Model-based Constrained Bayesian Optimization of IEEE 802.11 VANET Safety Messaging

Model-based Constrained Bayesian Optimization of IEEE 802.11 VANET Safety Messaging | 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 Model-based Constrained Bayesian Optimization of IEEE 802.11 VANET Safety Messaging Aidan Wright, Shengli Ding, Sandeep John Philips, Rianne Ann Matthew, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6278319/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Wireless Networks → Version 1 posted 10 You are reading this latest preprint version Abstract Vehicle safety remains a critical concern as road accidents occur daily. Wireless communication technologies are increasingly recognized for their potential to enhance the safety of both human-driven and autonomous vehicles. In particular, IEEE 802.11-based communication systems have been proposed and evaluated to improve road safety. Achieving low transmission latency and high reliability is essential for the development of Vehicular Ad Hoc Networks (VANETs). Given the dynamic nature of vehicular environments and stringent quality of service (QoS) requirements, adaptive network configurations are necessary. This paper presents two Bayesian model-based approaches for optimizing adaptive real-time communication parameters to achieve optimal network configurations while satisfying QoS constraints. The first approach integrates a stochastic QoS model with constrained Bayesian optimization algorithms, addressing the complexity and computational demands of traditional network simulations. The second approach incorporates a Deep Learning Neural Network (DLNN) into the Bayesian optimization framework, significantly accelerating the iterative optimization process and enabling adaptation to dynamic communication environments. The optimization algorithms are carefully designed and calibrated to ensure precision and robustness. Experimental results using Python demonstrate the efficiency and accuracy of our methods in rapidly converging to optimal parameters for IEEE 802.11-based VANETs. Compared to our previous models and existing approaches in the literature, the proposed optimization scheme offers substantial improvements in computation time, accuracy, and reliability, supporting real-time optimization of communication parameters. Bayesian Optimization Ad hoc networks Deep Learning Neural Networks Quality of Service Safety Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Wireless Networks → Version 1 posted Editorial decision: Revision requested 01 Oct, 2025 Reviews received at journal 02 Jun, 2025 Reviews received at journal 02 Jun, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers agreed at journal 20 May, 2025 Reviewers invited by journal 08 May, 2025 Editor assigned by journal 06 May, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 21 Mar, 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. 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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-6278319","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454648077,"identity":"9d41343b-6ced-4914-8f78-d8b48a313781","order_by":0,"name":"Aidan Wright","email":"","orcid":"","institution":"Oral Roberts University","correspondingAuthor":false,"prefix":"","firstName":"Aidan","middleName":"","lastName":"Wright","suffix":""},{"id":454648079,"identity":"2b6d8ded-e781-49c2-9775-683928810649","order_by":1,"name":"Shengli Ding","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Shengli","middleName":"","lastName":"Ding","suffix":""},{"id":454648080,"identity":"4dd60c7f-c547-4282-9b9f-d75c4a17cde3","order_by":2,"name":"Sandeep John Philips","email":"","orcid":"","institution":"Oral Roberts University","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"John","lastName":"Philips","suffix":""},{"id":454648081,"identity":"d216a9e5-a9ad-4928-9cbe-29e2215b786b","order_by":3,"name":"Rianne Ann Matthew","email":"","orcid":"","institution":"Oral Roberts University","correspondingAuthor":false,"prefix":"","firstName":"Rianne","middleName":"Ann","lastName":"Matthew","suffix":""},{"id":454648082,"identity":"73da99fd-fb51-4610-889a-d8136ac7ec8d","order_by":4,"name":"Xiaomin Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAjklEQVRIiWNgGAWjYHACxgcQOoF4LcwGJGthkyBNi8G1w88qPu45zMDPnmNAnBbJ2WlmN2c8O8wg2fOGSC380glmt3kOHGYwuEGsLWzS6d+KQVrsidbCL51jxgy2RYJ4v+QUS844kM4jceZZAXFaDG6nb/zw4YC1HH978gbitMAAD2nKR8EoGAWjYBTgBwDWPCiMaAHJcwAAAABJRU5ErkJggg==","orcid":"","institution":"Oral Roberts University","correspondingAuthor":true,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2025-03-21 14:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6278319/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6278319/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11276-025-04080-5","type":"published","date":"2026-01-14T16:28:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100615930,"identity":"6f5809ef-a9c6-42e5-beba-7b1742579f7d","added_by":"auto","created_at":"2026-01-19 17:38:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":758606,"visible":true,"origin":"","legend":"","description":"","filename":"AidanSpringerNature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6278319/v1_covered_7aa55147-9786-43ff-87fe-e1fc2b456373.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Model-based Constrained Bayesian Optimization of IEEE 802.11 VANET Safety Messaging","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":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Bayesian Optimization, Ad hoc networks, Deep Learning Neural Networks, Quality of Service, Safety","lastPublishedDoi":"10.21203/rs.3.rs-6278319/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6278319/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Vehicle safety remains a critical concern as road accidents occur daily. 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