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 | Al Abdulwahid, Abdulwahid | - |
dc.contributor.author | Lee, Youngmoon | - |
dc.contributor.author | Marapelli, Bhaskar | - |
dc.date.accessioned | 2023-09-15T15:40:50Z | - |
dc.date.available | 2023-09-15T15:40:50Z | - |
dc.date.created | 2023-09-15 | - |
dc.date.issued | 2023-08 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89101 | - |
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.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.relation.isPartOf | SENSORS | - |
dc.title | Traffic Management in IoT Backbone Networks Using GNN and MAB with SDN Orchestration | - |
dc.type | Article | - |
dc.type.rims | ART | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 001055790200001 | - |
dc.identifier.doi | 10.3390/s23167091 | - |
dc.identifier.bibliographicCitation | SENSORS, v.23, no.16 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.scopusid | 2-s2.0-85168717126 | - |
dc.citation.title | SENSORS | - |
dc.citation.volume | 23 | - |
dc.citation.number | 16 | - |
dc.contributor.affiliatedAuthor | Khan, Faheem | - |
dc.type.docType | Article | - |
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.subject.keywordPlus | SOFTWARE-DEFINED NETWORKS | - |
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.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.