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United Versus Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks (C-WSNs)

Wireless Personal Communications volume 95, pages 2461–2483 (2017)Cite this article

Abstract

Cooperation is an effective method to increase the performance metrics of spectrum sensing in cognitive radio (CR). For spectrum sensing in cognitive wireless sensor networks (C-WSNs), low complexity and consequently low performance methods are applicable due to resource constraint. Also, we can profit the cooperation for overcoming the noise uncertainty, fading, shadowing, hidden primary user problem etc. But, low performance methods increase severely false alarm rate \((P_{Fa})\) and waste the precious resources of sensor nodes, because of collisions and retransmitions. In this paper, we propose two approaches for utilizing high performance spectrum sensing methods in C-WSNs. Then, we focus on our second approach i.e. United Spectrum Sensing, as a more comprehensive method than conventional cooperative spectrum sensing in CR, to solve the problem of high performance spectrum sensing in C-WSNs.

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References

  1. Prakash, P., Lee, S. R., Noh, S. K., & Choi, D. Y. (2014). Issues in realization of cognitive radio sensor networks. International Journal of Control and Automation, 7(1), 141–152.

    Article  Google Scholar 

  2. Zahmati, A. S., Hussain, S., Fernando, X., & Grami, A. (2009). Cognitive wireless sensor networks: Emerging topics and recent challenges. IEEE International Conference on Science and Technology for Humanity (IEEE TIC-STH) (pp. 593–596).

  3. Yau, K. L. A., Komisarczuk, P., & Teal, P. D. (2009). Cognitive radio-based wireless sensor networks: Conceptual design and open issues. In Proceedings of 2nd IEEE Workshop on Wireless and Internet Services (WISe).

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

    Article  Google Scholar 

  5. Fragkiadakis, A., Angelakis, V., & Tragos, E. Z. (2014). Securing cognitive wireless sensor networks: A survey. International Journal of Distributed Sensor Networks. doi:10.1155/2014/393248.

  6. Joshi, G. P., Seung, Y. N., & Sung, W. K. (2013). Cognitive radio wireless sensor networks: Applications, challenges and research trends. Journal of Sensors, 13(9), 11196–11228.

    Article  Google Scholar 

  7. Beneslu, S., & Shafiee, M. (2014). A high performance, low complexity method for spectrum sensing in cognitive wireless sensor network (C-WSN). In Proceedings od 4th International Conference on Information Technology Management, Communication and Computer (pp. 40–48).

  8. Haykin, S., Thomson, D. J., & Reed, J. H. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97(5), 849–877.

    Article  Google Scholar 

  9. Shafiee, M., & Vakili, V. T. (2012). MTM-based spectrum sensing in cognitive wireless multimedia sensor networks (C-WMSNs). In Proceedings of 6th IEEE International Symposium on Telecommunications (IST).

  10. Shafiee, M., & Vakili, V. T. (2013). An approach to efficient spectrum sensing in cognitive wireless sensor networks (C-WSNs). Journal of Applied Mechanics and Materials (AMM), from 2nd International Conference on Civil Engineering and Transportation (Vol. 256–259, pp. 2303–2306).

  11. Viswanathan, R., & Ahsant, B. (2012). A review of sensing and distributed detection algorithms for cognitive radio systems. International Journal on Smart Sensing and Intelligent Systems, 5(1), 177–190.

    Google Scholar 

  12. Kay, S. M. (1988). Modern spectral estimation: Theory and application. Englewood Cliffs: Prentice Hall.

    MATH  Google Scholar 

  13. Han, N., Shon, S. H., Joo, J. O., & Kim, J. M. (2006). Spectrum sensing method for increasing the spectrum efficiency in wireless sensor network. Springer Journal of Computer Science, 4239, 478–488.

    Google Scholar 

  14. Fodor, V., & Glaropoulos, I. (2009). Sensor networks for spectrum sensing: Working assumptions and design goals. Sweden: Crops2 Project, Royal Institute of Technology (KTH University).

    Google Scholar 

  15. Pham, H. N., Zhang, Y., Engelstad, P. E., Skeie, T., & Eliassen, F. (2009). Optimal cooperative spectrum sensing in cognitive sensor networks. International Conference on Wireless Communications and Mobile Computing (ICWCMC) (pp. 1073–1079).

  16. Izumi, S., Tsuruda, K., Takeuchi, T., Lee, H., Kawaguchi, H., & Yoshimoto, M. (2010). A low-power multi resolution spectrum sensing (MRSS) architecture for a wireless sensor network with cognitive radio. In Proceedings of 4th IEEE International Conference on Sensor Technologies and Applications (pp. 39–44).

  17. Akbari, M., & Falahati, A. (2011). A fault-tolerant cooperative spectrum sensing algorithm over cognitive radio network based on wireless sensor network. The Journal of Wireless Sensor Network (WSN), 3(3), 83.

    Article  Google Scholar 

  18. Chatterjee, S. R., Hazra, R., Deb, A., & Chakraborty, M. (2011). Cyclostationary spectral analysis approach to spectrum sensing for mobile radio signals. In 2nd Annual IEEE International Conference on Innovative Techno-Management Solutions for Social Sector.

  19. Yan, J., & Inwhee, J. (2016). Markov model-based energy efficiency spectrum sensing in cognitive radio sensor networks. Hindawi Journal of Computer Networks and Communications.

  20. Subhedar, M., & Birajdar, G. (2011). Spectrum sensing techniques in cognitive radio networks: A survey. International Journal of Next-Generation Networks, 3, 37–51.

    Article  Google Scholar 

  21. Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys and Tutorials, 11(1), 116–130.

    Article  Google Scholar 

  22. Kay, S. M. (1993). Fundamentals of statistical signal processing: Estimation theory. Englewood Cliffs: Prentice Hall.

    MATH  Google Scholar 

  23. Cabric, D., Mishra, S., & Brodersen, R. (2004). Implementation issues in spectrum sensing for cognitive radios. Asilomar Conference on Signals, Systems and Computers (Vol. 1, pp. 772–776).

  24. Akyildiz, I. F., Lo, B. F., & Balakrishnan, R. (2011). Cooperative spectrum sensing in cognitive radio networks: A survey. Elsevier Journal on Physical Communication, 4(1), 40–62.

    Article  Google Scholar 

  25. Selim, B., Alhussein, O., Karagiannidis, G. K., & Muhaidat, S. (2015). Optimal cooperative spectrum sensing over composite fading channels. IEEE International Conference on Communication Workshop (ICCW).

  26. Do, N. T., & An, B. (2015). A soft-hard combination-based cooperative spectrum sensing scheme for cognitive radio networks. Journal of Sensors, 15(2), 4388–4407.

    Article  Google Scholar 

  27. Kyperountas, S., Correal, N., & Shi, Q. (2010). A comparison of fusion rules for cooperative spectrum sensing in fading channels. In Proceedings of 20th Virginia Tech Symposium on Wireless Personal Communications (pp. 1–6).

  28. Guimaraes, D. A., & Aquino, G. P. (2015). Resource-efficient fusion over fading and non-fading reporting channels for cooperative spectrum sensing. Journal of Sensors, 15(1), 1861–1884.

    Article  Google Scholar 

  29. Weiss, T., Hillenbrand, J., & Jondral, F. (2003). A diversity approach for the detection of idle spectral resources in spectrum pooling systems. In Proceedings of 48th International Scientific Colloquium (p. 3738).

  30. Lunden, J., Koivunen, V., Huttunen, A., & Poor, H. V. (2007). Spectrum sensing in cognitive radios based on multiple cyclic frequencies. In IEEE International Conference on Cognitive Radio Oriented Wireless Networks and Communication (Crowncom).

  31. Dressler, F. (2008). Self-organization in sensor and actor networks. Chichester: Wiley.

    Google Scholar 

  32. Rizvi, S., Karpinski, K., & Razaque, A. (2015). Novel architecture of self-organized mobile wireless sensor networks. Journal of Computing Science and Engineering, 9(4), 163–176.

    Article  Google Scholar 

  33. Shah, M., Zhang, S., & Maple, C. (2013). Cognitive radio networks for internet of things: Applications, challenges and future. In Proceedings of 19th IEEE International Conference on Automation and Computing (ICAC) (pp. 1–6).

  34. Hossain, E., Rasti, M., Tabassum, H., & Abdelnasser, A. (2014). Evolution toward 5G multi-tier cellular wireless networks: An interference management perspective. IEEE Wireless Communications, 21(3), 118–127.

    Article  Google Scholar 

  35. Thomson, D. J. (1982). Spectrum estimation and harmonic analysis. Proceedings of the IEEE, 70(9), 1055–1096.

    Article  Google Scholar 

  36. Haykin, S. (2005). Cognitive radio: Bain-empowered wireless communications. IEEE Selected Areas on Communincation, 23(2), 201–220.

    Article  Google Scholar 

  37. Hu, G., Muqing, W., Chunxiu, X., & Qianqian, W. (2010). An improved multitaper method for spectrum sensing in cognitive radio networks. In Proceedings of 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT) (pp. 393–396).

  38. Alghamdi, O. A., & Ahmed, M. Z. (2011). New optimization method for cooperative spectrum sensing in cognitive radio networks. IEEE Wireless Advanced (WiAd).

  39. Alghamdi, O. A., Abu-Rgheff, M. A., & Ahmed, M. Z. (2010). MTM parameters optimization for 64-FFT cognitive radio spectrum sensing using monte carlo simulation. In Proceedings of 2nd International Conference on Emerging Network Intelligence (pp. 107–113).

  40. Qian, Z., Lu, C., An, M., & Tolimieri, R. (1994). Self-sorting in place FFT algorithm with minimum working space. IEEE Transactions on Signal Processing, 42(10), 2835–2836.

    Article  Google Scholar 

  41. Harvey, D., & Roche, D. S. (2010). In-place truncated fourier transform and applications to polynomial multiplication. In Proceedings of International Symposium on Symbolic and Algebraic Computation (pp. 325–329).

  42. Cooley, J. W. (1990). ”How the FFT gained acceptance”, a history of scientific computing. Reading, MA: ACM Publication.

    Google Scholar 

  43. Cordeiro, C., Challapali, K., & Birru, D. (2006). IEEE 802.22: An introduction to the first wireless standard based on cognitive radios. Journal of Communications, 1(1), 38–47.

    Article  Google Scholar 

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Authors and Affiliations

  1. Iran University of Science and Technology (IUST), Tehran, Iran

    Morteza Shafiee & Vahid Tabataba Vakili

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  1. Morteza Shafiee
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  2. Vahid Tabataba Vakili
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Correspondence to Vahid Tabataba Vakili.

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Shafiee, M., Vakili, V.T. United Versus Cooperative Spectrum Sensing in Cognitive Wireless Sensor Networks (C-WSNs). Wireless Pers Commun 95, 2461–2483 (2017). https://doi.org/10.1007/s11277-016-3929-x

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  • DOI: https://doi.org/10.1007/s11277-016-3929-x

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