IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2255-d538002.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2255/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2255/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paweł Dymora & Mirosław Mazurek & Krzysztof Smalara, 2021. "Modeling and Fault Tolerance Analysis of ZigBee Protocol in IoT Networks," Energies, MDPI, vol. 14(24), pages 1-21, December.
    2. Mohammad Reza Ghaderi & Vahid Tabataba Vakili & Mansour Sheikhan, 2021. "Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 83-108, May.
    3. Bandeiras, F. & Pinheiro, E. & Gomes, M. & Coelho, P. & Fernandes, J., 2020. "Review of the cooperation and operation of microgrid clusters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    4. Piotr Arabas & Andrzej Sikora & Wojciech Szynkiewicz, 2021. "Energy-Aware Activity Control for Wireless Sensing Infrastructure Using Periodic Communication and Mixed-Integer Programming," Energies, MDPI, vol. 14(16), pages 1-17, August.
    5. Ala’ Khalifeh & Mai Saadeh & Khalid A. Darabkh & Prabagarane Nagaradjane, 2021. "Radio Frequency Based Wireless Charging for Unsupervised Clustered WSN: System Implementation and Experimental Evaluation," Energies, MDPI, vol. 14(7), pages 1-21, March.
    6. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    7. Douglas de Farias Medeiros & Cleonilson Protasio de Souza & Fabricio Braga Soares de Carvalho & Waslon Terllizzie Araújo Lopes, 2022. "Energy-Saving Routing Protocols for Smart Cities," Energies, MDPI, vol. 15(19), pages 1-19, October.
    8. Jian Yang & Yu Liu & Shangguang Jiang & Yazhou Luo & Nianzhang Liu & Deping Ke, 2022. "A Method of Probability Distribution Modeling of Multi-Dimensional Conditions for Wind Power Forecast Error Based on MNSGA-II-Kmeans," Energies, MDPI, vol. 15(7), pages 1-21, March.
    9. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2255-:d:538002. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.