DOI QR코드

DOI QR Code

Energy-efficient data transmission technique for wireless sensor networks based on DSC and virtual MIMO

  • Received : 2018.12.01
  • Accepted : 2020.01.08
  • Published : 2020.06.08

Abstract

In a wireless sensor network (WSN), the data transmission technique based on the cooperative multiple-input multiple-output (CMIMO) scheme reduces the energy consumption of sensor nodes quite effectively by utilizing the space-time block coding scheme. However, in networks with high node density, the scheme is ineffective due to the high degree of correlated data. Therefore, to enhance the energy efficiency in high node density WSNs, we implemented the distributed source coding (DSC) with the virtual multiple-input multiple-output (MIMO) data transmission technique in the WSNs. The DSC-MIMO first compresses redundant source data using the DSC and then sends it to a virtual MIMO link. The results reveal that, in the DSC-MIMO scheme, energy consumption is lower than that in the CMIMO technique; it is also lower in the DSC single-input single-output (SISO) scheme, compared to that in the SISO technique at various code rates, compression rates, and training overhead factors. The results also indicate that the energy consumption per bit is directly proportional to the velocity and training overhead factor in all the energy saving schemes.

Keywords

References

  1. S. Cui, A. J. Goldsmith, and A. Bahai, Energy-efficiency of MIMO and cooperative MIMO techniques in sensor networks, IEEE J. SAC 22 (2004), no. 6, 1089-1098.
  2. S. K. Jayaweera, Virtual MIMO-based cooperative communication for energy-constrained wireless sensor networks, IEEE Trans. Wireless Commun. 5 (2006), no. 5, 984-989. https://doi.org/10.1109/TWC.2006.1633350
  3. Z. Rafique, B.-C. Seet, and A. Al-Anbuky, Performance analysis of cooperative virtual MIMO systems for wireless sensor networks, IEEE Sens. 13 (2013), no. 6, 7033-7052. https://doi.org/10.3390/s130607033
  4. Y. Peng et al., Enhancing energy efficiency via cooperative MIMO in wireless sensor networks: state of the art and future research directions, IEEE Commun. Mag. 55 (2017), 47-53. https://doi.org/10.1109/MCOM.2017.1600837
  5. M. Nasim, S. Qaisar, and S. Lee, An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks, IEEE Sens. 12 (2012), 92-114.
  6. N. Li, L. Zhang, and B. Li, A new energy-efficient data transmission scheme based on DSC and virtual MIMO for wireless sensor network, J. Contr. Sci. Eeng. 2015 (2015), 1-10.
  7. H. Wang et al., Cross-layer routing optimization in multirate wireless sensor networks for Distributed Source Coding based applications, IEEE Trans. Wireless Commun. 7 (2008), no. 10, pp. 3999-4009. https://doi.org/10.1109/T-WC.2008.070516
  8. S. K. Jayaweera et al., Signal-processing-aided distributed compression in virtual MIMO-based wireless sensor networks, IEEE Trans. Veh. Technol. 56 (2007), no. 5, 2630-2640. https://doi.org/10.1109/TVT.2007.900361
  9. N. Abughalieh et al., A mutual algorithm for optimizing distributed source coding in wireless sensor networks, Int. J. Distrib. Sens. Netw. 8 (2012), no. 2, 1-9.
  10. S. A. Imam et al., An energy-efficient data transmission scheme based on DSC-MIMO, in Proc. IEEE Int. Conf. Integr. Circuits Microsyst. (Nanjing, China), Nov. 2017, pp. 309-312.
  11. M. Sartipi and F. Fekri, Distributed Source Coding in Wireless Sensor Networks using LDPC Coding: the Entire Slepian-Wolf Rate Region, in Proc. IEEE Wireless Commun. Netw. Conf. (New Orleans, LA, USA), Mar. 2005, pp. 1939-1944.
  12. M. Sartipi and F. Fekri, Distributed source coding using short to moderate length rate-compatible LDPC codes the entire Slepian-Wolf rate region, IEEE Trans. Commun. 56 (2008), no. 3, 400-411. https://doi.org/10.1109/TCOMM.2008.060006
  13. T. Sheltami, M. Musaddiq, and E. Shakshuki, Data compression techniques in Wireless Sensor Networks, Future Gener. Comp. Syst. 64 (2016), 151-162. https://doi.org/10.1016/j.future.2016.01.015
  14. S. Haykin, Digital Communication System, Willy India Pvt. Ltd, New Delhi, India, 2015.
  15. T. M. Cover and J. A. Thomos, Elements of Information theory, Wiley-Interscience publication, Hoboken, NJ, 2002.
  16. M. Leinonen, M. Codreanu, and M. Juntti, Distributed distortion-rate optimized compressed sensing in wireless sensor networks, IEEE Trans. Commun. 66 (2018), no. 4, 1609-1623. https://doi.org/10.1109/TCOMM.2018.2790385
  17. N. Deligiannis et al., Distributed joint source-channel coding with copula-function-based correlation modeling for wireless sensors measuring temperature, IEEE Sens. 15 (2015), 4496-4507. https://doi.org/10.1109/JSEN.2015.2421821
  18. J. Chen and X. Han, The distributed source coding method research based on clustering wireless sensor networks, Int. J. Sens. Netw. 17 (2015), no. 4, https://doi.org/10.1504/IJSNET.2015.069585.
  19. M. Sartipi and F. Fekri. Source and channel coding in wireless sensor networks using LDPC codes, in Proc. Annu. IEEE Comuun. Soc. Conf. Sens. AD Hoc Commun. Netw. (Santa Clara, CA, USA), 2004, pp. 309-316.
  20. M. R. Islam and Y. S. Han, Cooperative MIMO communication at wireless sensor Network:An error correcting code approach, Sens. 11 (2011), no. 10, 9887-9903. https://doi.org/10.3390/s111009887
  21. T. S. Rappaport, Wireless communications, Prentice Hall of India, New Delhi, India, 2002.
  22. S. Gravano, Introduction of Error control, Oxford University Press, Oxford, UK, 2001.

Cited by

  1. Energy efficient wireless sensor network using optimum hops and virtual MIMO technique vol.2, pp.9, 2020, https://doi.org/10.1007/s42452-020-03360-3
  2. Current Trends on Green Wireless Sensor Networks vol.21, pp.13, 2020, https://doi.org/10.3390/s21134281