MLaR: machine-learning-assisted centralized link-state routing in software-defined-based wireless networks

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dc.authoridÇalhan, Ali/0000-0002-5798-3103
dc.authoridCİCİOĞLU, MURTAZA/0000-0002-5657-7402
dc.authorwosidÇalhan, Ali/H-1375-2014
dc.authorwosidCİCİOĞLU, MURTAZA/AAL-5004-2020
dc.contributor.authorCicioğlu, Murtaza
dc.contributor.authorÇalhan, Ali
dc.date.accessioned2023-07-26T11:58:01Z
dc.date.available2023-07-26T11:58:01Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractSoftware-defined networking (SDN) is a flexible networking paradigm that provides isolation of control and data planes from each other, proposes control mechanisms, network programmability and autonomy, and new tools for developing solutions to traditional network infrastructure problems such as latency, throughput, and packet loss losses. One of the most important critical issues that evaluated by SDN offers is the hardware and vendor-independent software for routing protocols in wireless communication. Therefore, using the SDN approach to run, manage and optimize routing algorithms efficiently has become one of the important topics. The SDN also makes it possible to use machine learning techniques for routing. In this study, a new machine learning-assisted routing (MLaR) algorithm is proposed for software-defined wireless networks. Through the trained model, this algorithm can make the most appropriate routing decision in real-time by using the historical network parameters of mobile nodes (latency, bandwidth, SNR, distance). This way, a learning the proposed routing algorithm that can adjust itself according to dynamic network conditions has been developed. The proposed MLaR algorithm is compared with the traditional Dijkstra algorithm in terms of delay and throughput ratio, and the MLaR gives more successful results. According to the simulation results, the proposed approach achieved 3.1 and 1.3 times improvement in delay and throughput, respectively, compared to the traditional Dijkstra.en_US
dc.identifier.doi10.1007/s00521-022-07993-w
dc.identifier.endpage5420en_US
dc.identifier.issn0941-0643
dc.identifier.issn1433-3058
dc.identifier.issue7en_US
dc.identifier.scopus2-s2.0-85141178953en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5409en_US
dc.identifier.urihttps://doi.org/10.1007/s00521-022-07993-w
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13374
dc.identifier.volume35en_US
dc.identifier.wosWOS:000878478600002en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇalhan, Ali
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofNeural Computing & Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectSoftware-Defined Networking; Internet Of Things; Routing; Machine Learningen_US
dc.subjectManagementen_US
dc.titleMLaR: machine-learning-assisted centralized link-state routing in software-defined-based wireless networksen_US
dc.typeArticleen_US

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