Predictive IoT Network Routing Optimization Using Hybrid Augmented Gradient Boosting Classifier Algorithm

Predictive IoT Network Routing Optimization Using Hybrid Augmented Gradient Boosting Classifier Algorithm | 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 Predictive IoT Network Routing Optimization Using Hybrid Augmented Gradient Boosting Classifier Algorithm Nidhi Bajpai, Madhavi Dhingra, Nisha Chaurasia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7296600/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 exponential development of Internet of Things (IoT) systems lead to network traffic challenges that require advanced classification methods for optimizing resource distribution, congestion control and QoS enhancement. Both rule-based and deep packet inspection classification techniques show limited capacity to respond to IoT traffic changes because of its dynamic complexity together with encryption barriers. The proposed research work developed a machine learning-based technique for IoT traffic classification and routing by using HAGBC alongside LDA and best fit feature techniques which enabled traffic clustering using Tuna Swarm-inspired Jaccard and Interpolation Fuzzy C-Means Clustering. The model achieves optimal weight adjustment through Self-Updated Dung Beetle Optimization which results in superior classification accuracy together with reduced computational complexity. The experimental results have demonstrated increased scalability, decreased energy usage, and enhanced accuracy with the proposed algorithm. The proposed model maintains adaptive functionality toward network changes to execute real-time traffic management efficiently for developing automatic network infrastructure systems across extensive IoT settings. Internet of Things LDA Tuna Swarm-inspired Jaccard with Interpolation Fuzzy C-Means Clustering algorithm Self-Updated Dung Beetle Optimization method machine learning HAGBC 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. 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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-7296600","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514151206,"identity":"9e489ca4-007d-49bf-acc4-354a281f5a37","order_by":0,"name":"Nidhi Bajpai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3RsWrCQBzH8V+4ki5C1+sgeYLCBcEqKr7KBYcsLXRqHTrkEOLkHtCXKEJmw0EmwbWDgy7ONzqE0jMpZLrGsdD7Dn+Og8/dwQE221+MoAuODZwI2Khqp56NJEvKHdJAoIk+v+Kt+gIzebwl6eGAvUeWs6McFvAe5nc5xXQQRAbSn7lvjOPki1XO5HMMP5XEpdiGRsJkq0s5pCMSrkkEpyROLBvJWCShkr0C44p8NZNAJE9MwkVQkeg34r5Szk4Tsdq+ZIuYTjTp9HgedoxkJ9P783Q/+ljO1+pcDEfpLjt+qvdB20R+IOCX3wh6GTf6qVfk0XpN1DXCZrPZ/k3f9zxcGlAPKnAAAAAASUVORK5CYII=","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Bajpai","suffix":""},{"id":514151207,"identity":"9c4da050-57e2-40b7-98be-58f200c5a12a","order_by":1,"name":"Madhavi Dhingra","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Madhavi","middleName":"","lastName":"Dhingra","suffix":""},{"id":514151210,"identity":"ec4335a9-eb06-419c-abd4-64be3cc63d50","order_by":2,"name":"Nisha Chaurasia","email":"","orcid":"","institution":"Dr. B R Ambedkar NIT","correspondingAuthor":false,"prefix":"","firstName":"Nisha","middleName":"","lastName":"Chaurasia","suffix":""}],"badges":[],"createdAt":"2025-08-05 05:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7296600/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7296600/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103171186,"identity":"aeea48df-9ce1-4b29-8553-c7929b0bd7cd","added_by":"auto","created_at":"2026-02-22 14:10:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":855606,"visible":true,"origin":"","legend":"","description":"","filename":"RoutingOptimization6July25.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7296600/v1_covered_62b38a9d-b631-489c-be19-9199374149f2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predictive IoT Network Routing Optimization Using Hybrid Augmented Gradient Boosting Classifier Algorithm","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":"Internet of Things, LDA, Tuna Swarm-inspired Jaccard with Interpolation Fuzzy C-Means Clustering algorithm, Self-Updated Dung Beetle Optimization method, machine learning, HAGBC","lastPublishedDoi":"10.21203/rs.3.rs-7296600/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7296600/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exponential development of Internet of Things (IoT) systems lead to network traffic challenges that require advanced classification methods for optimizing resource distribution, congestion control and QoS enhancement. 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