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Restricted Boltzmann Machine Based Energy Efficient Cognitive Network

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Innovations in Bio-Inspired Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 424))

Abstract

Current network technology is statically configured and it is difficult to self-adjust changes on demand. Existing protocols react for situations but it cannot take intelligent decisions. Emerging cognitive network plays a key role in networking environment because of its unique features namely reasoning and decision making. Energy efficiency is highly desirable for effective data communication network. In this paper, energy aware routing protocols and trust based metrics for improving energy efficiency is addressed. The proposed method uses Restricted Boltzmann Machine to stabilize the energy level of the network during routing. RBM based routing is comparatively better than conventional Boltzmann Machine based routing in terms of self-learning the trust metrics. The performance graph shows that the proposed RBM based routing has achieved lesser energy level consumption and higher trust values which ensures effective cognitive approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, 625 015, Tamilnadu, India

    P. MohanaPriya & S. Mercy Shalinie

  2. Department of Electronics and Information Technology (DeitY), Ministry of Communications & IT, Government of India, New Delhi, India

    Tulika Pandey

Authors
  1. P. MohanaPriya
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  2. S. Mercy Shalinie
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  3. Tulika Pandey
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Corresponding authors

Correspondence to P. MohanaPriya , S. Mercy Shalinie or Tulika Pandey .

Editor information

Editors and Affiliations

  1. Dep. of Computer Science, VŠB – Technical Univ. of Ostrava, Ostrava, Czech Republic

    Václav Snášel

  2. (MIR Labs), Scientific Net Innov & Res Excel, Auburn, Washington, USA

    Ajit Abraham

  3. Faculty of Elec. Eng. & Comp. Sci., VÅ B - Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Pavel Krömer

  4. Department of Paper Technology, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Millie Pant

  5. Fac of Info & Comm, Comp Inte & Tech Lab, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Malaysia

    Azah Kamilah Muda

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© 2016 Springer International Publishing Switzerland

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MohanaPriya, P., Shalinie, S.M., Pandey, T. (2016). Restricted Boltzmann Machine Based Energy Efficient Cognitive Network. In: Snášel, V., Abraham, A., Krömer, P., Pant, M., Muda, A. (eds) Innovations in Bio-Inspired Computing and Applications. Advances in Intelligent Systems and Computing, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-28031-8_40

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  • DOI: https://doi.org/10.1007/978-3-319-28031-8_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28030-1

  • Online ISBN: 978-3-319-28031-8

  • eBook Packages: EngineeringEngineering (R0)

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