Skip to main content

Advertisement

Springer Nature Link
Log in

A novel node selection scheme for energy-efficient cooperative spectrum sensing using D–S theory

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Energy-efficient and reliable detection of available spectrum are fundamental objectives for cooperative spectrum sensing (CSS) in cognitive radio sensor networks (CRSNs). In this paper, a novel node selection scheme for energy-efficient CSS based on Dempster–Shafer (D–S) theory is proposed. Firstly, taking into account the historical data of nodes with historical reliability and residual energy of nodes, we propose a filtering strategy to filter out ineligible nodes, in order to reduce the computation loads in later steps and the amount of sensing results to be transmitted. Secondly, taking into account energy consumption balance of the network with the distance from the remaining nodes to fusion center, we propose a representative node selection algorithm for CSS, in order to reduce energy consumption. Thirdly, we consider that some representative nodes (R-Nodes) may not work as expected. Hence, facing this problem of malicious nodes in CRSNs, we propose an evaluation method based on D–S theory which considers simultaneously the current reliability and the mutually supportive degree among different R-Nodes to derive final decision. Simulation results show that the proposed scheme can not only reduce energy consumption, but can guarantee spectrum sensing accuracy, even in the presence of malicious nodes.

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

Access this article

Log in via an institution

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.

Institutional subscriptions

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

References

  1. Akan, O. B., Karli, O. B., & Ergul, O. (2009). Cognitive radio sensor networks. IEEE Network,23(4), 34–40.

    Article  Google Scholar 

  2. Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications,6(4), 13–18.

    Article  Google Scholar 

  3. Ghasemi, A., & Sousa, E. S. (2005). Collaborative spectrum sensing for opportunistic access in fading environments. In First IEEE international symposium on new frontiers in dynamic spectrum access networks, 2005. DySPAN 2005 (pp. 131–136). IEEE.

  4. Axell, E., Leus, G., Larsson, E. G., & Poor, H. V. (2012). Spectrum sensing for cognitive radio: State-of-the-art and recent advances. IEEE Signal Processing Magazine,29(3), 101–116.

    Article  Google Scholar 

  5. Patil, V. M., & Patil, S. R. (2016). A survey on spectrum sensing algorithms for cognitive radio. In International conference on advances in human machine interaction (HMI) (pp. 1–5). IEEE.

  6. Sun, H., Nallanathan, A., Wang, C. X., & Chen, Y. (2013). Wideband spectrum sensing for cognitive radio networks: A survey. IEEE Wireless Communications,20(2), 74–81.

    Article  Google Scholar 

  7. Urkowitz, H. (1967). Energy detection of unknown deterministic signals. Proceedings of the IEEE,55(4), 523–531.

    Article  Google Scholar 

  8. Wang, B., Liu, K. R., & Clancy, T. C. (2010). Evolutionary cooperative spectrum sensing game: How to collaborate? IEEE Transactions on Communications,58(3), 890–900.

    Article  Google Scholar 

  9. Mu, H., & Tugnait, J. K. (2012). Joint soft-decision cooperative spectrum sensing and power control in multiband cognitive radios. IEEE Transactions on Signal Processing,60(10), 5334–5346.

    Article  MathSciNet  MATH  Google Scholar 

  10. Reisi, N., Gazor, S., & Ahmadian, M. (2013). Distributed cooperative spectrum sensing in mixture of large and small scale fading channels. IEEE Transactions on Wireless Communications,12(11), 5406–5412.

    Article  Google Scholar 

  11. Liu, X., Li, F., & Na, Z. (2017). Optimal resource allocation in simultaneous cooperative spectrum sensing and energy harvesting for multichannel cognitive radio. IEEE Access,5, 3801–3812.

    Article  Google Scholar 

  12. Cai, Y., Mo, Y., Ota, K., Luo, C., Dong, M., & Yang, L. (2014). Optimal data fusion of collaborative spectrum sensing under attack in cognitive radio networks. IEEE Network,28(1), 17–23.

    Article  Google Scholar 

  13. Gao, Z., Zhu, H., Li, S., Du, S., & Li, X. (2012). Security and privacy of collaborative spectrum sensing in cognitive radio networks. IEEE Wireless Communications,19(6), 106–112.

    Article  Google Scholar 

  14. Shafer, G. (1976). A mathematical theory of evidence (Vol. 42). Princeton: Princeton University Press.

    MATH  Google Scholar 

  15. Lee, P. H., & Tsai, P. Y. (2015). Design and implementation of spatial-temporal spectrum sensing in cooperative cognitive radio sensor network. In International SoC design conference (ISOCC) (pp. 25–26). IEEE.

  16. Fu, J., Yibing, Z., Yi, L., Shuo, L., &, Jun, P. (2015). The energy efficiency optimization based on dynamic spectrum sensing and nodes scheduling in cognitive radio sensor networks. In 27th Chinese control and decision conference (CCDC) (pp. 4371–4378). IEEE.

  17. Ma, X., Zeng, F., & Xu, J. (2015). A novel energy efficient cooperative spectrum sensing scheme for cognitive radio sensor network based on evolutionary game. In IEEE international workshop on local and metropolitan area networks (LANMAN) (pp. 1–6). IEEE.

  18. Qihang, P., Kun, Z., Jun, W., & Shaoqian, L. (2006). A distributed spectrum sensing scheme based on credibility and evidence theory in cognitive radio context. In IEEE 17th international symposium on personal, indoor and mobile radio communications (pp. 1–5). IEEE.

  19. Men, S., Chargé, P., & Pillement, S. (2015). A robust cooperative spectrum sensing method against faulty nodes in CWSNs. In IEEE international conference on communication workshop (ICCW) (pp. 334–339). IEEE.

  20. Liu, X., Jia, M., Na, Z., Lu, W., & Li, F. (2018). Multi-modal cooperative spectrum sensing based on Dempster–Shafer fusion in 5G-based cognitive radio. IEEE Access,6, 199–208.

    Article  Google Scholar 

  21. Han, Y., Chen, Q., & Wang, J. X. (2012). An enhanced DS theory cooperative spectrum sensing algorithm against SSDF attack. In IEEE 75th vehicular technology conference (VTC spring) (pp. 1–5). IEEE.

  22. Wang, J., Feng, S., Wu, Q., Zheng, X., Xu, Y., & Ding, G. (2014). A robust cooperative spectrum sensing scheme based on Dempster–Shafer theory and trustworthiness degree calculation in cognitive radio networks. EURASIP Journal on Advances in Signal Processing,2014(1), 35.

    Article  Google Scholar 

  23. Monemian, M., Mahdavi, M., & Omidi, M. J. (2016). Optimum sensor selection based on energy constraints in cooperative spectrum sensing for cognitive radio sensor networks. IEEE Sensors Journal,16(6), 1829–1841.

    Article  Google Scholar 

  24. Cacciapuoti, A. S., Akyildiz, I. F., & Paura, L. (2012). Correlation-aware user selection for cooperative spectrum sensing in cognitive radio ad hoc networks. IEEE Journal on Selected Areas in Communications,30(2), 297–306.

    Article  Google Scholar 

  25. Ebrahimzadeh, A., Najimi, M., Andargoli, S. M. H., & Fallahi, A. (2015). Sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing. IEEE Transactions on Vehicular Technology,64(4), 1565–1577.

    Article  Google Scholar 

  26. Cacciapuoti, A. S., Caleffi, M., Paura, L., & Savoia, R. (2013). Decision maker approaches for cooperative spectrum sensing: Participate or not participate in sensing? IEEE Transactions on Wireless Communications,12(5), 2445–2457.

    Article  Google Scholar 

  27. Najimi, M., Ebrahimzadeh, A., Andargoli, S. M. H., & Fallahi, A. (2015). Energy-efficient sensor selection for cooperative spectrum sensing in the lack or partial information. IEEE Sensors Journal,15(7), 3807–3818.

    Article  Google Scholar 

  28. Ghasemi, A., & Sousa, E. S. (2007). Opportunistic spectrum access in fading channels through collaborative sensing. JCM,2(2), 71–82.

    Article  Google Scholar 

  29. Digham, F. F., Alouini, M. S., & Simon, M. K. (2007). On the energy detection of unknown signals over fading channels. IEEE Transactions on Communications,55(1), 21–24.

    Article  Google Scholar 

  30. Cabric, D., Tkachenko, A., &, Brodersen, R. W. (2006). Experimental study of spectrum sensing based on energy detection and network cooperation. In Proceedings of the first international workshop on technology and policy for accessing spectrum (p. 12). ACM.

  31. Peh, E., & Liang, Y. C. (2007). Optimization for cooperative sensing in cognitive radio networks. In Wireless communications and networking conference, 2007. WCNC 2007 (pp. 27–32). IEEE.

  32. Heinzelman, W. B., Chandrakasan, A. P., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transactions on Wireless Communications,1(4), 660–670.

    Article  Google Scholar 

  33. Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences (p. 10). IEEE.

  34. Nguyen-Thanh, N., & Koo, I. (2009). An enhanced cooperative spectrum sensing scheme based on evidence theory and reliability source evaluation in cognitive radio context. IEEE Communications Letters,13(7), 492–494.

    Article  Google Scholar 

  35. Xing, X., Wang, W., Wang, Z., & Liu, K. (2013). Weighted cooperative spectrum sensing based on DS evidence theory and double-threshold detection. In IEEE 5th international symposium on microwave, antenna, propagation and EMC technologies for wireless communications (MAPE) (pp. 145–149). IEEE.

  36. Maleki, S., Pandharipande, A., & Leus, G. (2011). Energy-efficient distributed spectrum sensing for cognitive sensor networks. IEEE Sensors Journal,11(3), 565–573.

    Article  Google Scholar 

  37. Ali, Q. I., Abdulmaowjod, A., & Mohammed, H. M. (2010). Simulation and performance study of wireless sensor network (WSN) using MATLAB. In 1st International conference on energy, power and control (EPC-IQ) (pp. 307–314). IEEE.

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 61602252 and 61702278), the Natural Science Foundation of Jiangsu Province of China (Grant Nos. BK20160967 and BK20160964), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant No. 16KJB510024), and the China-USA Computer Science Research Center.

Author information

Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, 219 Ningliu Rd, Nanjing, 210044, China

    Zilong Jin & Yu Qiao

  2. Department of Computer Science and Engineering, Michigan State University, 428 S Shaw Ln, Lansing, MI, 48824, USA

    Zilong Jin

Authors
  1. Zilong Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu Qiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zilong Jin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jin, Z., Qiao, Y. A novel node selection scheme for energy-efficient cooperative spectrum sensing using D–S theory. Wireless Netw 26, 269–281 (2020). https://doi.org/10.1007/s11276-018-1810-4

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-018-1810-4

Keywords

Search

Navigation

pFad - Phonifier reborn

Pfad - The Proxy pFad of © 2024 Garber Painting. All rights reserved.

Note: This service is not intended for secure transactions such as banking, social media, email, or purchasing. Use at your own risk. We assume no liability whatsoever for broken pages.


Alternative Proxies:

Alternative Proxy

pFad Proxy

pFad v3 Proxy

pFad v4 Proxy