Application of energy efficient soft-decision error control in wireless sensor networks

Abstract

Energy efficient reliable communication over an unpredictable wireless medium is a major challenge for resource constrained wireless sensor nodes in process/environment control application. In this paper, we propose a Soft Decision Decoding (SDD) based advanced Forward Error Correction (FEC) scheme for low power distributed sensor nodes. The proposed BCH (Bose-Chaudhuri-Hocquenghem) based adaptive Chase-2 decoding scheme offers attractive energy benefits as compared to Hard Decision Decoding (HDD). The reduced decoding complexity is obtained by limiting the codeword search space and fewer algebraic operations compared to standard Chase-2. A realistic environmental scenario incorporating path loss, Rayleigh fading and Additive White Gaussian Noise has been considered to investigate the performance of the proposed scheme. A detailed comparison is carried out with HDD of BCH codes using the widely known Crossbow MicaZ node parameters. The simulation results indicate that for low power WSN, the proposed SDD based adaptive scheme offers better tradeoff between energy and reliability than HDD schemes.

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

References

  1. 1.

    Rohani, B., & Homayounfar, K. Computation of instantaneous bit error probability from log-likelihood ratio. In RCS2008-150 (pp. 119–123).

  2. 2.

    Argon, C., & McLaughlin, S. W. (2004). An efficient Chase decoder for turbo product codes. In IEEE trans. on communications (Vol. 52(6)).

  3. 3.

    Chase, D. (1972). A class of algorithms for decoding block codes with channel measurement information. In IEEE trans. inform. theory (Vol. IT-18, pp. 170–182).

    Google Scholar 

  4. 4.

    Schmidt, D., Berning, M., & Wehn, N. (2009). Error correction in single-hop wireless sensor networks-a case study. In Proceedings of the IEEE conference on design, automation, and test in Europe (DATE’09) (pp. 1296–1301).

    Google Scholar 

  5. 5.

    Balakrishnan, G., Yang, M., Jiang, Y., & Kim, Y. (2007). Performance analysis of error control codes for wireless sensor networks. In Fourth international conference on information technology (ITNG’07).

    Google Scholar 

  6. 6.

    Sharma, G., Dholakia, A., & Hassan, A. A. (1996). Simulation of error trapping decoders on a fading channel. In Proc. 1996 IEEE vehicular technology conf. (pp. 1361–1365).

    Google Scholar 

  7. 7.

    Dubois-Ferriere, H., Estrin, D., & Vetterli, M. (2005). Packet combining in sensor networks. In Proceedings of the sensys’05.

    Google Scholar 

  8. 8.

    Wireless Medium Access Control (MAC) and Physical Layer (PHY) (2006). Specifications for low-rate wireless personal area networks (WPANs). IEEE Std. 802.15.4, September 2006.

  9. 9.

    Jamieson, K. (2008). The SoftPHY abstraction: from packets to symbols in wireless network design. Ph.D. thesis, Massachusetts Institute of Technology.

  10. 10.

    Vuran, M. C., & Akyildiz, I. F. (2009). Error control in wireless sensor networks: a cross layer analysis. IEEE/ACM Transactions on Networking, 17(4), 1186–1199.

    Article  Google Scholar 

  11. 11.

    MicaZ Datasheet. Crossbow Corp. [Online] http://www.xbow.com.

  12. 12.

    Maunder, R. G., Weddell, A. S., Merrett, G. V., Al-Hashimi, B. M., & Hanzo, L. (2008). Iterative decoding for redistributing energy consumption in wireless sensor networks. In IEEE international conference on computer communications and networks, St. Thomas, US Virgian Islands (pp. 1–6).

    Google Scholar 

  13. 13.

    Herzog, R., Hagenauer, J., & Schmidbauer, A. (1997). Soft-in/soft-out Hadamard despreader for iterative decoding in the IS-95(A) system. In Proceedings of the IEEE vehicular technology conference (Vol. 2, pp. 1219–1222).

    Google Scholar 

  14. 14.

    Morelos-Zaragoza, R. H. (2002). The art of error correcting codes. New York: Wiley.

    Google Scholar 

  15. 15.

    Pyndiah, R. (1997). Iterative decoding of product codes: block turbo codes. In International symposium on turbo codes, Brest, France.

    Google Scholar 

  16. 16.

    Yazdani, R., & Ardakani, M. (2007). Optimum linear LLR calculation for iterative decoding on fading channels. In Information theory, 2007. ISIT 2007. IEEE international symposium (pp. 61–65).

    Google Scholar 

  17. 17.

    Lin, S., & Costello, D. J. Jr. (2004). Error control coding. Fundamentals and applications (2nd ed.). New York: Prentice Hall.

    Google Scholar 

  18. 18.

    Rappaport, T. S. (2001). Wireless communications: principals and practice (2nd ed.). New York: Prentice Hall.

    Google Scholar 

  19. 19.

    Xia, F. (2008). QoS challenges and opportunities in wireless sensor/actuator networks. Sensors, 8(2), 1099–1110.

    Article  Google Scholar 

  20. 20.

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

    Google Scholar 

  21. 21.

    Kashani, Z. H., & Shiva, M. (2006). BCH coding and multi-hop communication in wireless sensor networks. In International conference on embedded and ubiquitous computing (IFIP’06).

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jasvinder Singh.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Singh, J., Pesch, D. Application of energy efficient soft-decision error control in wireless sensor networks. Telecommun Syst 52, 2573–2583 (2013). https://doi.org/10.1007/s11235-011-9588-z

Download citation

Keywords

  • Forward Error Correction (FEC)
  • BCH
  • HDD
  • SDD
  • Chase