Evaluating machine learning-based routing algorithms on various wireless network topologies
Fragment książki (Rozdział monografii pokonferencyjnej)
MNiSW
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Poziom I
| Status: | |
| Autorzy: | Turlykozhayeva Dana, Akhtanov Sауаt, Zhexebay Dauren, Ussipov Nurzhan, Boranbayeva Narkez, Sawicki Daniel |
| Dyscypliny: | |
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| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Strony: | 1 - 12 |
| Scopus® Cytowania: | 0 |
| Bazy: | Scopus |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | TAK |
| Nazwa konferencji: | Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2025 |
| Skrócona nazwa konferencji: | SPIE-IEEE-PSP 2025 |
| URL serii konferencji: | LINK |
| Termin konferencji: | 3 lipca 2025 do 4 lipca 2025 |
| Miasto konferencji: | Lublin |
| Państwo konferencji: | POLSKA |
| Publikacja OA: | NIE |
| Abstrakty: | angielski |
| Wireless networks are crucial to modern communication infrastructure, supporting a wide range of applications from personal use to industrial operations. The rapid growth of mobile devices, IoT, and advanced technologies like 5G has heightened the demand for scalable, efficient, and reliable wireless networks. These networks come in various types, including ad hoc, cellular, and sensor networks, each with distinct topologies such as Random, Binomial Tree, and Regular Grid. Routing in wireless networks is particularly complex due to the dynamic nature of the medium, device mobility, and diverse topologies. Traditional routing algorithms, though effective in some cases, frequently fail to handle the complexities of contemporary wireless networks. Integrating machine learning technologies with routing algorithms offers a promising solution. In particular, reinforcement learning (RL) has become prevalent in routing due to its ability to operate without the need for extensive datasets, unlike supervised and unsupervised learning methods. This article evaluates two RL-based routing algorithms, Q-Routing and CQ-Routing, by comparing their average delivery times under various loads across Random, Binomial Tree, and Regular Grid topologies. This study offers a comprehensive analysis that addresses the importance of different network structures, providing insights into how each algorithm performs across diverse topologies, an aspect not covered in previous surveys. |
