Enhanced Novelty approaches for Resource Allocation Model for Multi-Cloud Environment in Vehicular Ad-Hoc Networks

Enhanced Novelty approaches for Resource Allocation Model for Multi-Cloud Environment 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 Enhanced Novelty approaches for Resource Allocation Model for Multi-Cloud Environment in Vehicular Ad-Hoc Networks Nici Marx V S, SUNDARAVADIVEL P, Augustian Isaac R, Elangovan D This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5399276/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 As the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. As a result, an efficient scheduling mechanism is required to determine the order of handling application requests, as well as the appropriate use of a broadcast media and data transfer. In this paper an innovative approach, incorporating the Crossover and Mutation (CM)-centered Marine Predator Algorithm (MPA) is introduced for an effective resource allocation. This strategic resource allocation optimally schedules resources within the Vehicular Edge computing (VEC) network, ensuring the most efficient utilization. The proposed method begins by the meticulous feature extraction from the Vehicular network model, with attributes such as mobility patterns, transmission medium, bandwidth, storage capacity, and packet delivery ratio. For further analysis the Elephant Herding Lion Optimizer (EHLO) algorithm is employed to pinpoint the most critical attributes. Subsequently the Modified Fuzzy C-Means (MFCM) algorithm is used for efficient vehicle clustering centred on selected attributes. These clustered vehicle characteristics are then transferred and stored within the cloud server infrastructure. The performance of the proposed methodology is evaluated using MATLAB software using simulation method. This study offers a comprehensive solution to the resource allocation challenge in Vehicular Cloud Networks, addresses the burgeoning demands of modern applications while ensuring QoS assurances and signifies a significant advancement in the field of VEC. Systems and Networking Vehicular Edge computing (VEC) Crossover and Mutation (CM) Marine Predator Algorithm (MPA) Elephant Herding Lion Optimizer (EHLO) modified Fuzzy c-means (MFCM) algorithm cloud server vehicle Full Text Additional Declarations The authors declare no competing interests. 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-5399276","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374627733,"identity":"276e5750-9bc8-4948-9884-b2fdc2b98546","order_by":0,"name":"Nici Marx V S","email":"","orcid":"","institution":"Saveetha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Nici","middleName":"Marx V","lastName":"S","suffix":""},{"id":374627734,"identity":"d1798528-5ba6-4244-ad33-db5270e5e190","order_by":1,"name":"SUNDARAVADIVEL P","email":"data:image/png;base64,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","orcid":"","institution":"Saveetha Engineering College","correspondingAuthor":true,"prefix":"","firstName":"SUNDARAVADIVEL","middleName":"","lastName":"P","suffix":""},{"id":374627735,"identity":"c62c0363-76d8-4bd9-96f3-e7d25dd1a880","order_by":2,"name":"Augustian Isaac R","email":"","orcid":"","institution":"Saveetha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Augustian","middleName":"Isaac","lastName":"R","suffix":""},{"id":374627736,"identity":"4d7544e9-0f06-4975-8a07-bbcacc68c852","order_by":3,"name":"Elangovan D","email":"","orcid":"","institution":"Saveetha Engineering College","correspondingAuthor":false,"prefix":"","firstName":"Elangovan","middleName":"","lastName":"D","suffix":""}],"badges":[],"createdAt":"2024-11-06 04:01:07","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5399276/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5399276/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68414881,"identity":"1f58be5d-0331-4f92-beee-d6dcab3093b0","added_by":"auto","created_at":"2024-11-07 04:49:05","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":748314,"visible":true,"origin":"","legend":"","description":"","filename":"NicimamJournalPaper3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5399276/v1_covered_d8b3d62e-efc3-4109-aadf-12c0eaa58f5b.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eEnhanced Novelty approaches for Resource Allocation Model for Multi-Cloud Environment in Vehicular Ad-Hoc Networks\u003c/strong\u003e\u003c/p\u003e","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":"Vehicular Edge computing (VEC), Crossover and Mutation (CM), Marine Predator Algorithm (MPA), Elephant Herding Lion Optimizer (EHLO), modified Fuzzy c-means (MFCM) algorithm, cloud server, vehicle","lastPublishedDoi":"10.21203/rs.3.rs-5399276/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5399276/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs the number of service requests for applications continues increasing due to various conditions, the limitations on the number of resources provide a barrier in providing the applications with the appropriate Quality of Service (QoS) assurances. 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