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dc.contributor.author	Haseeb, Khalid	es_ES
dc.contributor.author	Ahmad, Irshad	es_ES
dc.contributor.author	Iqbal Awan, Israr	es_ES
dc.contributor.author	Lloret, Jaime	es_ES
dc.contributor.author	Bosch Roig, Ignacio	es_ES
dc.date.accessioned	2022-06-01T18:07:21Z
dc.date.available	2022-06-01T18:07:21Z
dc.date.issued	2021-09	es_ES
dc.identifier.uri	http://hdl.handle.net/10251/183044
dc.description.abstract	[EN] In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.	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	Software-defined network	es_ES
dc.subject	Machine learning	es_ES
dc.subject	Internet of things	es_ES
dc.subject	Routing algorithm	es_ES
dc.subject	Network resources	es_ES
dc.subject.classification	INGENIERIA TELEMATICA	es_ES
dc.subject.classification	TEORIA DE LA SEÑAL Y COMUNICACIONES	es_ES
dc.title	A Machine Learning SDN-Enabled Big Data Model for IoMT System	es_ES
dc.type	Artículo	es_ES
dc.identifier.doi	10.3390/electronics10182228	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	Haseeb, K.; Ahmad, I.; Iqbal Awan, I.; Lloret, J.; Bosch Roig, I. (2021). A Machine Learning SDN-Enabled Big Data Model for IoMT System. Electronics. 10(18):1-13. https://doi.org/10.3390/electronics10182228	es_ES
dc.description.accrualMethod	S	es_ES
dc.relation.publisherversion	https://doi.org/10.3390/electronics10182228	es_ES
dc.description.upvformatpinicio	1	es_ES
dc.description.upvformatpfin	13	es_ES
dc.type.version	info:eu-repo/semantics/publishedVersion	es_ES
dc.description.volume	10	es_ES
dc.description.issue	18	es_ES
dc.identifier.eissn	2079-9292	es_ES
dc.relation.pasarela	S\453028	es_ES
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dc.subject.ods	03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades	es_ES
dc.subject.ods	07.- Asegurar el acceso a energías asequibles, fiables, sostenibles y modernas para todos	es_ES
dc.subject.ods	09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación	es_ES
dc.subject.ods	12.- Garantizar las pautas de consumo y de producción sostenibles	es_ES
upv.costeAPC	1150	es_ES

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