Telecommunication Systems

, Volume 68, Issue 1, pp 105–113 | Cite as

On the performance of wireless sensor networks with QSSK modulation in the presence of co-channel interference

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Abstract

This paper proposes the use of quadrature space shift keying (QSSK) modulation for wireless sensor networks (WSNs). QSSK is a multiple input multiple output communication protocol that attracted substantial interest due to several promised inherent advantages. It has been shown in literature that QSSK scheme achieves high spectral efficiency with low average error probability, high energy efficiency, and very simple transmitter and receiver architectures. Hence, its use in WSNs is very promising to ameliorate the major limitations of such networks. The performance of a WSN with QSSK modulation and in the presence of co-channel interference is studied in this paper. A closed form expression for the average pair wise error probability is derived and used to obtain a tight upper bound on the overall average bit error ratio. Obtained results verified the superiority of QSSK scheme as compared to traditional modulation techniques especially for high spectral efficiency.

Keywords

Wireless sensor networks MIMO Quadrature space shift keying (QSSK) Co-channel interference (CCI ) Performance analysis 

Notes

Acknowledgements

This work is supported by North Atlantic Treaty Organization (NATO), under the SPS Grant G4936 (Hybrid Sensor Network for Emergency Critical Scenarios).

References

  1. 1.
    Arampatzis, T., Lygeros, J., & Manesis, S. (2005). A survey of applications of wireless sensors and wireless sensor networks. In IEEE International symposium on, mediterrean conference on control and automation intelligent control.Google Scholar
  2. 2.
    Borges, L. M., Velez, F. J., & Lebres, A. S. (2014). Survey on the characterization and classification of wireless sensor network applications. IEEE Communications Surveys and Tutorials, vol. 16(no. 4), 1860–1890.CrossRefGoogle Scholar
  3. 3.
    Althunibat, S., Antonopoulos, A., Karatsakli, E., Granelli, F., & Verikoukis, C. (2016). Countering intelligent-dependent malicious nodes in target detection wireless sensor networks. IEEE Sensors Journal, 16(23), 8627–8639.Google Scholar
  4. 4.
    Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: A survey. Computer Networks, 38(4), 393–422.CrossRefGoogle Scholar
  5. 5.
    Abbasi, A. A., & Younis, M. (2007). A survey on clustering algorithms for wireless sensor networks. Computer Communications, 30(14), 2826–2841.CrossRefGoogle Scholar
  6. 6.
    Polastre, J., Szewczyk, R., & Culler, D. (2005). Telos: Enabling ultra-low power wireless research. In Fourth International Symposium on Information Processing in Sensor Networks, IPSN 2005 (pp. 364–369).Google Scholar
  7. 7.
    XBOW MICA2 Mote Specifications. http://www.xbow.com.
  8. 8.
    Yu, Y., Krishnamachari, B., & Prasanna, V. K. (2004). Energy-latency tradeoffs for data gathering in wireless sensor networks. IEEE INFOCOM, 2004. doi: 10.1109/INFCOM.2004.1354498.
  9. 9.
    Son, D., Krishnamachari, B., & Heidemann, J. (2006). Experimental study of concurrent transmission in wireless sensor networks. In Proceedings of the 4th international conference on Embedded networked sensor systems. ACM.Google Scholar
  10. 10.
    Bagaa, M., Challal, Y., Ksentini, A., Derhab, A., & Badache, N. (2014). Data aggregation scheduling algorithms in wireless sensor networks: Solutions and challenges. IEEE Communications Surveys and Tutorials, vol. 16(no. 3), 1339–1368.CrossRefGoogle Scholar
  11. 11.
    Gao, S., Qian, L., & Vaman, D. R. (2008). Distributed energy efficient spectrum access in wireless cognitive radio sensor networks. In IEEE Wireless communications and networking conference (WCNC).Google Scholar
  12. 12.
    Shojafar, M., Abolfazli, S., Mostafaei, H., & Singhal, M. (2015). Improving channel assignment in multi-radio wireless mesh networks with learning automata. Wireless Personal Communications, 82(1), 61–80.CrossRefGoogle Scholar
  13. 13.
    Zhu, J., Song, Y., Jiang, D., & Song, H. (2016). Multi-armed bandit channel access scheme with cognitive radio technology in wireless sensor networks for the internet of things. IEEE Access, 4, 4609–4617.CrossRefGoogle Scholar
  14. 14.
    Chiwewe, T. M., & Hancke, G. P. (2012). A distributed topology control technique for low interference and energy efficiency in wireless sensor networks. IEEE Transactions on Industrial Informatics, 8(1), 11–19.CrossRefGoogle Scholar
  15. 15.
    Kumar, S., Sharma, A., & Raghuvanshi, S. S. (2011). Energy efficient scheduling algorithm with interference reduction for wireless sensor networks. In International conference on computational intelligence and communication networks, Gwalior, (pp. 328–332).Google Scholar
  16. 16.
    Liang, S., Ge, Y., Jiang, S., & Tan, H. P. (2014). A lightweight and robust interference mitigation scheme for wireless body sensor networks in realistic environments. In IEEE Wireless communications and networking conference (WCNC), Istanbul, (pp. 1697–1702).Google Scholar
  17. 17.
    Razi, A., & Abedi, A. (2011). Interference reduction in Wireless Passive Sensor Networks using directional antennas. In 4th Annual caneus fly by wireless workshop, Montreal, QC (pp. 1–4).Google Scholar
  18. 18.
    Pantazis, N. A., Nikolidakis, S. A., & Vergados, D. D. (2013). Energy-efficient routing protocols in wireless sensor networks: A survey. IEEE Communications surveys and tutorials, 15(2), 551–591.CrossRefGoogle Scholar
  19. 19.
    Althunibat, S., Abu-Al-Aish, A., Shehab, W. F. A., & Alsawalmeh, W. H. (2016). Auction-based data gathering scheme for wireless sensor networks. IEEE Communications Letters, 20(6), 1223–1226.CrossRefGoogle Scholar
  20. 20.
    Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys and Tutorials, vol. 19(no. 2), 828–854.CrossRefGoogle Scholar
  21. 21.
    Mesleh, R., Ikki, S. S., & Aggoune, H. M. (2015). Quadrature spatial modulation. IEEE Transactions on Vehicular Technology, 64(6), 2738–2742.CrossRefGoogle Scholar
  22. 22.
    Basar, E. (2016). Index modulation techniques for 5G wireless networks. IEEE Communications Magazine, vol. 54(no. 7), 168–175.CrossRefGoogle Scholar
  23. 23.
    Jeganathan, J., Ghrayeb, A., Szczecinski, L., & Ceron, A. (2009). Space shift keying modulation for MIMO channels. IEEE Transactions on Wireless Communications, 8(7), 3692–3703.CrossRefGoogle Scholar
  24. 24.
    Mesleh, R. Y., Haas, H., Sinanovic, S., Ahn, C. W., & Yun, S. (2008). Spatial modulation. IEEE Transactions on Vehicular Technology, 57(4), 2228–2241.CrossRefGoogle Scholar
  25. 25.
    Mesleh, R., Ikki, S. S., & Aggoune, H. M. (2017). Quadrature spatial modulation-performance analysis and impact of imperfect channel knowledge. Transactions on Emerging Telecommunications Technologies, 28, e2905. doi: 10.1002/ett.2905.
  26. 26.
    Badarneh, O. S., & Mesleh, R. (2016). A comprehensive framework for quadrature spatial modulation in generalized fading scenarios. IEEE Transactions on Communications, 64(7), 2961–2970.CrossRefGoogle Scholar
  27. 27.
    Ikki, S. S., & Mesleh, R. (2012). A general framework for performance analysis of space shift keying (SSK) modulation in the presence of gaussian imperfect estimations. IEEE Communications Letters, 16(2), 228–230.CrossRefGoogle Scholar
  28. 28.
    Khalifeh, A., Al-Agtash, S., Tanash, R., & AlQudah, M., (2016). Deploying agents for monitoring and notification of wireless sensor networks. In 2016 IEEE 28th International conference on tools with artificial intelligence (ICTAI), San Jose, CA, (pp. 754–757).Google Scholar
  29. 29.
    Akyildiz, I. F., & Wang, Xudong. (2005). A survey on wireless mesh networks. IEEE Communications Magazine, 43(9), S23–S30.CrossRefGoogle Scholar
  30. 30.
    Benyamina, D., Hafid, A., & Gendreau, M. (2012). Wireless mesh networks design? A survey. IEEE Communications Surveys and Tutorials, 14(2), 299–310.CrossRefGoogle Scholar
  31. 31.
    Craig, J. W. (1991). A new, simple and exact result for calculating the probability of error for two-dimensional signal constellations. In MILCOM 91 - Conference record, McLean, VA, (vol. 2, pp. 571–575).Google Scholar
  32. 32.
    Turin, G. L. (1960). The characteristic function of Hermitian quadratic forms in complex normal variables. Biometrika, 47(1/2), 199–201.CrossRefGoogle Scholar
  33. 33.
    Proakis, J. G. (1995). Digital communications. New York: McGraw-Hill.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Communications EngineeringAl-Hussein Bin Talal University (AHU)Ma’anJordan
  2. 2.Department of Electrical and Communication EngineeringGerman Jordanian University (GJU)AmmanJordan

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