Real-Time and Low Latency DynamicComputational Offloading Deep Neural NetworkTechnique in the Internet of Vehicles (IoV)

Real-Time and Low Latency DynamicComputational Offloading Deep Neural NetworkTechnique in the Internet of Vehicles (IoV) | 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 Real-Time and Low Latency DynamicComputational Offloading Deep Neural NetworkTechnique in the Internet of Vehicles (IoV) Amar Nayak Amar, Sanjay Agrawal Sanju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5285396/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Recently, a new computing paradigm, edge computing, has gained attention because of high-throughput and short-delay task offloading for large-scale Internet-of-Things (IoT) applications. Computational task offloading in the IoT faces significant challenges relatedto energy consumption, performance, security, and latency. Traditionalmachine-learning cloud-based approaches may not meet the real-timetask offloading and processing requirements of IoT applications. To de-termine the potential of fully connected vehicles, the Internet of Vehicles(IoV) requires high-bandwidth and low-latency services. IoT devices inIoV networks have limited resources to perform large and complex op-erations. This issue of limited resources can be solved by dynamic task offloading using cloud and edge computing to improve IoV performance.This paper proposes an innovative Deep Neural Network (DNN) basedReal-Time and Low Latency dynamic computational offloading alloca-tion method that combines the strengths of Cloud and Edge computingwith low-latency computational offloading for IoT applications, such asIoV, Intelligent Transportation Systems, and smart cities. The proposedDNN model is trained to make decisions about where and when to offloadcomputational tasks dynamically based on input features like availableresources, vehicle network conditions, and speed. Finally, for implement-ing and evaluating the performances of proposed low latency, real-timedynamic computational offloading real traffic data are used to build upthe simulation scenarios. Experimental results indicate that our methodachieves improved performance in terms of latency, dynamic efficient re-source allocation, and computational task offloading compared to thetraditional machine learning cloud-based task offloading method. Cloud computing Edge computing Low latency Computational offloading Real time IoV DNN Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 20 Oct, 2024 Submission checks completed at journal 20 Oct, 2024 First submitted to journal 17 Oct, 2024 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-5285396","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368181348,"identity":"df682dcf-37fc-482b-a826-1fee9632ff67","order_by":0,"name":"Amar Nayak Amar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYPACCTn7+4cPgBgyxGqxMGa4wZYA0sJDrJaKxIYbPAYgFmEt/NMOP/vAuEMisXF2z+dXN2oseBjYDx/dgE+LxO004xmMZySMm2XObrPOOQZ0GE9a2g281txOMGZgbJOQbWPI3WacwwbUIsFjhleL/O30zyAtjD0MOc+Mc/4RocXgdg7YFsUZEjnMj3PbiNBieDunmCGxTcLYgOeYGXNunwQPGyG/yN1O38zwsa1OzoC9+fHnnG91cvzsh4/h9z4IJEAoNgkwSVA5EmD+QIrqUTAKRsEoGDkAAHEgQ9Q7l+v+AAAAAElFTkSuQmCC","orcid":"","institution":"National Institute of Technical Teachers Training and Research","correspondingAuthor":true,"prefix":"","firstName":"Amar","middleName":"Nayak","lastName":"Amar","suffix":""},{"id":368181349,"identity":"cf179f46-b6da-456a-a34b-6a1f5ffa9918","order_by":1,"name":"Sanjay Agrawal Sanju","email":"","orcid":"","institution":"National Institute of Technical Teachers Training and Research","correspondingAuthor":false,"prefix":"","firstName":"Sanjay","middleName":"Agrawal","lastName":"Sanju","suffix":""}],"badges":[],"createdAt":"2024-10-17 23:23:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5285396/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5285396/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67817948,"identity":"9a603dee-526e-4fa4-bae3-bb6ac8960485","added_by":"auto","created_at":"2024-10-30 04:44:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":513949,"visible":true,"origin":"","legend":"","description":"","filename":"FinalPaper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5285396/v1_covered_c4a174e8-1ba5-4308-b12c-3c560ccf494f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-Time and Low Latency DynamicComputational Offloading Deep Neural NetworkTechnique in the Internet of Vehicles (IoV)","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cloud computing, Edge computing, Low latency, Computational offloading, Real time, IoV, DNN","lastPublishedDoi":"10.21203/rs.3.rs-5285396/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5285396/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecently, a new computing paradigm, edge computing, has gained attention because of high-throughput and short-delay task offloading for large-scale Internet-of-Things (IoT) applications. Computational task offloading in the IoT faces significant challenges relatedto energy consumption, performance, security, and latency. Traditionalmachine-learning cloud-based approaches may not meet the real-timetask offloading and processing requirements of IoT applications. To de-termine the potential of fully connected vehicles, the Internet of Vehicles(IoV) requires high-bandwidth and low-latency services. IoT devices inIoV networks have limited resources to perform large and complex op-erations. This issue of limited resources can be solved by dynamic task offloading using cloud and edge computing to improve IoV performance.This paper proposes an innovative Deep Neural Network (DNN) basedReal-Time and Low Latency dynamic computational offloading alloca-tion method that combines the strengths of Cloud and Edge computingwith low-latency computational offloading for IoT applications, such asIoV, Intelligent Transportation Systems, and smart cities. The proposedDNN model is trained to make decisions about where and when to offloadcomputational tasks dynamically based on input features like availableresources, vehicle network conditions, and speed. Finally, for implement-ing and evaluating the performances of proposed low latency, real-timedynamic computational offloading real traffic data are used to build upthe simulation scenarios. 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