Fog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring

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dc.contributor.authorYıldırım, Emre
dc.contributor.authorCicioğlu, Murtaza
dc.contributor.authorÇalhan, Ali
dc.date.accessioned2023-07-26T11:55:11Z
dc.date.available2023-07-26T11:55:11Z
dc.date.issued2023
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe new coronavirus disease (COVID-19) has increased the need for new technologies such as the Internet of Medical Things (IoMT), Wireless Body Area Networks (WBANs), and cloud computing in the health sector as well as in many areas. These technologies have also made it possible for billions of devices to connect to the internet and communicate with each other. In this study, an Internet of Medical Things (IoMT) framework consisting of Wireless Body Area Networks (WBANs) has been designed and the health big data from WBANs have been analyzed using fog and cloud computing technologies. Fog computing is used for fast and easy analysis, and cloud computing is used for time-consuming and complex analysis. The proposed IoMT framework is presented with a diabetes prediction scenario. The diabetes prediction process is carried out on fog with fuzzy logic decision-making and is achieved on cloud with support vector machine (SVM), random forest (RF), and artificial neural network (ANN) as machine learning algorithms. The dataset produced in WBANs is used for big data analysis in the scenario for both fuzzy logic and machine learning algorithm. The fuzzy logic gives 64% accuracy performance in fog and SVM, RF, and ANN have 89.5%, 88.4%, and 87.2% accuracy performance respectively in the cloud for diabetes prediction. In addition, the throughput and delay results of heterogeneous nodes with different priorities in the WBAN scenario created using the IEEE 802.15.6 standard and AODV routing protocol have been also analyzed.en_US
dc.identifier.doi10.1007/s11517-023-02776-4
dc.identifier.issn0140-0118
dc.identifier.issn1741-0444
dc.identifier.pmid36670240en_US
dc.identifier.scopus2-s2.0-85146558371en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s11517-023-02776-4
dc.identifier.urihttps://hdl.handle.net/20.500.12684/13018
dc.identifier.wosWOS:000920520600001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorÇalhan, Ali
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofMedical & Biological Engineering & Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectCloud Computing; Fog Computing; Iomt; Wbans; Data Analytics; Machine Learningen_US
dc.subjectDiagnosis; Systemen_US
dc.titleFog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoringen_US
dc.typeArticleen_US

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