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dc.contributor.author | Rehman, Amjad | es_ES |
dc.contributor.author | Saba, Tanzila | es_ES |
dc.contributor.author | Haseeb, Khalid | es_ES |
dc.contributor.author | Alam, Teg | es_ES |
dc.contributor.author | Lloret, Jaime | es_ES |
dc.date.accessioned | 2024-01-11T19:02:51Z | |
dc.date.available | 2024-01-11T19:02:51Z | |
dc.date.issued | 2022-10 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/201832 | |
dc.description.abstract | [EN] In wireless multimedia networks, the Internet of Things (IoT) and visual sensors are used to interpret and exchange vast data in the form of images. The digital images are subsequently delivered to cloud systems via a sink node, where they are interacted with by smart communication systems using physical devices. Visual sensors are becoming a more significant part of digital systems and can help us live in a more intelligent world. However, for IoT-based data analytics, optimizing communications overhead by balancing the usage of energy and bandwidth resources is a new research challenge. Furthermore, protecting the IoT network's data from anonymous attackers is critical. As a result, utilizing machine learning, this study proposes a mobile edge computing model with a secured cloud (MEC-Seccloud) for a sustainable Internet of Health Things (IoHT), providing real-time quality of service (QoS) for big data analytics while maintaining the integrity of green technologies. We investigate a reinforcement learning optimization technique to enable sensor interaction by examining metaheuristic methods and optimally transferring health-related information with the interaction of mobile edges. Furthermore, two-phase encryptions are used to guarantee data concealment and to provide secured wireless connectivity with cloud networks. The proposed model has shown considerable performance for various network metrics compared with earlier studies. | es_ES |
dc.description.sponsorship | This work has been partially funded by the "La Fundacion para el Fomento de la Investigacion Sanitaria y Biomedica de la Comunitat Valenciana (Fisabio)" through the project PULSIDATA (A43). This research is supported by the Artificial Intelligence & Data Analytics Lab (AIDA), CCIS Prince Sultan University, Riyadh, Saudi Arabia. The authors are thankful for technical support. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sustainability | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Data analytics,machine learning,internet of health things,sustainable network,security,data hiding,healthcare | es_ES |
dc.subject.classification | INGENIERÍA TELEMÁTICA | es_ES |
dc.title | Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/su141912185 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/FISABIO//PULSIDATA (A43)/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Rehman, A.; Saba, T.; Haseeb, K.; Alam, T.; Lloret, J. (2022). Sustainability Model for the Internet of Health Things (IoHT) Using Reinforcement Learning with Mobile Edge Secured Services. Sustainability. 14(19):1-14. https://doi.org/10.3390/su141912185 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/su141912185 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 14 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 14 | es_ES |
dc.description.issue | 19 | es_ES |
dc.identifier.eissn | 2071-1050 | es_ES |
dc.relation.pasarela | S\506769 | es_ES |
dc.contributor.funder | Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana | es_ES |