Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing

Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing Somayeh Iranpak, Asadollah Shahbahrami, Hassan Shakeri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1888013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract High amounts of patient and healthcare data are generated daily on the Internet of Things (IoT). The processing time and analyzing the big data received from IoT devices, as well as providing the necessary accuracy for data classification, are considered as the most important challenges in IoT. Therefore, this study aims to improve the accuracy of data classification by implementing hybrid approaches of feature selection (FS) and deep learning (DL) within the fog computing infrastructure while reducing the response time (RT) and bandwidth usage. Also, the major goal is to analyze and classify the states of patients remotely through IoT, cloud computing and fog computing technologies. The information processing time is reduced by implementing Particle Swarm Optimization (PSO) and Imperialist Competitive Algorithm (ICA) in fog computing to select the prominent features, and the classification accuracy is improved through classifying new data by a deep neural network (DNN) model. The simulation results show that the accuracy of the classification and remote monitoring of patients is 98.54%, which is about 4.5% better than the case without using PSO-ICA algorithms, and it is also improved by about 10% on average compared to other methods such as Linear Regression (LR), K Nearest Neighbors (KNN), Neural Network (NN), and Bayesian Belief Net (BBN). Cloud computing Fog computing Internet of things Deep neural network Remote monitoring of patients Medical big data analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1888013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":123984985,"identity":"e32f83ed-876a-430d-af86-f43876d1fe2d","order_by":0,"name":"Somayeh Iranpak","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Somayeh","middleName":"","lastName":"Iranpak","suffix":""},{"id":123984986,"identity":"245c4e45-c69c-45d7-8187-209cffcd0074","order_by":1,"name":"Asadollah Shahbahrami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYDACCQY2ZgaGA0AWY+MDIFcGLk6MlmYDIJeHFC0MbCBlPLiVQoH87OZnjwtq7uTzTzvcVvm1zYKHf3YD44cfDBb5uLQY3Dlmbjzj2DPLGbcT227LtknwSNw5wCzZwyBh2YBLi0SCmTQP22EDBpAWSaAWhhsJDNJABxvgdNiM9G/SPP8OG8gDtRSDtMjfSGD+jU8Lw40cM2netsMGBkAtjB+BWgxuJLDhtcXgRk6Z9My+wwaGtxObpRnOSfAY3khss+wxwOuwbdIF3w4byN1Of/jxR1mdnNyN5MM3flTU4XYYMmCGRApjA9B2ojQA1f4gUuEoGAWjYBSMLAAAuVdSVmNz2qIAAAAASUVORK5CYII=","orcid":"","institution":"Islamic Azad University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Asadollah","middleName":"","lastName":"Shahbahrami","suffix":""},{"id":123984987,"identity":"440b0924-826d-458f-996f-db887466bcf6","order_by":2,"name":"Hassan Shakeri","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"","lastName":"Shakeri","suffix":""}],"badges":[],"createdAt":"2022-07-23 09:29:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1888013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1888013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":24606935,"identity":"1b71838e-5017-422e-9a73-3c9390e62666","added_by":"auto","created_at":"2022-08-01 16:21:27","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1387677,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1888013/v1_covered.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analyzing IoT big data in healthcare using Deep learning and Distributed Fog Computing","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1888013/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cloud computing, Fog computing, Internet of things, Deep neural network, Remote monitoring of patients, Medical big data analysis","lastPublishedDoi":"10.21203/rs.3.rs-1888013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1888013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh amounts of patient and healthcare data are generated daily on the Internet of Things (IoT). 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