Machine Learning-based Clustered Data Dissemination Protocol for Mobile Wireless Sensor Networks

Machine Learning-based Clustered Data Dissemination Protocol for Mobile Wireless Sensor 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 Machine Learning-based Clustered Data Dissemination Protocol for Mobile Wireless Sensor Networks Dr RAJESH MITUKULA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6656926/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 Mobile Wireless Sensor Networks (MWSNs) are widely operated in dynamic environments, including smart cities, environmental monitoring, and disaster management. However, mobility-induced topology variations introduce significant challenges in clustering and data dissemination. To address these challenges, an Energy-Efficient Machine Learning-Optimized Cluster-based Data Dissemination (EMCDD) Protocol was proposed. This protocol integrates a hybrid model of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for optimal cluster formation and CH selection, while Support Vector Machine (SVM)-based classification ensures robust CH election. The data transmission is optimized using a deep Q-learning-based adaptive scheduling mechanism, which dynamically allocates time slots based on node mobility and connection duration. Simulation results demonstrate that EMCDD outperforms HDDP and ADDP protocols in terms of throughput, total energy consumption, and end-to-end delay under varying node mobility and traffic conditions. Optimization Clustering Machine Learning Energy Efficiency Data dissemination Mobile Wireless Sensor Network 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-6656926","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456410119,"identity":"b7e94ab5-0b90-4a78-877b-a375dea578c8","order_by":0,"name":"Dr RAJESH MITUKULA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie2PvWrDMBRGr6YuFVoNKeQVbArqYuwH6XKNQFnaN8igElBeIRn6CF1LRoGhXdR4DWSx6VxIR0+t/LMqzlioznLv8B19VwCBwN8EgahuGnBL6hbyZC5ViCJKdoqaUmBUoFNKGAv93K1N07S7dM7Ix/sn3VXZy7p0Lcv03qfcWBS31Mpkq/bFitqjeLWFU97ko/IoEaCcEV1ibGyyovoouHGKu9CvsHrRtvoH80HZC141E0qEEqg2GEOvmIwfplqiWsyoFsnGtWyftUB+cC147i/sofhudTZnGxufvnSW82rR1Kdl6lUArnGUTT+KPoneeMfVEAU2vJqfDQcCgcC/5BeJ92ryPZgM0gAAAABJRU5ErkJggg==","orcid":"","institution":"University College of Engineering, Science \u0026 Technology, JNTUH","correspondingAuthor":true,"prefix":"Dr","firstName":"RAJESH","middleName":"","lastName":"MITUKULA","suffix":""}],"badges":[],"createdAt":"2025-05-13 15:23:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6656926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6656926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83852663,"identity":"26f00fea-afb1-411a-993f-d692babfe246","added_by":"auto","created_at":"2025-06-03 16:31:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550295,"visible":true,"origin":"","legend":"","description":"","filename":"EMCDD.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6656926/v1_covered_3d86d36e-9634-4f02-98f8-31412efeef97.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning-based Clustered Data Dissemination Protocol for Mobile Wireless Sensor Networks ","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Optimization, Clustering, Machine Learning, Energy Efficiency, Data dissemination, Mobile Wireless Sensor Network","lastPublishedDoi":"10.21203/rs.3.rs-6656926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6656926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMobile Wireless Sensor Networks (MWSNs) are widely operated in dynamic environments, including smart cities, environmental monitoring, and disaster management. However, mobility-induced topology variations introduce significant challenges in clustering and data dissemination. To address these challenges, an Energy-Efficient Machine Learning-Optimized Cluster-based Data Dissemination (EMCDD) Protocol was proposed. This protocol integrates a hybrid model of Particle Swarm Optimization (PSO) and \u0026nbsp;Genetic Algorithm (GA) for optimal cluster formation and CH selection, while Support Vector Machine (SVM)-based classification ensures robust CH election. The data transmission is optimized using a deep Q-learning-based adaptive scheduling mechanism, which dynamically allocates time slots based on node mobility and connection duration. 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