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International Conference on Computational Intelligence, Communications, and Business Analytics

CICBA 2017: Computational Intelligence, Communications, and Business Analytics pp 382–396Cite as

On Energy Efficient Cooperative Spectrum Sensing Using Possibilistic Fuzzy C-Means Clustering

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 775))

Abstract

Fuzzy c-means (FCM) clustering is extensively used on the energy detection based cooperative spectrum sensing (CSS) to enhance the efficiency of the system. The performance of FCM degrades at low signal-to-noise ratio (SNR) due to the non-spherical energy values at fusion center (FC). The proposed work explores the scope of possibilistic fuzzy c-means (PFCM) algorithm in noisy data set. PFCM combines the fuzzy membership function and the possibilistic information in the clustering process to partition the inseparable data into the respective clusters. The work also investigates the energy consumption by the secondary users (SUs) under the constraint of the predefined primary user (PU) detection threshold. A large set of simulation results illustrate the superiority of the proposed scheme compared to the existing techniques performed in the similar framework.

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Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India

    Anal Paul & Santi P. Maity

Authors
  1. Anal Paul
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  2. Santi P. Maity
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Correspondence to Anal Paul .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Paul, A., Maity, S.P. (2017). On Energy Efficient Cooperative Spectrum Sensing Using Possibilistic Fuzzy C-Means Clustering. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 775. Springer, Singapore. https://doi.org/10.1007/978-981-10-6427-2_31

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  • DOI: https://doi.org/10.1007/978-981-10-6427-2_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6426-5

  • Online ISBN: 978-981-10-6427-2

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