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Primary User Emulation Detection Algorithm Based on Distributed Sensor Networks

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Abstract

In this paper, we propose a novel primary user emulation (PUE) detection approach which employs a distributed sensor network, where each sensor node operates as an independent PUE detector. Distributed nodes collaborate in order to obtain the final detection results for the whole network. A voting algorithm is used to improve the performance of energy detection, while the classification is conducted by the nearest node in order to improve the efficiency of the detector. As a result of voting, if a potential primary user exists, then the features of the unknown user is compared with entries from the database in order to obtain a solid detection match. An artificial neural network (ANN) is used for the classification of an unknown user. To assess the accuracy of the detection result, we implement a reliability check at the output of ANN. The proposed algorithm is validated via computer simulations as well as by experimental hardware implementations using the Universal Software Radio Peripheral (USRP) software-defined radio (SDR) platform. The experiment results show that the distributed network detector detects the PUE 180–200%, depending on the number of primary users, faster than single node detector.

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Notes

  1. Y = binocdf(X,N,P) computes a binomial CDF at each of the values in X using the corresponding number of trials in N and probability of success for each trial in P [36].

  2. The free space path loss block belongs to the RF Impairments Library.

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Acknowledgements

The authors would like to thank The MathWorks, Natick, MA, USA for their generous support of this research.

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Authors and Affiliations

  1. Worcester Polytechnic Institute, Worcester, MA, USA

    Di Pu, Bengi Aygun & Alexander M Wyglinski

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  1. Di Pu
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  2. Bengi Aygun
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  3. Alexander M Wyglinski
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Correspondence to Bengi Aygun.

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Pu, D., Aygun, B. & Wyglinski, A.M. Primary User Emulation Detection Algorithm Based on Distributed Sensor Networks. Int J Wireless Inf Networks 24, 344–355 (2017). https://doi.org/10.1007/s10776-017-0363-2

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  • DOI: https://doi.org/10.1007/s10776-017-0363-2

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