Blockchain-Integrated IoT Framework for Patient Monitoring Using Optimized Multi-Scale Adaptive Graph Neural Networks

Blockchain-Integrated IoT Framework for Patient Monitoring Using Optimized Multi-Scale Adaptive Graph Neural Networks | 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 Blockchain-Integrated IoT Framework for Patient Monitoring Using Optimized Multi-Scale Adaptive Graph Neural Networks Narendra Singh Yadav Narendra Singh Yadav, Rajesh Tiwari Rajesh Tiwari, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7110753/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 The increasing use of Internet of Things (IoT) devices in healthcare has significantly improved real-time patient monitoring. However, it also introduces critical challenges in data security, patient privacy, and the integration of heterogeneous data streams. To address these issues, we propose the first framework integrating Hyperledger Fabric with a Multi-Scale Adaptive Graph Neural Network (GNN) for real-time IoT healthcare systems , offering unmatched security and predictive accuracy. The blockchain layer ensures tamper-proof storage, enforces fine-grained access control, and builds trust among stakeholders, while the adaptive GNN captures temporal dynamics and complex interdependencies in physiological signals to detect anomalies. Experimental results on synthetic healthcare datasets show that our framework achieves a 99.6% accuracy, an AUC and F1-score of 1.0, and a blockchain throughput of 97.13 transactions per second , significantly outperforming traditional machine learning models by 10–15% in accuracy and delivering 2× higher throughput compared to prior blockchain-based systems. While current validation is based on synthetic data, real-world deployment is planned to confirm generalizability. This integrated approach paves the way for scalable, trustworthy, and equitable digital healthcare solutions , especially in resource-constrained and decentralized settings. Digital healthcare Blockchain technology Hyperledger Fabric Graph Neural Networks (GNN) Real-time monitoring Healthcare informatics Good Health and Well-being SDG Goal 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7110753","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516531626,"identity":"09ffb937-a6c0-4931-bbaa-eda6a5848ce6","order_by":0,"name":"Narendra Singh Yadav Narendra Singh Yadav","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Narendra","middleName":"Singh Yadav Narendra Singh","lastName":"Yadav","suffix":""},{"id":516531627,"identity":"135efeaa-af09-41f0-8f37-6a5694726275","order_by":1,"name":"Rajesh Tiwari Rajesh 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Networks","fulltext":[],"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":true,"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":"Digital healthcare, Blockchain technology, Hyperledger Fabric, Graph Neural Networks (GNN), Real-time monitoring, Healthcare informatics, Good Health and Well-being, SDG Goal","lastPublishedDoi":"10.21203/rs.3.rs-7110753/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7110753/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe increasing use of Internet of Things (IoT) devices in healthcare has significantly improved real-time patient monitoring. However, it also introduces critical challenges in data security, patient privacy, and the integration of heterogeneous data streams. To address these issues, \u003cstrong\u003ewe propose the first framework integrating Hyperledger Fabric with a Multi-Scale Adaptive Graph Neural Network (GNN) for real-time IoT healthcare systems\u003c/strong\u003e, offering unmatched security and predictive accuracy. The blockchain layer ensures tamper-proof storage, enforces fine-grained access control, and builds trust among stakeholders, while the adaptive GNN captures temporal dynamics and complex interdependencies in physiological signals to detect anomalies. \u003cstrong\u003eExperimental results on synthetic healthcare datasets show that our framework achieves a 99.6% accuracy, an AUC and F1-score of 1.0, and a blockchain throughput of 97.13 transactions per second\u003c/strong\u003e, significantly outperforming traditional machine learning models by 10–15% in accuracy and delivering 2× higher throughput compared to prior blockchain-based systems. While current validation is based on synthetic data, real-world deployment is planned to confirm generalizability. \u003cstrong\u003eThis integrated approach paves the way for scalable, trustworthy, and equitable digital healthcare solutions\u003c/strong\u003e, especially in resource-constrained and decentralized settings.\u003c/p\u003e","manuscriptTitle":"Blockchain-Integrated IoT Framework for Patient Monitoring Using Optimized Multi-Scale Adaptive Graph Neural Networks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-26 20:36:02","doi":"10.21203/rs.3.rs-7110753/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"74cd5d42-2aa3-41b6-81f2-3a07b03c0700","owner":[],"postedDate":"September 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-25T08:09:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-26 20:36:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7110753","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7110753","identity":"rs-7110753","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}