mlCoCoA: a machine learning-based congestion control for CoAP

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dc.authoridAbut, Fatih/0000-0001-5876-4116
dc.contributor.authorDemir, Alper Kamil
dc.contributor.authorAbut, Fatih
dc.date.accessioned2025-01-06T17:43:20Z
dc.date.available2025-01-06T17:43:20Z
dc.date.issued2020
dc.description.abstractInternet of Things (IoT) is a technological invention that has the potential to impact on how we live and how we work by connecting any device to the Internet. Consequently, a vast amount of novel applications will enhance our lives. Internet Engineering Task Force (IETF) standardized the Constrained Application Protocol (CoAP) to accommodate the application layer and network congestion needs of such IoT networks. CoAP is designed to be very simple where it employs a genuine congestion control (CC) mechanism, named as default CoAP CC leveraging basic binary exponential backoff. Yet efficient, default CoAP CC does not always utilize the network dynamics the best. As a result, CoCoA has been exposed to better utilize the IoT networks. Although CoCoA considers the network dynamics, the RTO calculation of CoCoA is based on constant coefficient values. However, our experiments show that these constant values, in general, do not achieve the best throughput. Inspired by these observations, we propose a new machine learning-based CC mechanism called as mlCoCoA that is a variation of CoCoA. Particularly, mlCoCoA sets retransmission timeout (RTO) estimation parameters of CoCoA adaptively by using a machine learning method. In this study, we applied support vector machines on a self-created dataset to develop new models for improving the throughput of the IoT network with dynamic selection of CoCoA coefficient values. We carried out extensive simulations in Cooja environment coupled with Californium. Our results indicate that compared to the performance of default CoAP CC and CoCoA mechanisms, mlCoCoA has merit in terms of improving the throughput of CoAP applications.
dc.identifier.doi10.3906/elk-2003-17
dc.identifier.endpage2882
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85095727730
dc.identifier.scopusqualityQ2
dc.identifier.startpage2863
dc.identifier.trdizinid514063
dc.identifier.urihttps://doi.org/10.3906/elk-2003-17
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/514063
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2625
dc.identifier.volume28
dc.identifier.wosWOS:000576681600001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectInternet of Things
dc.subjectCoAP
dc.subjectCoCoA
dc.subjectcongestion control
dc.subjectmachine learning
dc.titlemlCoCoA: a machine learning-based congestion control for CoAP
dc.typeArticle

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