DOX-CMTabNetX: An Energy-Efficient Optimization Framework for heterogeneous wireless sensor Network in Agrovoltaic monitoring system

DOX-CMTabNetX: An Energy-Efficient Optimization Framework for heterogeneous wireless sensor Network in Agrovoltaic monitoring system | 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 DOX-CMTabNetX: An Energy-Efficient Optimization Framework for heterogeneous wireless sensor Network in Agrovoltaic monitoring system BLESSINA PREETHI R, Saranya Nair M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9160384/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Agrovoltaic systems increasingly rely on Internet of Things (IoT)-enabled heterogeneous wireless sensor networks (HWSNs) for real-time environmental monitoring. However, energy inefficiency and redundant data transmission remain critical challenges. This study proposes a hybrid optimization and learning framework, termed DOX-CMTabNetX, for efficient sensor deployment, clustering, and data analysis in agrovoltaic environments. The approach integrates a Brownian motion–enhanced Dingo Optimization algorithm for optimal cluster head selection using a multi-objective fitness function that jointly considers residual energy, communication distance, environmental zone priority, solar–moisture impact factors, and intra-cluster communication cost. To further improve efficiency, a redundancy reduction mechanism based on spatial and temporal correlation and Euclidean distance filtering is introduced to eliminate duplicate data transmissions.The proposed framework is evaluated against established benchmark models, including FAML, HBWCO, ST-IAOA-X, and FFO-TCN-BiGRU. The experimental results demonstrate that the proposed method significantly improves network performance, achieving up to 43.1%, 26.6%, 48.2%, and 40.9% higher throughput compared with the respective baseline models. Additionally, the integration of the MTabNetX classifier enables high-precision data analytics of Sensunuts module testbed generated dataset for classification accuracy in the range of 96–99%, representing an improvement of approximately 2–10% over existing methods. These results indicate that the proposed framework effectively reduces energy consumption, minimizes redundant transmissions, and enhances overall network lifetime and reliability, making it a robust solution for next-generation agrovoltaic IoT systems. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 14 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 17 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 15 Apr, 2026 Editor invited by journal 27 Mar, 2026 Editor assigned by journal 21 Mar, 2026 Submission checks completed at journal 21 Mar, 2026 First submitted to journal 18 Mar, 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. 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-9160384","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":624692042,"identity":"7f7fffd0-944c-4321-8888-0dc515494a86","order_by":0,"name":"BLESSINA PREETHI R","email":"","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":false,"prefix":"","firstName":"BLESSINA","middleName":"PREETHI","lastName":"R","suffix":""},{"id":624692043,"identity":"849cc51b-1af0-4027-8eb0-c14f7be93fac","order_by":1,"name":"Saranya Nair M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYFADZhBRwcAD4RkQreUMAw8P8VpAgLGNAWYNbiDffvbh58I2uzz+dt4HTDfnHZaxZ2B++IGh4A5OLQZn0o2lZ7YlF0scZjdgzt12GOgwNmMJBoNnuLUwpDFI87YxJzYcZmMAakkD+cUMKH4Yt8P6nzH/5m2rT5wP1jIHpIX9G14tDDfS2IC2HE7cANbSYAPUwoPfFoMbz9isec4dT9wI1HI45xhQy2GeYokEvA5LY77NU1adOO/8McbHOTUS9uzt7Rs/fPiDx2EgwMgGoQ+ASVCcJuDXAAR/CKoYBaNgFIyCkQwAG5FGJVs9R7MAAAAASUVORK5CYII=","orcid":"","institution":"Vellore Institute of Technology University","correspondingAuthor":true,"prefix":"","firstName":"Saranya","middleName":"Nair","lastName":"M","suffix":""}],"badges":[],"createdAt":"2026-03-18 14:11:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9160384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9160384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107489702,"identity":"c8b76d55-82f7-4853-9531-e9b9ebaed71f","added_by":"auto","created_at":"2026-04-22 02:48:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4440493,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9160384/v1_covered_a4b94449-ce5d-4406-aa52-28ea924b4fdd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DOX-CMTabNetX: An Energy-Efficient Optimization Framework for heterogeneous wireless sensor Network in Agrovoltaic monitoring system","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":"","lastPublishedDoi":"10.21203/rs.3.rs-9160384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9160384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgrovoltaic systems increasingly rely on Internet of Things (IoT)-enabled heterogeneous wireless sensor networks (HWSNs) for real-time environmental monitoring. 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