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Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment

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dc.contributor.author Najm, Ihab Ahmed es_ES
dc.contributor.author Hamoud, Alaa Khalaf es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.contributor.author Bosch Roig, Ignacio es_ES
dc.date.accessioned 2020-11-27T04:31:14Z
dc.date.available 2020-11-27T04:31:14Z
dc.date.issued 2019-07 es_ES
dc.identifier.uri http://hdl.handle.net/10251/155959
dc.description.abstract [EN] The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-tra_ c networks with multiple nodes/ sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G o_ ers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Machine learning es_ES
dc.subject Decision tree algorithm es_ES
dc.subject IoT es_ES
dc.subject WSN es_ES
dc.subject C4.5 es_ES
dc.subject Congestion control es_ES
dc.subject 5G network es_ES
dc.subject.classification INGENIERIA TELEMATICA es_ES
dc.subject.classification TEORIA DE LA SEÑAL Y COMUNICACIONES es_ES
dc.title Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics8060607 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions es_ES
dc.description.bibliographicCitation Najm, IA.; Hamoud, AK.; Lloret, J.; Bosch Roig, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 8(6):1-23. https://doi.org/10.3390/electronics8060607 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics8060607 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 23 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 6 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\407431 es_ES
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