Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Guo,Yanmin | - |
dc.contributor.author | Wang,Yu | - |
dc.contributor.author | Khan,Faheem | - |
dc.contributor.author | Al-Atawi,Abdullah A. | - |
dc.contributor.author | Abdulwahid ,Abdulwahid Al | - |
dc.contributor.author | Lee,Youngmoon | - |
dc.contributor.author | Marapelli, Bhaskar | - |
dc.date.accessioned | 2023-08-16T07:32:03Z | - |
dc.date.available | 2023-08-16T07:32:03Z | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113832 | - |
dc.description.abstract | Traffic management is a critical task in software-defined IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic management approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and multi-arm bandit algorithms to dynamically optimize traffic management policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a multi-arm bandit algorithm to optimize traffic management policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic management methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic management in SDNs, enabling efficient resource management and QoS assurance. | - |
dc.format.extent | 1 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration | - |
dc.type | Article | - |
dc.publisher.location | 스위스 | - |
dc.identifier.doi | 10.3390/s23167091 | - |
dc.identifier.scopusid | 2-s2.0-85168717126 | - |
dc.identifier.wosid | 001055790200001 | - |
dc.identifier.bibliographicCitation | Sensors, v.23, no.16, pp 7091 - 7091 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 23 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 7091 | - |
dc.citation.endPage | 7091 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.subject.keywordAuthor | traffic management | - |
dc.subject.keywordAuthor | anomaly detection | - |
dc.subject.keywordAuthor | intrusion detection | - |
dc.subject.keywordAuthor | network security | - |
dc.subject.keywordAuthor | internet of things | - |
dc.subject.keywordAuthor | network traffic analysis | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | SDN (software-defined networking) | - |
dc.subject.keywordAuthor | GNN (graph neural network) | - |
dc.subject.keywordAuthor | MAB (multi-armed bandit) | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/23/16/7091 | - |
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