Dynamic Task Offloading in Vehicular NetworksUsing Large Language Models: A Novel EdgeIntelligence Framework for Adaptive, Low-Latency,and Energy-Aware Decision Making

Dynamic Task Offloading in Vehicular NetworksUsing Large Language Models: A Novel EdgeIntelligence Framework for Adaptive, Low-Latency,and Energy-Aware Decision Making | 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 Dynamic Task Offloading in Vehicular NetworksUsing Large Language Models: A Novel EdgeIntelligence Framework for Adaptive, Low-Latency,and Energy-Aware Decision Making Zouheir Trabelsi, Muhammad Ali, Tariq Qayyum, Asadullah Tariq This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7637000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 13 You are reading this latest preprint version Abstract Task offloading in vehicular environments is essential for efficient computation and resource utilization among connected vehicles. Traditional approaches, such as reinforcement learning and federated learning, often struggle with dynamic adaptation, high communication costs, and scalability issues. This paper introduces a novel approach leveraging a Large Language Model (LLM) deployed at an edge node to optimize task offloading decisions. Unlike conventional machine learning models, the LLM is trained on a structured dataset specifically designed for vehicular task offloading, allowing it to process real time updates including speed, direction, CPU availability, and battery levels without extensive manual feature engineering. By analyzing multi dimensional data holistically, the LLM dynamically selects the most suitable node for task execution, significantly improving decision accuracy and adaptability. The proposed system outperforms traditional methods in scalability and responsiveness, particularly in large scale vehicular networks. However, challenges such as computational overhead, latency, and energy efficiency must be addressed for real world deployment. This work highlights the transformative potential of LLMs in intelligent vehicular decision making, paving the way for more efficient and adaptive task offloading strategies. Physical sciences/Engineering Physical sciences/Mathematics and computing Task Offloading Large Language Models (LLMs) Autonomous Mobility Edge Intelligence Intelligent Transportation Systems (ITS) and Latency Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Nov, 2025 Reviews received at journal 30 Oct, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviewers agreed at journal 16 Oct, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 11 Oct, 2025 Reviewers agreed at journal 05 Oct, 2025 Reviewers invited by journal 05 Oct, 2025 Editor assigned by journal 02 Oct, 2025 Editor invited by journal 25 Sep, 2025 Submission checks completed at journal 24 Sep, 2025 First submitted to journal 24 Sep, 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. 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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-7637000","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":530088712,"identity":"1537703a-c8f2-471a-8e64-e5dd1971bf07","order_by":0,"name":"Zouheir 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