Skip to main content

Advertisement

Log in

A Novel Interdependent Source-Channel Coding Technique for Enhanced Energy Efficiency in Communication over Wireless Sensor Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Reliable energy efficient information transmission is the primary design objective of a Wireless Sensor Network (WSN), considering its unique energy and resource constraints. Energy efficiency and bit error rate (BER) performance are the basic criteria to be taken into account while designing an optimal error correction scheme for WSNs. In this paper, a novel energy efficient error control scheme is proposed which minimizes the energy overheads of a typical error control scheme such as additional bits’ transmit energy and encoding/decoding energy, while achieving a better BER performance compared to the standard schemes. The redundant bits’ transmit energy is saved by incorporating compression and coding energy is minimized by employing simpler operations compared to other schemes. Further,the proposed scheme is validated in the context of mica2 motes. The BER performance and energy consumption of the presented scheme are studied and compared with standard error control schemes,such as, Hamming (7, 4) and RS (31, 29). Simulation results demonstrate the efficacy of the proposed methodology yielding a coding gain (CG) of 4.093 dB with a parameter selection of {30, 7, 2, 5}, in AWGN channel at BER of \(10^{-5}\), as compared to CG of 0.561 dB and 1.485 dB obtained using Hamming (7, 4) and RS (31, 29), respectively. Further, the standard codes above have a redundancy of 75% and 6.9% respectively while the proposed code with the above parameters achieves a compression of 23.81%. Quantification of energy consumption corresponding to each of the above schemes is also provided to prove the energy efficiency of the proposed technique.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Notes

  1. Wireless Measurement System Mica2 Data Sheet, Document Part Number: 6020-0042-04.

  2. AVRORA: The AVR simulation and Analysis Framework, Available at http://compilers.cs.ucla.edu/avrora/.

References

  1. Dargie, W., & Poellabauer, C. (2010). Fundamentals of wireless sensor networks: Theory and practice. Hoboken: Wiley.

    Book  Google Scholar 

  2. Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.

    Article  Google Scholar 

  3. Yick, J., Mukherjee, B., & Ghosal, D. (2008). Wireless sensor network survey. Computer Networks, 52(12), 2292–2330.

    Article  Google Scholar 

  4. Nithya, V., Ramachandran, B., & Bhaskar, V. (2014). Energy efficient coded communication for ieee 802.15. 4 compliant wireless sensor networks. Wireless Personal Communications, 77(1), 675–690.

    Article  Google Scholar 

  5. Chouhan, S., Bose, R., & Balakrishnan, M. (2009). A framework for energy-consumption-based design space exploration for wireless sensor nodes. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 28(7), 1017–1024.

    Article  Google Scholar 

  6. Abedi, A. (2011). Power-efficient-coded architecture for distributed wireless sensing. IET Wireless Sensor Systems, 1(3), 129–136.

    Article  Google Scholar 

  7. Sankarasubramaniam, Y., Akyildiz, I. F., & McLaughlin, S. (2003). Energy efficiency based packet size optimization in wireless sensor networks. In Sensor network protocols and applications, 2003 IEEE international workshop on, IEEE (pp. 1–8).

  8. Li, L., Maunder, R. G., Al-Hashimi, B. M., & Hanzo, L. (2013). A low-complexity turbo decoder architecture for energy-efficient wireless sensor networks. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 21(1), 14–22.

    Article  Google Scholar 

  9. Yitbarek, YH., Yu, K., Åkerberg, J., Gidlund, M., & Björkman, M. (2014). Implementation and evaluation of error control schemes in industrial wireless sensor networks. In Industrial Technology (ICIT), 2014 IEEE International Conference on, IEEE, (pp. 730–735).

  10. Xiong, Z., Liveris, A. D., & Cheng, S. (2004). Distributed source coding for sensor networks. IEEE Signal Processing Magazine, 21(5), 80–94.

    Article  Google Scholar 

  11. Deligiannis, N., Zimos, E., Ofrim, D. M., Andreopoulos, Y., & Munteanu, A. (2015). Distributed joint source-channel coding with copula-function-based correlation modeling for wireless sensors measuring temperature. IEEE Sensors Journal, 15(8), 4496–4507.

    Article  Google Scholar 

  12. Zhao, Y., & Garcia-Frias, J. (2006). Turbo compression/joint source-channel coding of correlated binary sources with hidden markov correlation. Signal Processing, 86(11), 3115–3122.

    Article  MATH  Google Scholar 

  13. Garcia-Frias, J., Zhao, Y., & Zhong, W. (2007). Turbo-like codes for transmission of correlated sources over noisy channels. IEEE Signal Processing Magazine, 24(5), 58–66.

    Article  Google Scholar 

  14. Akyildiz, I. F., & Vuran, M. C. (2011). Theory and design of digital communication systems. New York: Cambridge University Press.

    Google Scholar 

  15. Proakis, J. (2001). Digital communications. Boston: McGraw-Hill.

    MATH  Google Scholar 

  16. Ha, T. (2011). Theory and design of digital communication systems. New York: Cambridge University Press.

    Google Scholar 

  17. Wells, R. (1999). Applied coding and information theory for engineers. Upper Saddle River, N.J.: Prentice Hall.

    Google Scholar 

  18. Chouhan, S., Balakrishnan, M., & Bose, R. (2012). System-level design space exploration methodology for energy-efficient sensor node configurations: An experimental validation. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 31(4), 586–596.

    Article  Google Scholar 

  19. Lee, T. H. (2003). The design of CMOS radio-frequency integrated circuits. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  20. Titzer, BL., Lee, DK., & Palsberg, J. (2005). Avrora: Scalable sensor network simulation with precise timing. In Proceedings of the 4th international symposium on Information processing in sensor networks, IEEE Press, (p. 67).

  21. Chipcon AS. SmartRF® CC1000  Datasheet (rev. 2.2) 2004-04-22. Retrieved from http://www.astlab.hu/pdfs/cc1000.pdf.

  22. Dezfouli, B., Radi, M., Razak, S. A., Hwee-Pink, T., & Bakar, K. A. (2015). Modeling low-power wireless communications. Journal of Network and Computer Applications, 51, 102–126.

    Article  Google Scholar 

  23. Sklar, B. (1988). Digital communications. Englewood Cliffs, N.J.: Prentice-Hall.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. C. Resmi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Resmi, N.C., Chouhan, S. A Novel Interdependent Source-Channel Coding Technique for Enhanced Energy Efficiency in Communication over Wireless Sensor Networks. Wireless Pers Commun 96, 3727–3743 (2017). https://doi.org/10.1007/s11277-017-4068-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-017-4068-8

Keywords