Development of a routing method for ground-air Ad-Hoc network of special purpose

Robert Bieliakov ()
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Robert Bieliakov: Kruty Heroes Military Institute of Telecommunications and Information Technology

Technology audit and production reserves, 2024, vol. 2, issue 2(76), 44-51

Abstract: The object of the study is the process of forming control decisions to ensure the operation of the ground-air communication network routing subsystem based on neural network algorithms. The carried-out research is based on the application of the numerical-analytical approach to the selection of modern scientific and applied solutions for building management models for promising Ad-Hoc communication networks. In the Google Collab simulation environment, using the Python programming language, it was possible: firstly, to simulate the operation of a ground-to-air communication network based on previously obtained models and a routing process management system based on the FA-OSELM algorithm. Secondly, in accordance with the scenario of route construction and maintenance described in the article, to experimentally determine the communication metrics of the proposed method of intelligent routing of the ground-air Ad-Hoc special-purpose network, in order to assess its efficiency, adequacy and reliability of the results obtained. Thus, in order to evaluate the effectiveness of the proposed solutions, a comparative analysis of the application of three existing routing methods (FLCA, Q-Routing, Neuro Routing) used in Ad-Hoc networks relative to the developed method was conducted.The result of the experiment showed that the proposed routing method MAODV-FA-OSELM provides significant advantages over analogs. Thus, the method exhibits the best network throughput (2.12e+06), the lowest average network latency (0.12), the lowest packet loss (6.32), the lowest bit error rate (2.41), and the lowest overhead (0.10e+06). However, it should be noted that a promising direction of further research may be the study of the computational complexity of the routing management process and the determination of the minimum allowable representative sample of initial data to ensure online decision-making.

Keywords: ground-air communication network; neural network; machine learning with reinforcement; routing method; throughput (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:baq:taprar:v:2:y:2024:i:2:p:44-51

DOI: 10.15587/2706-5448.2024.302394

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