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Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing

Author

Listed:
  • Krzysztof Przystupa

    (Department of Automation, Lublin University of Technology, 20-618 Lublin, Poland)

  • Julia Pyrih

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Mykola Beshley

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Mykhailo Klymash

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Andriy Branytskyy

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Halyna Beshley

    (Department of Telecommunications, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Daniel Pieniak

    (Department of Mechanics and Machine Building, University of Economics and Innovations in Lublin, 20-209 Lublin, Poland)

  • Konrad Gauda

    (Department of Mechanics and Machine Building, University of Economics and Innovations in Lublin, 20-209 Lublin, Poland)

Abstract

With the constant growth of requirements to the quality of infocommunication services, special attention is paid to the management of information transfer in wireless self-organizing networks. The clustering algorithm based on the Motley signal propagation model has been improved, resulting in cluster formation based on the criterion of shortest distance and maximum signal power value. It is shown that the use of the improved clustering algorithm compared to its classical version is more efficient for the route search process. Ant and simulated annealing algorithms are presented to perform route search in a wireless sensor network based on the value of the quality of service parameter. A comprehensive routing method based on finding the global extremum of an ordered random search with node addition/removal is proposed by using the presented ant and simulated annealing algorithms. It is shown that the integration of the proposed clustering and routing solutions can reduce the route search duration up to two times.

Suggested Citation

  • Krzysztof Przystupa & Julia Pyrih & Mykola Beshley & Mykhailo Klymash & Andriy Branytskyy & Halyna Beshley & Daniel Pieniak & Konrad Gauda, 2021. "Improving the Efficiency of Information Flow Routing in Wireless Self-Organizing Networks Based on Natural Computing," Energies, MDPI, vol. 14(8), pages 1-24, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2255-:d:538002
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    References listed on IDEAS

    as
    1. Yakubu Tsado & Kelum A. A. Gamage & Bamidele Adebisi & David Lund & Khaled M. Rabie & Augustine Ikpehai, 2017. "Improving the Reliability of Optimised Link State Routing in a Smart Grid Neighbour Area Network based Wireless Mesh Network Using Multiple Metrics," Energies, MDPI, vol. 10(3), pages 1-23, February.
    2. Carolina Del-Valle-Soto & Carlos Mex-Perera & Juan Arturo Nolazco-Flores & Ramiro Velázquez & Alberto Rossa-Sierra, 2020. "Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols," Energies, MDPI, vol. 13(3), pages 1-33, February.
    3. Bishnu Nepal & Motoi Yamaha & Hiroya Sahashi & Aya Yokoe, 2019. "Analysis of Building Electricity Use Pattern Using K-Means Clustering Algorithm by Determination of Better Initial Centroids and Number of Clusters," Energies, MDPI, vol. 12(12), pages 1-17, June.
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