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A Machine Learning SDN-Enabled Big Data Model for IoMT System

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A Machine Learning SDN-Enabled Big Data Model for IoMT System

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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
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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