Influence of relaying malicious node within cooperative sensing in cognitive radio network

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Abstract

Intending to enhance the utilization of the radio spectrum in cognitive radio without inflecting the primary user is a primary issue. Cooperative sensing techniques have been proposed to improve the radio access decision compensating for the inherent sensing errors present in the devices. Meanwhile collaboration among secondary users poses a potential security threat. A malicious node might alter the resulted sensing information to attain a personal benefit. This text quantifies the number of nodes getting affected by the malicious nodes’ false reports by deriving formulas validated by simulated scenarios. The analysis of this paper is based on the assumption that nodes are deployed according to a poisson point process. First, we estimate the scope of influence of the malicious node, it is shown in a one dimensional network that a malicious node can take advantage of its relative position with respect to other neighboring nodes in order to leverage its influence. Then, the influence of this malicious node in a two dimensional network is investigated varying its relative position. The derivations are validated by simulations of the network carried out via R programming language describing the relevant deployment scenarios.

Keywords

Cognitive radio Cooperative sensing Stochastic geometry Poisson point process 

References

  1. 1.
    Mitola, J., & Maguire, G. Q. (1999). Cognitive radio: Making software radios more personal. IEEE Personal Communications, 6(4), 13–18.  https://doi.org/10.1109/98.788210. ISSN 1070-9916.CrossRefGoogle Scholar
  2. 2.
    Yucek, T., & Arslan, H. (2009). A survey of spectrum sensing algorithms for cognitive radio applications. IEEE Communications Surveys Tutorials, 11(1), 116–130.  https://doi.org/10.1109/SURV.2009.090109. ISSN 1553-877X.CrossRefGoogle Scholar
  3. 3.
    Mishra, S.M., Sahai, A., & Brodersen, R.W. (June 2006). Cooperative sensing among cognitive radios. In 2006 IEEE international conference on communications (Vol. 4. pp. 1658–1663).  https://doi.org/10.1109/ICC.2006.254957.
  4. 4.
    Gupta, A., & Jha, R. K. (2015). A survey of 5g network: Architecture and emerging technologies. IEEE Access, 3, 1206–1232.  https://doi.org/10.1109/ACCESS.2015.2461602. ISSN 2169-3536.CrossRefGoogle Scholar
  5. 5.
    Soltanmohammadi, E., & Naraghi-Pour, M. (2014). Fast detection of malicious behavior in cooperative spectrum sensing. IEEE Journal on Selected Areas in Communications, 32(3), 377–386.  https://doi.org/10.1109/JSAC.2014.140301. ISSN 0733-8716.CrossRefGoogle Scholar
  6. 6.
    Adelantado, F., & Verikoukis, C. (June 2011). A non-parametric statistical approach for malicious users detection in cognitive wireless ad-hoc networks. In 2011 IEEE international conference on communications (ICC) (pp. 1–5).  https://doi.org/10.1109/icc.2011.5963004.
  7. 7.
    ElSawy, H., Hossain, E., & Haenggi, M. (2013). Stochastic geometry for modeling, analysis, and design of multi-tier and cognitive cellular wireless networks: A survey. IEEE Communications Surveys Tutorials, 15(3), 996–1019.  https://doi.org/10.1109/SURV.2013.052213.00000. ISSN 1553-877X.CrossRefGoogle Scholar
  8. 8.
    Shojaeifard, A., Hamdi, K. A., Alsusa, E., So, D. K. C., & Tang, J. (2015). Exact sinr statistics in the presence of heterogeneous interferers. IEEE Transactions on Information Theory, 61(12), 6759–6773.  https://doi.org/10.1109/TIT.2015.2482482. ISSN 0018-9448.MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Hasan, S. M., Hayat, M. A., & Hossain, M. F. (2015). On the downlink sinr and outage probability of stochastic geometry based lte cellular networks with multi-class services. In 2015 18th international conference on computer and information technology (ICCIT) (pp. 65–69).  https://doi.org/10.1109/ICCITechn.2015.7488044.
  10. 10.
    Dulman, S., Rossi, M., Havinga, P., & Zorzi, M. (2006). On the hop count statistics for randomly deployed wireless sensor networks. International Journal of Sensor Networks, 1(1/2), 89–102.  https://doi.org/10.1504/IJSNET.2006.010837.CrossRefGoogle Scholar
  11. 11.
    Ta, X., Mao, G., & Anderson, B. D. O. (2007). On the probability of k-hop connection in wireless sensor networks. IEEE Communications Letters, 11(8), 662–664.  https://doi.org/10.1109/LCOMM.2007.070569. ISSN 1089-7798.CrossRefGoogle Scholar
  12. 12.
    Luo, J., Hu, J., Wu, D., & Li, R. (2015). Opportunistic routing algorithm for relay node selection in wireless sensor networks. IEEE Transactions on Industrial Informatics, 11(1), 112–121.  https://doi.org/10.1109/TII.2014.2374071. ISSN 1551-3203.CrossRefGoogle Scholar
  13. 13.
    Guo, A., & Haenggi, M. (2013). Spatial stochastic models and metrics for the structure of base stations in cellular networks. IEEE Transactions on Wireless Communications, 12(11), 5800–5812.  https://doi.org/10.1109/TWC.2013.100113.130220. ISSN 1536-1276.CrossRefGoogle Scholar
  14. 14.
    Lu, W., & Di Renzo, M. (2015). Stochastic geometry modeling of cellular networks: Analysis, simulation and experimental validation. In Proceedings of the 18th ACM international conference on modeling, analysis and simulation of wireless and mobile systems. MSWiM ’15, (pp. 179–188). ACM, New York, NY.  https://doi.org/10.1145/2811587.2811597. http://doi.acm.org/10.1145/2811587.2811597.
  15. 15.
    Gentle, James E. (2009). Computational statistics. Berlin: Springer.CrossRefMATHGoogle Scholar
  16. 16.
    Abramowitz, Milton, & Stegun, Irene A. (1970). Handbook of mathematical functions: With formulas, graphs, and mathematical tables. Mineloa: Dover Publications.MATHGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Network PlanningNational Telecommunication InstituteCairoEgypt
  2. 2.Computer EngineeringArab Academy for Science, Technology and Maritme TransportCairoEgypt

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