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A Bio-Inspired-Based Degree Constrained MST Algorithm for Cognitive Radio Networks

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Information and Communication Technology for Competitive Strategies (ICTCS 2020)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 191))

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Abstract

Cognitive radio network (CRN) has been observed as one of the most emerging technologies since last one decade and identified as a natural extension of wireless networks. The CRN consists of secondary user nodes (SUs) interconnected using spectrum holes in environment. In wireless networks, especially in distributed systems, constructing minimum spanning tree (MST) has been experienced as a classical problem. In this, finding degree constraint spanning tree is a well-known solution approach to construct MST. We intend to propose similar works to create MST for CRN using bio-inspired method. In our problem, we restrict every SU node to have a maximum of degree “d.” We used the genetic algorithm along with ant colony optimization to solve the problem. It is the first algorithm of its kind to the best of our knowledge.

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Authors and Affiliations

  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, India

    Deepesh Vanwani & Mahendra Kumar Murmu

Authors
  1. Deepesh Vanwani
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  2. Mahendra Kumar Murmu
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Editor information

Editors and Affiliations

  1. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

  2. Computing and Technology, Nottingham Trent University, Nottingham, Nottinghamshire, UK

    Mufti Mahmud

  3. University of Peradeniya, Kandy, Sri Lanka

    Roshan G. Ragel

  4. Prof Ram Meghe College of Engineering and Management, Amravati, India

    Nileshsingh V. Thakur

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Vanwani, D., Murmu, M.K. (2022). A Bio-Inspired-Based Degree Constrained MST Algorithm for Cognitive Radio Networks. In: Joshi, A., Mahmud, M., Ragel, R.G., Thakur, N.V. (eds) Information and Communication Technology for Competitive Strategies (ICTCS 2020). Lecture Notes in Networks and Systems, vol 191. Springer, Singapore. https://doi.org/10.1007/978-981-16-0739-4_1

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