Decision tree-based task offloading in vehicle edge computing

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dc.authoridTAY, Muhammet/0000-0001-5569-7886en_US
dc.authorscopusid57236203000en_US
dc.authorscopusid15123190100en_US
dc.contributor.authorTay, Muhammet
dc.contributor.authorSenturk, Arafat
dc.date.accessioned2024-08-23T16:07:18Z
dc.date.available2024-08-23T16:07:18Z
dc.date.issued2024en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractThere are significant developments in the Internet of Vehicles (IoV) field, and the requirements needed in this area are increasing rapidly. When these needs are examined in the near future, it appears that the demand for connected, autonomous, shared, and electric vehicles will increase. Therefore, fundamental problems such as big data flow and storage will arise in the IoV field. Another problem is the delay sensitivity of IoVs and the need to minimize data loss. The use of edge computing (EC) tools can play an important role in obtaining effective solutions to overcome these problems. Delay, bandwidth, and energy consumption rate, which are important qualities in EC systems, emerge as a problem that needs to be improved for delay-sensitive systems. These improvements belong to the category of nonlinear challenging problems. Effective optimization or machine learning methods can be used to improve these types of problems. In this study, a two-stage machine learning method is proposed for a more efficient task completion rate and service time. According to the proposed method, in the first stage, the decision tree algorithm is used to select the computing tool to which the task will be sent, and the decision is made on which computing tool to send it to. In the second stage, a linear regression-based classification method is used to select a delay-sensitive computing tool. The performance analysis of the proposed method was made using the edgeCloudSim simulation tool, and according to the results obtained, the proposed method provides better results than other algorithms in the literature.en_US
dc.identifier.doi10.1002/cpe.8026
dc.identifier.issn1532-0626
dc.identifier.issn1532-0634
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85184241217en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.urihttps://doi.org/10.1002/cpe.8026
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14583
dc.identifier.volume36en_US
dc.identifier.wosWOS:001153787700001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.relation.ispartofConcurrency and Computation-Practice & Experienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectcloud computingen_US
dc.subjectdecision treeen_US
dc.subjectedge computingen_US
dc.subjecttask offloadingen_US
dc.subjectInterneten_US
dc.titleDecision tree-based task offloading in vehicle edge computingen_US
dc.typeArticleen_US

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