Neural Computing and Applications

, Volume 32, Issue 1, pp 151–161 | Cite as

Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks

  • S. VimalEmail author
  • L. Kalaivani
  • M. Kaliappan
  • A. Suresh
  • Xiao-Zhi Gao
  • R. Varatharajan
Brain- Inspired computing and Machine learning for Brain Health


The cognitive radio network (CR) is a primary and promising technology to distribute the spectrum assignment to an unlicensed user (secondary users) which is not utilized by the licensed user (primary user).The cognitive radio network frames a reactive security policy to enhance the energy monitoring while using the CR network primary channels. The CR network has a good amount of energy capacity using battery resource and accesses the data communication via the time-slotted channel. The data communication with moderate energy-level utilization during transmission is a great challenge in CR network security monitoring, since intruders may often attack the network in reducing the energy level of the PU or SU. The framework used to secure the communication is using the discrete-time partially observed Markov decision process. This system proposes a modern data communication-secured scheme using private key encryption with the sensing results, and eclat algorithm has been proposed for energy detection and Byzantine attack prediction. The data communication is secured using the AES algorithm at the CR network, and the simulation provides the best effort-efficient energy usage and security.


Machine learning Wireless communication Cognitive radio networks Byzantine attack Éclat algorithm 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical responsibilities of the authors

The authors follow the ethical information provided in the journal and hereby abide the same with the journal. I confirm that this work is original and has not been published elsewhere nor it is currently under consideration for publication elsewhere.


  1. 1.
    Zhao Q, Krishnamachari B, Liu K (2008) On myopic sensing for multi-channel opportunistic access: structure, optimality, and performance. IEEE Trans Wirel Commun 7(12):5431–5440CrossRefGoogle Scholar
  2. 2.
    Yan M et al (2011) Game-theoretic approach against selfish attacks in cognitive radio networks. In: IEEE/ACIS 10th International conference computer and information science (ICIS)Google Scholar
  3. 3.
    Cichoń K, Kliks A, Bogucka H (2016) Energy-efficient cooperative spectrum sensing: a survey. IEEE Commun Surveys Tutor 18(3):1861–1886CrossRefGoogle Scholar
  4. 4.
    Wang HS, Moayeri N (1995) Finite-state Markov channel: a useful model for radio communication channels. IEEE Trans Veh Technol 44(1):163–171CrossRefGoogle Scholar
  5. 5.
    Alahmadi A, Abdelhakim M, Ren J, Li T (2013) Mitigating primary user emulation attacks in cognitive radio networks using advanced encryption standard. In: Global communications conference (GLOBECOM)Google Scholar
  6. 6.
    Park S, Hong D (2013) Optimal spectrum access for energy harvesting cognitive radio networks. IEEE Trans Wirel Commun 12(12):6166–6179CrossRefGoogle Scholar
  7. 7.
    Elkashlan M, Wang L, Duong TQ, Karagiannidis GK, Nallanathan A (2015) On the security of cognitive radio networks. IEEE Trans Veh Technol 64(8):3790–3795CrossRefGoogle Scholar
  8. 8.
    Patel K, Durvesh A (2016) Detection of multiple selfish attack nodes in cognitive radio. IJARIIE-ISSN (O)-2395-4396 2(2)Google Scholar
  9. 9.
    Vimal S, Kalaivani L, Kaliappan M (2017) Collaborative approach on mitigating spectrum sensing data hijack attack and dynamic spectrum allocation based on CASG modeling in wireless cognitive radio networks. Clust Comput. CrossRefGoogle Scholar
  10. 10.
    Suresh Annamalai, Reyana A, Varatharajan R (2018) CEMulti-core architecture for optimization of energy over heterogeneous environment with high performance smart sensor devices. Wireless Pers Commun. CrossRefGoogle Scholar
  11. 11.
    Holcomb S, Rawat DB (2016) Recent security issues on cognitive radio networks: a survey. In: Proceedings of Southeast Con, pp 1–6Google Scholar
  12. 12.
    Kim JM, Lee HS, Yi J, Park M (2006) Power adaptive data encryption for energy-efficient and secure communication in solar-powered wireless sensor networks. J Sens vol. 2016, Feb. 2016, Art. no. 2678269. NIST Standards (2001). Advanced Encryption Standard (AES)Google Scholar
  13. 13.
    Subbulakshmi P et al (2017) Honest auction based radio network. Wirel Pers Commun. CrossRefGoogle Scholar
  14. 14.
    Lee P, Eu ZA, Han M, Tan H-P (2011) Empirical modeling of a solar-powered energy harvesting wireless sensor node for time-slotted operation. In: Proceedings of IEEE wireless communications and networking conference, pp 179–184Google Scholar
  15. 15.
    Abdullah HN, Abed HS (2016) Improvement of energy consumption in cognitive radio by reducing the number of sensed samples. In: Proceedings of Al-Sadeq international conference multidisciplinary IT communication science and applications (AIC-MITCSA), pp 1–6Google Scholar
  16. 16.
    Chaudhary A, Dongre M, Patil H (2016) Energy-decisive and upgrade cooperative spectrum sensing in cognitive radio networks. Procedia Comput Sci 79:683–691CrossRefGoogle Scholar
  17. 17.
    Tang Long, Juebo Wu (2012) Research and analysis on cognitive radio network security. Wirel Sensor Netw 4:120–126CrossRefGoogle Scholar
  18. 18.
    Vullers RJM, van Schaijk R, Doms I, van Hoof C, Mertens R (2009) Micropower energy harvesting. Solid-State Electron 53(7):684–693CrossRefGoogle Scholar
  19. 19.
    Amiri (2010) Measurements of energy consumption and execution time of different operations on Tmote Sky sensor nodes. Ph.D. dissertation, Faculty Informatiky, Masaryk Univerzita, Brno, Czech RepublicGoogle Scholar
  20. 20.
    Sen J (2013) A survey on security and privacy protocols for cognitive wireless sensor networks. J Netw Inf Secur 1:1–43Google Scholar
  21. 21.
    Gill RK, Chawla P, Sachdeva M. Study of LEACH routing protocol for wireless sensor networksGoogle Scholar
  22. 22.
    Ilango S et al (2018) Optimization using artificial bee colony based clustering approach for big data. Clust Comput. CrossRefGoogle Scholar
  23. 23.
    Sánchez-Salas DA, Cuevas-Ruíz JL, González-Mendoza M (2012) Wireless channel model with Markov chains using MATLAB. In: MATLAB—a fundamental tool for scientific computing and engineering applications, vol 2. Rijeka, Croatia: InTech, 2012, ch. 11Google Scholar
  24. 24.
    Wu C, Wang Y, Yin Z (2018) Energy-efficiency opportunistic spectrum allocation in cognitive wireless sensor network. EURASIP J Wirel Commun Netw. CrossRefGoogle Scholar
  25. 25.
    Cao XR, Guo X (2007) Partially observable Markov decision processes with reward information: basic ideas and models. IEEE Trans Autom Control 52(4):677–681MathSciNetCrossRefGoogle Scholar
  26. 26.
    Xu X, He B, Yang W, Zhou X, Cai Y (2016) Secure transmission design for cognitive radio networks with poisson distributed eavesdroppers. IEEE Trans Inf Forensics Secur 11(2):373–387CrossRefGoogle Scholar
  27. 27.
    Vimal S et al (2016) Secure data packet transmission in MANET using enhanced identity-based cryptography. Int J New Technol Sci Eng 3(12):35–42Google Scholar
  28. 28.
    Fragkiadakis A, Tragos E, Askoxylakis I (2013) A survey on security threats and detection techniques in cognitive radio networks. Commun Surv Tutor IEEE 15(1):428–445CrossRefGoogle Scholar
  29. 29.
    Chen R, Park J-M, Reed JH (2008) Defense against primary user emulation attacks in cognitive radio networks. IEEE J Sel Areas Commun 26(1)CrossRefGoogle Scholar
  30. 30.
    Madbushi S, Raut R, Rukmini MSS (2018) Trust establishment in chaotic cognitive environment to improve attack detection accuracy under primary user emulation. Iran J Sci Technol Trans Electr Eng 42:291. CrossRefGoogle Scholar
  31. 31.
    Bertsekas DP (2007) Dynamic programming and optimal control, vol 1. Athena Scientific, BelmontzbMATHGoogle Scholar
  32. 32.
    Thylashri S, Femi D, David SA, Suresh A (2018) Vitality and peripatetic sustain cluster key management schemes in MANET. Int J Eng Technol 7(1.7):43–46CrossRefGoogle Scholar
  33. 33.
    Lin S, Li Y, Li Y, Ai B, Zhong Z (2014) Finite-state Markov channel modeling for vehicle-to-infrastructure communications. In: Proceedings of IEEE 6th international symposium wireless vehicular communications (WiVeC), pp 1–5Google Scholar
  34. 34.
    Howa KC, Maa M, Qin Y (2012) An altruistic differentiated service protocol in dynamic cognitive radio networks against selfish behaviors. Comput Netw 56(7):2068–2079CrossRefGoogle Scholar
  35. 35.
    Kannan N, Sivasubramanian S, Kaliappan M, Vimal S, Suresh A (2018) Predictive big data analytic on demonetization data using support vector machine. Clust Comput. CrossRefGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of ITNational Engineering CollegeKovilpattiIndia
  2. 2.Department of EEENational Engineering CollegeKovilpattiIndia
  3. 3.Department of Computer Science and EngineeringNehru Institute of Engineering and TechnologyCoimbatoreIndia
  4. 4.School of ComputingUniversity of Eastern FinlandKuopioFinland
  5. 5.Department of Electronics and Communication EngineeringSri Ramanujar Engineering CollegeKovilpattiIndia

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