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

dc.contributor.authorYıldırm, Emre
dc.contributor.authorCicioğlu, Murtaza
dc.contributor.authorCalhan, Ali
dc.contributor.buuauthorCİCİOĞLU, MURTAZA
dc.contributor.departmentMühendislik Fakültesi
dc.contributor.departmentBilgisayar Mühendisliği Bölümü
dc.contributor.researcheridAAL-5004-2020
dc.date.accessioned2025-01-08T10:12:23Z
dc.date.available2025-01-08T10:12:23Z
dc.date.issued2023-01-21
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.
dc.identifier.doi10.1007/s11517-023-02776-4
dc.identifier.eissn1741-0444
dc.identifier.endpage1147
dc.identifier.issn0140-0118
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85146558371
dc.identifier.startpage1133
dc.identifier.urihttps://link.springer.com/article/10.1007/s11517-023-02776-4
dc.identifier.urihttps://pmc.ncbi.nlm.nih.gov/articles/PMC9859747/
dc.identifier.urihttps://hdl.handle.net/11452/49376
dc.identifier.volume61
dc.identifier.wos000920520600001
dc.indexed.wosWOS.SCI
dc.language.isoen
dc.publisherSpringer
dc.relation.journalMedical Biological Engineering Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectDiagnosis
dc.subjectSystem
dc.subjectCloud computing
dc.subjectFog computing
dc.subjectIoMT
dc.subjectWBANs
dc.subjectData analytics
dc.subjectMachine learning
dc.subjectScience & technology
dc.subjectTechnology
dc.subjectLife sciences & biomedicine
dc.subjectComputer science, interdisciplinary applications
dc.subjectEngineering, biomedical
dc.subjectMathematical & computational biology
dc.subjectMedical informatics
dc.subjectComputer science
dc.subjectEngineering
dc.subjectMathematical computational biology
dc.subjectMedical informatics
dc.titleFog-cloud architecture-driven Internet of Medical Things framework for healthcare monitoring
dc.typeArticle
dspace.entity.typePublication
local.contributor.departmentMühendislik Fakültesi/Bilgisayar Mühendisliği Bölümü
local.indexed.atWOS
local.indexed.atScopus
relation.isAuthorOfPublication44bc36d2-0d2c-4f60-aed7-11bf3e17b449
relation.isAuthorOfPublication.latestForDiscovery44bc36d2-0d2c-4f60-aed7-11bf3e17b449

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