive Oversampling for Robust Data Transmission in Sensor Networks. In INFOCOM, 2010.
[5]	H. Yang, et al, “A Practical Information Coverage Approach in Wireless Sensor Network,” Information Processing Letters, vol. 115, no. 1, pp. 6-10, 2015.
[6]	C. Feng, W. S. A. Au, et al. Compressive sensing based positioning using RSS of WLAN access points. In INFOCOM, 2010.
[7]	PS. C. Thejaswi, T. Tran, and J. Zhang. When compressive sampling meets multicast: Outage analysis and subblock network coding. In INFOCOM, 2011.
[8]	A. Wani, etc. Compressive Sampling for Energy Efficient and Loss Resilient Camera Sensor Networks. In INFOCOM, 2011.
[9]	Z. Li, Y. Zhu, H. Zhu, and M. Li. Compressive Sensing Approach to Urban Traffic Sensing. In ICDCS, 2014.
[10]	H. Yang, et al, “Distributed Compressed Sensing in Wireless Local Area Networks,” International Journal of Communication Systems, vol. 22, no. 11, pp. 2723-2743, 2014.
[11]	H. Yang, L. Huang, H. Xu, “Distributed Compressed Sensing in Vehicular Ad-hoc Network,” Ad hoc & Sensor Wireless Networks, vol. 25, no. 1-2, pp. 121-145, 2015.
[12]	E. J. Cand`es, J. Romberg, and T. Tao, “Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information”, IEEE Trans. Inform. Theory, vol. 52, no. 2, pp. 489-509, 2006.
[13]	David L. Donoho, “Compressed sensing”, IEEE Trans. on Inform. Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[14]	E. Candes, “The Restricted Isometry Property and its Implications for Compressed Sensing”, Comptes Rendus Mathematique, vol. 346, no. 9. pp. 589-592, 2008.
[15]	M. F. Duarte, and S. Sarvotham, and D. Baron, and M. B. Wakin, and R. G. Baraniuk, “Distributed Compressed Sensing of Jointly Sparse Signals”, Asilomar Conf. Signals, Sys., Comput, pp. 1537-1541, 2005.

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Wang Yin-yin. (2016). Energy-Effective Communication Based on Compressed Sensing. American Journal of Networks and Communications, 5(6), 121-127. https://doi.org/10.11648/j.ajnc.20160506.11

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ACS Style

Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am. J. Netw. Commun. 2016, 5(6), 121-127. doi: 10.11648/j.ajnc.20160506.11

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AMA Style

Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am J Netw Commun. 2016;5(6):121-127. doi: 10.11648/j.ajnc.20160506.11

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@article{10.11648/j.ajnc.20160506.11,
  author = {Wang Yin-yin},
  title = {Energy-Effective Communication Based on Compressed Sensing},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {6},
  pages = {121-127},
  doi = {10.11648/j.ajnc.20160506.11},
  url = {https://doi.org/10.11648/j.ajnc.20160506.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.11},
  abstract = {In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.},
 year = {2016}
}

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TY  - JOUR
T1  - Energy-Effective Communication Based on Compressed Sensing
AU  - Wang Yin-yin
Y1  - 2016/12/08
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160506.11
DO  - 10.11648/j.ajnc.20160506.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 121
EP  - 127
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160506.11
AB  - In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.
VL  - 5
IS  - 6
ER  -

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Author Information

Wang Yin-yin

The First People’s Hospital of Yancheng, Yancheng, P. R. China

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APA Style

Wang Yin-yin. (2016). Energy-Effective Communication Based on Compressed Sensing. American Journal of Networks and Communications, 5(6), 121-127. https://doi.org/10.11648/j.ajnc.20160506.11

Copy | Download

ACS Style

Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am. J. Netw. Commun. 2016, 5(6), 121-127. doi: 10.11648/j.ajnc.20160506.11

Copy | Download

AMA Style

Wang Yin-yin. Energy-Effective Communication Based on Compressed Sensing. Am J Netw Commun. 2016;5(6):121-127. doi: 10.11648/j.ajnc.20160506.11

Copy | Download

@article{10.11648/j.ajnc.20160506.11,
  author = {Wang Yin-yin},
  title = {Energy-Effective Communication Based on Compressed Sensing},
  journal = {American Journal of Networks and Communications},
  volume = {5},
  number = {6},
  pages = {121-127},
  doi = {10.11648/j.ajnc.20160506.11},
  url = {https://doi.org/10.11648/j.ajnc.20160506.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnc.20160506.11},
  abstract = {In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Energy-Effective Communication Based on Compressed Sensing
AU  - Wang Yin-yin
Y1  - 2016/12/08
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnc.20160506.11
DO  - 10.11648/j.ajnc.20160506.11
T2  - American Journal of Networks and Communications
JF  - American Journal of Networks and Communications
JO  - American Journal of Networks and Communications
SP  - 121
EP  - 127
PB  - Science Publishing Group
SN  - 2326-8964
UR  - https://doi.org/10.11648/j.ajnc.20160506.11
AB  - In order to improve energy-effect, Compressed Sensing has been employed gradually in the process of collaborative communication. For practical applications, localized features of local area are further considered in this technology, which is called classification CS. However, collaborative methods in current literatures are not so suitable in this scene that the advantages of CS could not be benefit. In this paper, a novel collaborative communication mechanism based on classification CS is proposed for actual environments. An effective collaborative transmission mode based on classification is presented, in which energy cost reduce effectively in the process of transmission and the reconstructed signals could reach at least the theoretical low bound to avoid redundant samplings. In experiments, our mechanism has been proved valuable and feasible in realistic applications.
VL  - 5
IS  - 6
ER  -

Copy | Download