Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET

Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET | 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 Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET Santosh Kumar Maharana, Prashanta Kumar Patra This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6504224/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 Similar approaches are very useful in Vehicular Ad Hoc Networks in which: a flood of traffic and events occurs, especially in Intelligent Transportation Systems (ITS), as congestion control can have a huge impact in efficient traffic field and communication. The existing congestion control mechanisms are not suitable for this type of dynamic network topology and vehicle mobility. In response to these challenges, we propose an Adaptive Multi-Agent Graph Neural Network (AMAGNN), which utilizes graph neural networks (GNNs) and multi-agent reinforcement learning (MARL) for congestion control in vehicular ad hoc networks (VANETs). Institute, “AMAGNN models VANETs as dynamic graphs, with vehicles being intelligent agents that cooperatively learn to optimize data propagation and alleviate network congestion. Optimizes for scalability and adaptability by dynamically responding to network topology changes and traffic density variation. Results show that the proposed AMAGNN model outperforms current practices in congestion control for VANETs on all the key performance metrics. It attains Packet Delivery Ratio (92.5%), lowest delay (28.4 ms), and maximum throughput (275.6 kbps) which shows reliability and efficiency in communication. It also achieves the lowest congestion level (0.35) and average waiting time (0.31 s), highlighting its ability to reduce traffic and delay. AMAGNN achieves the best performance, compared with methods such as GCN-RL, DQN Adaptive, and MA-PPO, highlighting the effectiveness of adaptive multi-agent graph neural networks in the ever-changing vehicular environment. MARL GNN VANET AMAGNN AODV 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-6504224","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":452074569,"identity":"0ce92971-d356-49e9-8bec-ca4c825403cc","order_by":0,"name":"Santosh Kumar Maharana","email":"","orcid":"","institution":"Biju Patnaik University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"Kumar","lastName":"Maharana","suffix":""},{"id":452074570,"identity":"36b0135f-c61b-49e2-8176-1045da5235e4","order_by":1,"name":"Prashanta Kumar Patra","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3OsQqCUBTG8S8EW066HinwFa4IttWr3Gh1CyJoSAhs6QV6kWYh6C4+QBBULrU0NEWDQ1JBm9rWcH/LhcP9cw6gaf8oeT+EZvSZGLUTSn5MAJY177LU/HxCfujYq0t2eqDnotlKShMn3XZFIx4R70PfW2LoRYZVvk/sZMCNSJJohyYTDAmDRHlyvNwZeZE46uzkmNVIdhQwzCJhBG3Cpjpx0nDMg1gSp6Hvd4Ty4qrEUmrNt1z27YXKsutk6tp2Wp68yO+dgFn9X9M0TavyBJ8BOOwiKVIRAAAAAElFTkSuQmCC","orcid":"","institution":"Siksha O Anusandhan Deemed to be University","correspondingAuthor":true,"prefix":"","firstName":"Prashanta","middleName":"Kumar","lastName":"Patra","suffix":""}],"badges":[],"createdAt":"2025-04-22 12:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6504224/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6504224/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84554839,"identity":"85d431b1-9599-4b9a-8cad-0e968ee3f0fa","added_by":"auto","created_at":"2025-06-13 11:16:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1022241,"visible":true,"origin":"","legend":"","description":"","filename":"FINALSANTOSHDC1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6504224/v1_covered_6b8a0493-1cb2-4080-8f5f-450fbc033f13.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET","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":"MARL, GNN, VANET, AMAGNN, AODV","lastPublishedDoi":"10.21203/rs.3.rs-6504224/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6504224/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSimilar approaches are very useful in Vehicular Ad Hoc Networks in which: a flood of\u0026ensp;traffic and events occurs, especially in Intelligent Transportation Systems (ITS), as congestion control can have a huge impact in efficient traffic field and communication. The existing congestion control mechanisms are not suitable for this type of dynamic network topology and vehicle\u0026ensp;mobility. In response to these challenges, we propose an Adaptive Multi-Agent Graph Neural Network (AMAGNN), which utilizes graph neural networks (GNNs) and multi-agent reinforcement learning (MARL) for congestion\u0026ensp;control in vehicular ad hoc networks (VANETs). Institute, \u0026ldquo;AMAGNN models VANETs as dynamic graphs, with vehicles being intelligent agents that cooperatively learn to optimize\u0026ensp;data propagation and alleviate network congestion. Optimizes for scalability and adaptability by dynamically responding to\u0026ensp;network topology changes and traffic density variation. Results show that\u0026ensp;the proposed AMAGNN model outperforms current practices in congestion control for VANETs on all the key performance metrics. It attains Packet Delivery Ratio (92.5%), lowest delay (28.4 ms), and maximum throughput\u0026ensp;(275.6 kbps) which shows reliability and efficiency in communication. It also achieves the lowest congestion level (0.35) and average waiting time (0.31 s), highlighting its ability to reduce traffic\u0026ensp;and delay. AMAGNN achieves the best performance, compared with methods such as GCN-RL, DQN Adaptive,\u0026ensp;and MA-PPO, highlighting the effectiveness of adaptive multi-agent graph neural networks in the ever-changing vehicular environment.\u003c/p\u003e","manuscriptTitle":"Adaptive Multi-Agent Graph Neural Network (AMAGNN) for Congestion Control in VANET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 11:51:37","doi":"10.21203/rs.3.rs-6504224/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"ec8e34fa-a220-4f2e-afa0-de4137a64999","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-13T11:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 11:51:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6504224","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6504224","identity":"rs-6504224","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}