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

Krzysztof Przystupa, Julia Pyrih, Mykola Beshley, Mykhailo Klymash, Andriy Branytskyy, Halyna Beshley, Daniel Pieniak and Konrad Gauda
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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

Energies, 2021, vol. 14, issue 8, 1-24

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.

Keywords: ant algorithm; simulated annealing; k-means; signal-to-noise ratio (SNR) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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