VAST-GCN: An Attention-Driven Graph Convolutional Network(GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks

VAST-GCN: An Attention-Driven Graph Convolutional Network(GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc 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 VAST-GCN: An Attention-Driven Graph Convolutional Network(GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks Anish Shandilya, Varuna Gupta, Bosco Paul Alapatt, Ramesh Chandra Poonia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7427707/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and cut down on routing work. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. We test our approach in vehicle settings with 100, 500, and 1000 vehicles making graph data using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. We group this data using the K-Means method and check it with modularity score, silhouette score, and group density. When we compare results, VAST-GCN does better than regular GCN and MixHopGCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or they're moving fast. Our model offers a strong and scalable answer for future smart transport systems allowing reliable and efficient V2V talk. Vehicular Ad-Hoc Networks (VANETs) Cluster Head (CH) Selection Graph Convolutional Networks (GCNs) Spatial-Temporal Attention Vehicle-to-Vehicle (V2V) Communication Network Stability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Sep, 2025 Reviews received at journal 23 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 02 Sep, 2025 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-7427707","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514100579,"identity":"059daeb6-9d70-45d5-a196-8b9a8e44e08b","order_by":0,"name":"Anish Shandilya","email":"","orcid":"","institution":"CHRIST University","correspondingAuthor":false,"prefix":"","firstName":"Anish","middleName":"","lastName":"Shandilya","suffix":""},{"id":514100581,"identity":"3e3a1ebd-db55-411c-b30c-bab636923737","order_by":1,"name":"Varuna Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYAgAGyBmbDxAnGI2MJkG0tJAkpbDYBKvFoPjZ59JF9TUJm6Xb37A8OPPebu17YeBttTYROPUcibdTHrGseOJO9vYDBh7224nbzuTCNRyLC23AYcWyYY0NmketmOJG44xGDAzNtxONjsA1MLYcBi3lv5nQC3/QFrYPzAz/DmXbHb+IX4t/BJAW3jbaoBaeAyYGdgO2JndIGALv8QzZmvevgPGO9tyCg72tiUnmN0A2pKAxy9s/GmMt3m+1cluZz6+8cGPP3b2ZufTHz74UGODUwsUHGYwYIBERyJYZQJ+5SBQB9YCAvaEFY+CUTAKRsFIAwDLVGFm6nNmFAAAAABJRU5ErkJggg==","orcid":"","institution":"CHRIST University","correspondingAuthor":true,"prefix":"","firstName":"Varuna","middleName":"","lastName":"Gupta","suffix":""},{"id":514100582,"identity":"e4cff19d-2133-417f-934c-f9847d76ba55","order_by":2,"name":"Bosco Paul Alapatt","email":"","orcid":"","institution":"CHRIST University","correspondingAuthor":false,"prefix":"","firstName":"Bosco","middleName":"Paul","lastName":"Alapatt","suffix":""},{"id":514100585,"identity":"69d8f38c-5b3f-4d61-b0f5-ca5d097c83da","order_by":3,"name":"Ramesh Chandra Poonia","email":"","orcid":"","institution":"CHRIST University","correspondingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"Chandra","lastName":"Poonia","suffix":""}],"badges":[],"createdAt":"2025-08-21 15:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7427707/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7427707/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91403678,"identity":"b421a032-9675-4749-ac9f-b9dd683ccc68","added_by":"auto","created_at":"2025-09-16 07:29:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1412672,"visible":true,"origin":"","legend":"","description":"","filename":"finalmanuscriptsept25.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7427707/v1_covered_8d8fc9fd-171f-4c84-9dad-3f6134eee02b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"VAST-GCN: An Attention-Driven Graph Convolutional Network(GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-networked-and-distributed-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Networked and Distributed Computing](https://link.springer.com/journal/44227)","snPcode":"44227","submissionUrl":"https://submission.springernature.com/new-submission/44227/3","title":"International Journal of Networked and Distributed Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Vehicular Ad-Hoc Networks (VANETs), Cluster Head (CH) Selection, Graph Convolutional Networks (GCNs), Spatial-Temporal Attention, Vehicle-to-Vehicle (V2V) Communication, Network Stability","lastPublishedDoi":"10.21203/rs.3.rs-7427707/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7427707/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. 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