Publication:
The Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbed

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dc.citedby0
dc.contributor.authorRidwan M.A.en_US
dc.contributor.authorRadzi N.A.M.en_US
dc.contributor.authorAzmi K.H.M.en_US
dc.contributor.authorAhmad A.en_US
dc.contributor.authorAbdullah F.en_US
dc.contributor.authorAhmad W.S.H.M.W.en_US
dc.contributor.authorid57193648099en_US
dc.contributor.authorid57218936786en_US
dc.contributor.authorid57982272200en_US
dc.contributor.authorid26967455300en_US
dc.contributor.authorid56613644500en_US
dc.contributor.authorid58032416800en_US
dc.date.accessioned2024-10-14T03:19:54Z
dc.date.available2024-10-14T03:19:54Z
dc.date.issued2023
dc.description.abstractHigh quality of service (QoS) requires monitoring and controlling parameters such as delay and throughput. Due to network complexity, conventional QoS-improving routing algorithms (RAs) may be impractical. Thus, researchers are developing intelligent RAs, including machine learning (ML)-based algorithms to meet traffic Q oS r equirements. However, most current studies evaluate performance using simulations. Validation requires real-world environment studies, but lab-scale testbed studies are limited. Therefore, we proposed an ML-based RA (ML-RA-t) to improve delay and throughput, evaluated using simulation and a lab-scale testbed. The results show that ML-RA-t predicted the fastest route as compared to RIPv2 routing protocol in simulation and testbed. � 2023 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ISWTA58588.2023.10250137
dc.identifier.epage34
dc.identifier.scopus2-s2.0-85174320020
dc.identifier.spage29
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85174320020&doi=10.1109%2fISWTA58588.2023.10250137&partnerID=40&md5=c112e89aa1b5d0c133c24d9b64a1482d
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34456
dc.identifier.volume2023-August
dc.pagecount5
dc.publisherIEEE Computer Societyen_US
dc.sourceScopus
dc.sourcetitleIEEE Symposium on Wireless Technology and Applications, ISWTA
dc.subjectmachine learning
dc.subjectQoS
dc.subjectrouting algorithm
dc.subjectsimulation
dc.subjecttestbed
dc.subjectLearning algorithms
dc.subjectMachine learning
dc.subjectQuality control
dc.subjectQuality of service
dc.subjectTestbeds
dc.subjectControlling parameters
dc.subjectHigh quality
dc.subjectIntelligent routing algorithm
dc.subjectLearning-based algorithms
dc.subjectMachine-learning
dc.subjectMonitoring and controlling
dc.subjectMonitoring parameters
dc.subjectNetwork complexity
dc.subjectQuality-of-service
dc.subjectSimulation
dc.subjectRouting algorithms
dc.titleThe Implementation of a Machine Learning-based Routing Algorithm in a Lab-Scale Testbeden_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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