Advertisement

Distributed Algorithms for Learning and Cognitive Medium

  • Anandakumar Haldorai
  • Umamaheswari Kandaswamy
Chapter
Part of the EAI/Springer Innovations in Communication and Computing book series (EAISICC)

Abstract

Distributed algorithms for learning and cognitive medium are evaluated in cognitive radio networks composing of various Secondary Users (SUs). The accessibility data of cognitive channels are unidentified to SUs, hence they are evaluated via the application of detection decisions. Prior agreement or data exchange is unknown between SUs. Hence, this paper proposes principles for distributed learning and cognitive access that attain an orderly optimum cognitive framework throughput in self-play. This implies that the attainment of effective secondary transmission if effected is evaluated at all SUs. Resultantly, the principle given in this research shows a minimal regret in distributed algorithm and access, whereby a scenario when the amount of SUs is identified to the principle proves the overall algorithm regret of the transformation slot. The access principle and distributed learning attain an orderly optimum regret, which can be compared to the asymptotic minimal hurdle for regret beneath any kind of uniformly effective learning and principle cognitive media access. Considering the scenario when the amount of SUs is unidentified, an approximation of delivered via feedback. A proposal of a principle in the scenario whereby its asymptotic evaluation regret develops effectively compared to logarithmic regret amount of transformation slots is given.

Keywords

Distributed learning Logarithmic regret Multi-armed bandits Cognitive media access 

References

  1. 1.
    Pimple, O., Saravane, U., Gavankar, N.: Cognitive learning using distributed artificial intelligence. Int. J. Mach. Learn. Comput. 5(1), 7–11 (2015)CrossRefGoogle Scholar
  2. 2.
    Anandakumar, H., Arulmurugan, R., Onn, C.C.: Computational intelligence and sustainable systems. In: EAI/Springer Innovations in Communication and Computing (2019)Google Scholar
  3. 3.
    Suganya, M., Anandakumar, H.: Handover based spectrum allocation in cognitive radio networks. In: 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), Chennai, pp. 215–219 (2013)Google Scholar
  4. 4.
    Anandkumar, A., Michael, N., Tang, A., Swami, A.: Distributed algorithms for learning and cognitive medium access with logarithmic regret. IEEE J. Select. Areas Commun. 29(4), 731–745 (2011)CrossRefGoogle Scholar
  5. 5.
    Anandakumar, H., Umamaheswari, K.: Energy efficient network selection using 802.16g based GSM technology. J. Comput. Sci. 10(5), 745–754 (2014)CrossRefGoogle Scholar
  6. 6.
    Bonawitz, E., Denison, S., Griffiths, T., Gopnik, A.: Probabilistic models, learning algorithms, and response variability: sampling in cognitive development. Trends Cogn. Sci. 18(10), 497–500 (2014)CrossRefGoogle Scholar
  7. 7.
    Lan, Y., Cui, Z.: ILC with initial state learning for fractional order linear distributed parameter systems. Algorithms. 11(6), 85 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Czarnowski, I.: Prototype selection algorithms for distributed learning. Pattern Recogn. 43(6), 2292–2300 (2010)CrossRefGoogle Scholar
  9. 9.
    Guo, Z., Lin, S., Zhou, D.: Learning theory of distributed spectral algorithms. Inverse Probl. 33(7), 074009 (2017)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Heersmink, R., Knight, S.: Distributed learning: educating and assessing extended cognitive systems. Philos. Psychol. 31(6), 969–990 (2018)CrossRefGoogle Scholar
  11. 11.
    Pickett, M., Aha, D.: Using cortically-inspired algorithms for analogical learning and reasoning. Biol. Inspired Cognit. Architect. 6, 76–86 (2013)CrossRefGoogle Scholar
  12. 12.
    Nedjah, N., Macedo Mourelle, L.: Distributed learning algorithms for swarm robotics. Neurocomputing. 290–291 (2016)CrossRefGoogle Scholar
  13. 13.
    Perlovsky, L., Kuvich, G.: Machine learning and cognitive algorithms for engineering applications. Int. J. Cognit. Inform. Nat. Intell. 7(4), 64–82 (2013)CrossRefGoogle Scholar
  14. 14.
    Al-Harthi, Y., Borst, S., Whiting, P.: Distributed adaptive algorithms for optimal opportunistic medium access. Mob. Netw. Appl. 16(2), 217–230 (2010)CrossRefGoogle Scholar
  15. 15.
    Anandakumar, H., Umamaheswari, K.: A bio-inspired swarm intelligence technique for social aware cognitive radio handovers. Comput. Electr. Eng. 71, 925–937 (2018)CrossRefGoogle Scholar
  16. 16.
    Anandakumar, H., Umamaheswari, K.: Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Clust. Comput. 20(2), 1505–1515 (2017)CrossRefGoogle Scholar
  17. 17.
    Anandakumar, H., Umamaheswari, K.: Cooperative spectrum handovers in cognitive radio networks. In: EAI/Springer Innovations in Communication and Computing, pp. 47–63 (2018)Google Scholar
  18. 18.
    Haldorai, A., Ramu, A.: Cognitive social mining applications in data analytics and forensics. In: Advances in Social Networking and Online Communities (2019)Google Scholar
  19. 19.
    Wu, C.H.J., Tsai, J.H.: Concurrent asynchronous learning algorithms for massively parallel recurrent neural networks. J. Parall. Distribut. Comput. 14(3), 345–353 (1992)CrossRefGoogle Scholar
  20. 20.
    Kochen, M.: Representations and algorithms for cognitive learning. Artif. Intell. 5(3), 199–216 (1974)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Gorsky, P.: Toward a unified theory of instruction in the cognitive domain. Int. Rev. Res. Open Distribut. Learn. (2007)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anandakumar Haldorai
    • 1
  • Umamaheswari Kandaswamy
    • 2
  1. 1.Department of Computer Science and EngineeringSri Eshwar College of EngineeringCoimbatoreIndia
  2. 2.Department of Information TechnologyPSG College of TechnologyCoimbatoreIndia

Personalised recommendations