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An Efficient Geographical Opportunistic Routing Algorithm Using Diffusion and Sparse Approximation Models for Cognitive Radio Ad Hoc Networks

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • A. V. Senthil Kumar

    (Hindusthan College of Arts & Science, Bharathiar University, PG and Research Department of Computer Applications)

  • Hesham Mohammed Ali Abdullah

    (Hindusthan College of Arts & Science, Bharathiar University, PG and Research Department of Computer Applications)

  • P. Hemashree

    (Hindusthan College of Arts & Science, Bharathiar University, PG and Research Department of Computer Applications)

Abstract

Spectrum-Map-empowered Opportunistic Routing (SMOR) systems have been created to accomplish dynamic opportunistic links and dependable end-to-end transmission in Cognitive radio ad-hoc networks (CRAHNs). However, only delay has been considered in the mathematical analysis of SMOR in both regular and large-scale networks which results in degraded routing performance. This work examines the transmission delay and the network throughput is evaluated and the relationship between them to develop modified SMOR algorithm by incorporating the concept of acknowledgment (ACK) for each node in the routing link. The Modified SMOR for regular CRAHN utilizes Diffusion approximation based Markov chain modeling and queuing network theory while for large-scale CRAHN utilizes sparse approximation based stochastic geometry and queuing network theory for examining delay and throughput. The Modified SMOR-1 and Modified SMOR-2 are proposed for satisfying the opportunistic routing mechanisms. The experimental results illustrate that the modified SMOR improves the reliability and dynamic routing performance.

Suggested Citation

  • A. V. Senthil Kumar & Hesham Mohammed Ali Abdullah & P. Hemashree, 2020. "An Efficient Geographical Opportunistic Routing Algorithm Using Diffusion and Sparse Approximation Models for Cognitive Radio Ad Hoc Networks," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 323-333, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_30
    DOI: 10.1007/978-3-030-41862-5_30
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