A Novel Gaussian Mixture Model Clustering with Hierarchical Routing (GMMCHR) for Wireless Sensor Network

A Novel Gaussian Mixture Model Clustering with Hierarchical Routing (GMMCHR) for Wireless Sensor Network | 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 A Novel Gaussian Mixture Model Clustering with Hierarchical Routing (GMMCHR) for Wireless Sensor Network Neetu Sikarwar, Ranjeet Singh Tomar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6854625/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 Among the technologies developing most rapidly are wireless sensor net-works (WSNs). This is due to WSNs possessing numerous applications and a remarkably robust set of capabilities. This study addresses the critical issue of energy conservation in Wireless Sensor Networks (WSNs), where limited energy capacity of Sensor Nodes (SNs) significantly impacts network lifespan. To enhance energy efficiency, a novel hierarchical routing algo-rithm named GMMCHR (Gaussian Mixture Model Clustering with Hierar-chical Routing) is proposed. The method utilizes the GMM algorithm for clustering and introduces a hierarchical packet routing mechanism based on Central Cluster Heads (CCH) and Direct Cluster Heads (DCH), selected us-ing various Fitness Functions (FFs). Simulations were conducted in two sce-narios—100 nodes in a 100×100 m² area and 200 nodes in a 200×200 m² ar-ea—using MATLAB. Results demonstrate that GMMCHR significantly re-duces energy consumption, improves network coverage, and extends the overall network lifetime, validating its effectiveness in energy-efficient rout-ing for WSNs. Clustering Energy Efficiency (EE) Gaussian Mixture Model Hierarchical Routing WSN. 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-6854625","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470095004,"identity":"d3125ce7-c7bc-44c0-9181-09d57cde98dd","order_by":0,"name":"Neetu Sikarwar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYPACCQM29gYgbWBBlHLGBpAWPp4DIC0SRGthMJCTSABbR1i9OXvz8cc8fyyM2SSfX93wo0CCgb+9OwGvFsueY4nNvG0SZmzSOWU3e4AOkzhzdgNeLQY3cgybeRskbIBa0m7wALUYSOQS0HL//cdmnj9ALZJn0m7+IUrLDR7GZh42oMMk2I/dJs6WM2mGM+e2SRiz8eSw3ZYxkOAh7Jfjhx98ePOnznB++/FnN9/8sZHjb+/FrwUJ8BiASWKVgwD7A1JUj4JRMApGwQgCAMKYQtN5iBcDAAAAAElFTkSuQmCC","orcid":"","institution":"ITM University","correspondingAuthor":true,"prefix":"","firstName":"Neetu","middleName":"","lastName":"Sikarwar","suffix":""},{"id":470095005,"identity":"f7df56b9-731d-496e-981d-5a96f95b62bc","order_by":1,"name":"Ranjeet Singh Tomar","email":"","orcid":"","institution":"ITM University","correspondingAuthor":false,"prefix":"","firstName":"Ranjeet","middleName":"Singh","lastName":"Tomar","suffix":""}],"badges":[],"createdAt":"2025-06-09 12:53:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6854625/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6854625/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94730207,"identity":"fc0e1b11-b9d1-475d-a01b-a248ea99fd65","added_by":"auto","created_at":"2025-10-30 07:05:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":530051,"visible":true,"origin":"","legend":"","description":"","filename":"ANovelGaussianMixtureModelClusteringwithHierarchicalRoutingGMMCHR.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6854625/v1_covered_819042a3-4e8e-4744-b0ea-3601fc426ea4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Gaussian Mixture Model Clustering with Hierarchical Routing (GMMCHR) for Wireless Sensor Network","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":"Clustering, Energy Efficiency (EE), Gaussian Mixture Model, Hierarchical Routing, WSN.","lastPublishedDoi":"10.21203/rs.3.rs-6854625/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6854625/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAmong the technologies developing most rapidly are wireless sensor net-works (WSNs). This is due to WSNs possessing numerous applications and a remarkably robust set of capabilities. This study addresses the critical issue of energy conservation in Wireless Sensor Networks (WSNs), where limited energy capacity of Sensor Nodes (SNs) significantly impacts network lifespan. To enhance energy efficiency, a novel hierarchical routing algo-rithm named GMMCHR (Gaussian Mixture Model Clustering with Hierar-chical Routing) is proposed. The method utilizes the GMM algorithm for clustering and introduces a hierarchical packet routing mechanism based on Central Cluster Heads (CCH) and Direct Cluster Heads (DCH), selected us-ing various Fitness Functions (FFs). Simulations were conducted in two sce-narios\u0026mdash;100 nodes in a 100\u0026times;100 m\u0026sup2; area and 200 nodes in a 200\u0026times;200 m\u0026sup2; ar-ea\u0026mdash;using MATLAB. 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