A Hybrid Augmented Gradient Boosting Classifier-Based Fuzzy Clustering-Based Routing Algorithm for IoT

A Hybrid Augmented Gradient Boosting Classifier-Based Fuzzy Clustering-Based Routing Algorithm for IoT | 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 Article A Hybrid Augmented Gradient Boosting Classifier-Based Fuzzy Clustering-Based Routing Algorithm for IoT Nidhi Bajpai, Madhavi Dhingra, Nisha Chaurasia This is a preprint; it has not been peer reviewed by a journal.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-9196897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The exponential growth of Internet of Things (IoT) systems has introduced significant network traffic challenges that require intelligent classification for optimized routing, congestion control, and Quality of Service (QoS) enhancement. Traditional rule-based and deep packet inspection techniques often fail to adapt to the dynamic and encrypted nature of IoT traffic. To address these limitations, this study proposes a machine learning-driven routing optimization framework using a Hybrid Augmented Gradient Boosting Classifier (HAGBC) integrated with Linear Discriminant Analysis (LDA), Jaccard with Interpolation Scaled Tuna Swarm-based Fuzzy C-Means Clustering (JISTS-FCM), and Self-Updated Dung Beetle Optimization (SU-DBO).The proposed model enhances feature separability, optimizes cluster formation, and dynamically adjusts model weights to improve traffic classification and routing decisions. Experimental evaluation demonstrates that the proposed HAGBC model achieves superior performance with an accuracy of 99.1%, precision of 99.32%, recall of 99.10%, specificity of 98.92%, and F1-score of 99.21%, outperforming conventional approaches such as Decision Tree, SVM, K-NN, and Random Forest. Physical sciences/Engineering Physical sciences/Mathematics and computing 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: Under Review Version 1 posted Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Editor invited by journal 08 Apr, 2026 Submission checks completed at journal 06 Apr, 2026 First submitted to journal 06 Apr, 2026 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. 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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-9196897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":621219574,"identity":"9b996f0f-e771-4fed-8ccc-76e6419309ef","order_by":0,"name":"Nidhi Bajpai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIie2RvWrDMBRGrxHEi6Gr+xZ3kjEY5UG6WAiSqdCpHRroLYF26QPkJbqajCqCdjH1augS0RfwWsjQm6Qd/TMWqrPoQ9yjT0IAgcBfRIAETbxARNBhwVvRvZ2sRJurxUGhYQVY4fNPetK5YxhUslhUO79VcRY7ggQb9fzouGVVXPQp+Xp2g7o2In/i66X4Yapas/K6uKQeBV0iU/0gBFpWkBXJwUbkxpQ7gY0nKPHdSA5TFCew5RaLVsl2tGV2ner6TeQbTy+EppQtt5RDb2lcdf61vTXZ2dJ/7vdqLpul33Wrolf5xSCcvkMfJ8uR8QMKf8J8wnAgEAj8M74Bhzxk1tlZ0rAAAAAASUVORK5CYII=","orcid":"","institution":"Amity University","correspondingAuthor":true,"prefix":"","firstName":"Nidhi","middleName":"","lastName":"Bajpai","suffix":""},{"id":621219577,"identity":"612e1689-06fd-493e-ad07-1e8453d03c99","order_by":1,"name":"Madhavi Dhingra","email":"","orcid":"","institution":"Amity University","correspondingAuthor":false,"prefix":"","firstName":"Madhavi","middleName":"","lastName":"Dhingra","suffix":""},{"id":621219580,"identity":"0cba5f59-4626-4ad7-be2a-bbf99264d744","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":"2026-03-23 07:25:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9196897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9196897/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481550,"identity":"ca951e6e-26f3-4759-8e13-39f643cb4d1a","added_by":"auto","created_at":"2026-04-22 02:19:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":655217,"visible":true,"origin":"","legend":"","description":"","filename":"RoutingOptimization60426.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9196897/v1_covered_60883035-1774-405a-9abc-86a27b5ab3c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Hybrid Augmented Gradient Boosting Classifier-Based Fuzzy Clustering-Based Routing Algorithm for IoT","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"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-9196897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9196897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe exponential growth of Internet of Things (IoT) systems has introduced significant network traffic challenges that require intelligent classification for optimized routing, congestion control, and Quality of Service (QoS) enhancement. 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