Optimized Energy Efficient Cluster based Routing Protocol for Real Time Communication in 5G enabled VANETs using Reinforcement Learning

Optimized Energy Efficient Cluster based Routing Protocol for Real Time Communication in 5G enabled VANETs using Reinforcement Learning | 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 Optimized Energy Efficient Cluster based Routing Protocol for Real Time Communication in 5G enabled VANETs using Reinforcement Learning Arun Kumar M, Ramesh Babu Somisetty, Pradeep M, Arunkumar C This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9460469/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The combination of 5G networks and Vehicular Ad-hoc Networks (VANETs) has transformed real-time communication to smart transportation systems, but challenges such as high energy usage, changes in topology, and rigid latency requirements still exist. This work offers a 5G-enabled VANET Optimized Energy-Efficient Cluster-Based Routing Protocol (OEECRP), which combines dynamic clustering, reinforcement learning (RL)-based routing, and 5G network slicing to provide sustainable, low-latency communication and therefore addresses these problems. Ensuring network lifespan, the protocol initially uses a weighted clustering technique to choose Cluster Heads (CHs) depending on residual energy, mobility, and 5G link reliability. A Q-learning model then dynamically adapts to traffic density, node energy levels, and latency limitations to achieve the best routing decisions, with a reward mechanism that balances energy efficiency and real-time performance. The Ultra-Reliable Low-Latency Communication (URLLC) that 5G network slicing provides priority to important traffic also reduces the reliance on centralized cloud infrastructure. The protocol is tested in the urban environment with the number of cars of 50–200 and is implemented in MATLAB R2023a and the 5G Toolbox and Communications Toolbox. Experimental results show that OEECRP beats five conventional models such as AODV, DSR, DSDV, GPSR and LEACH by approximately 30 percent reduction of energy consumption, less than 50ms end-to-end delay and increased packet delivery ratio (PDR). These results prove the reliability and scalability of OEECRP, thus offering a powerful solution to real-time, energy-conscious VANET communication. Physical sciences/Engineering Physical sciences/Mathematics and computing 5G-VANETs Energy-efficient routing Reinforcement learning Cluster-based communication Real-time VANETs Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 23 Apr, 2026 Editor invited by journal 23 Apr, 2026 Editor assigned by journal 20 Apr, 2026 Submission checks completed at journal 20 Apr, 2026 First submitted to journal 19 Apr, 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. 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-9460469","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":633631824,"identity":"9e43c111-7418-477a-b03c-c26d730f0d0b","order_by":0,"name":"Arun Kumar M","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYJACxgYo/YDhAEzMgDgtzAYgLTykaGGTQGjBA/hnnzF7OLNtm7x5A3da5Y8zdnL2DMwPPzAU3MGpReJcjrnhxrbbhnMO8G67zXMj2ZiHgc1YgsHgGW5rzvCYST5su804gwGoheHDgcQeBgYzoF8O49QhD9ViD9JS+OPDgfoeBvZveLUYgLQAHZYI0sLAc+NAAg8DD35bDM+wlUnOOHc7eQYz72ZpnjPJhj2HeYolEvBokTvDvE2yp+y27Qz23o0ffxyzk2dvb9/44cMf3FoQgBmZkUCEhlEwCkbBKBgFuAEAkA5Q1OekMf8AAAAASUVORK5CYII=","orcid":"","institution":"Sri Krishna College of Engineering and Technology","correspondingAuthor":true,"prefix":"","firstName":"Arun","middleName":"Kumar","lastName":"M","suffix":""},{"id":633631825,"identity":"f0dea36a-2a6d-4c4c-9624-6d02ae360b9f","order_by":1,"name":"Ramesh Babu Somisetty","email":"","orcid":"","institution":"AVN Institute of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ramesh","middleName":"Babu","lastName":"Somisetty","suffix":""},{"id":633631826,"identity":"e8f84332-987b-4560-b1b7-af418c4fecee","order_by":2,"name":"Pradeep M","email":"","orcid":"","institution":"RVS College of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Pradeep","middleName":"","lastName":"M","suffix":""},{"id":633631827,"identity":"da57e9f0-a17a-43c4-a03d-edbe2adbeb10","order_by":3,"name":"Arunkumar C","email":"","orcid":"","institution":"Sri Eshwar College of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Arunkumar","middleName":"","lastName":"C","suffix":""}],"badges":[],"createdAt":"2026-04-19 08:24:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9460469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9460469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108493399,"identity":"fb55ef33-b82c-4573-9701-3764ef4a6bbd","added_by":"auto","created_at":"2026-05-05 10:00:15","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786342,"visible":true,"origin":"","legend":"","description":"","filename":"OptimizedEnergyEfficientClusterbasedRoutingProtocolforRealTimeCommunicationin5GenabledVANETsusingReinforcementLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9460469/v1_covered_a59ce5fc-9d6a-434c-a6e2-0a09c86fc074.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimized Energy Efficient Cluster based Routing Protocol for Real Time Communication in 5G enabled VANETs using Reinforcement Learning","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":"5G-VANETs, Energy-efficient routing, Reinforcement learning, Cluster-based communication, Real-time VANETs","lastPublishedDoi":"10.21203/rs.3.rs-9460469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9460469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe combination of 5G networks and Vehicular Ad-hoc Networks (VANETs) has transformed real-time communication to smart transportation systems, but challenges such as high energy usage, changes in topology, and rigid latency requirements still exist. This work offers a 5G-enabled VANET Optimized Energy-Efficient Cluster-Based Routing Protocol (OEECRP), which combines dynamic clustering, reinforcement learning (RL)-based routing, and 5G network slicing to provide sustainable, low-latency communication and therefore addresses these problems. Ensuring network lifespan, the protocol initially uses a weighted clustering technique to choose Cluster Heads (CHs) depending on residual energy, mobility, and 5G link reliability. A Q-learning model then dynamically adapts to traffic density, node energy levels, and latency limitations to achieve the best routing decisions, with a reward mechanism that balances energy efficiency and real-time performance. The Ultra-Reliable Low-Latency Communication (URLLC) that 5G network slicing provides priority to important traffic also reduces the reliance on centralized cloud infrastructure. The protocol is tested in the urban environment with the number of cars of 50\u0026ndash;200 and is implemented in MATLAB R2023a and the 5G Toolbox and Communications Toolbox. Experimental results show that OEECRP beats five conventional models such as AODV, DSR, DSDV, GPSR and LEACH by approximately 30 percent reduction of energy consumption, less than 50ms end-to-end delay and increased packet delivery ratio (PDR). These results prove the reliability and scalability of OEECRP, thus offering a powerful solution to real-time, energy-conscious VANET communication.\u003c/p\u003e","manuscriptTitle":"Optimized Energy Efficient Cluster based Routing Protocol for Real Time Communication in 5G enabled VANETs using Reinforcement Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 15:06:16","doi":"10.21203/rs.3.rs-9460469/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-23T13:56:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-23T10:25:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-20T12:10:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-20T12:10:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-19T08:17:29+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"cb0ff05d-8740-4d29-bd7d-6135519d305f","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67438439,"name":"Physical sciences/Engineering"},{"id":67438440,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-05-04T15:06:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 15:06:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9460469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9460469","identity":"rs-9460469","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}