A DePIN Architecture for Large Language Model-driven Integrated Sensing and Communications

A DePIN Architecture for Large Language Model-driven Integrated Sensing and Communications | 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 A DePIN Architecture for Large Language Model-driven Integrated Sensing and Communications Jiahao Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8801619/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Integrated Sensing and Communications (ISAC) enables real-time perception and coor- dination in the Internet of Things. However, current ISAC architectures face critical challenges in decentralization, trust, and incentivization. This paper introduces a novel Decentralized Physical Infrastructure Network (DePIN) approach to ISAC, where distributed nodes utilize Large Language Models (LLMs) to analyze multimodal sensor data while earning crypto-token rewards. At the core of this framework, we propose a permissioned blockchain that semantically validates LLM- generated content through oracle contracts and a Proof-of-Code (PoC) consensus mechanism. Each DePIN node leverages an onboard LLM to process sensor data and generate context-aware decisions, evaluated based on semantic quality metrics including coherence, novelty, and factual alignment. We formulate a constrained incentive maximization problem that jointly considers sensing qual ity, LLM inference accuracy, and system cost. We propose a Deep Reinforcement Learning (DRL) algorithm that adaptively optimizes the ISAC and DePIN token incentives across nodes to solve this. Extensive simulations demonstrate that our DePIN-based approach significantly outperforms conventional strategies in maximizing incentive accumulation while minimizing operation costs in dynamic LLM-driven ISAC systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Integrated sensing and communications Blockchain Large language models Deep reinforcement learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Mar, 2026 Reviewers agreed at journal 01 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviews received at journal 25 Feb, 2026 Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor invited by journal 11 Feb, 2026 Editor assigned by journal 06 Feb, 2026 Submission checks completed at journal 06 Feb, 2026 First submitted to journal 05 Feb, 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. 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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-8801619","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":597477475,"identity":"c1b4f0f2-3782-4a0a-ac87-ed77b73227a0","order_by":0,"name":"Jiahao Guo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYJAC5r//bBjYSNPDw5ZGupbDJKjmbz+d+EGC57w9n3TzA4YfFdsIa5E4k7tZwkDidmKbzDEDxp4ztwlrMZDg3SCRYHA7gQ1IMjO2Eadl848DCefs2STSPxCtZZtkw4EDjG0SOUTaAvTLNmvGhuREoJaCg0T5hb/97ObbjA129vIz0jc++FFBhBYUcIBE9aNgFIyCUTAKcAEASUE1+ksrbJYAAAAASUVORK5CYII=","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":true,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2026-02-06 01:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8801619/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8801619/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104399071,"identity":"33d849f6-11a7-4144-af3e-b3384b68874d","added_by":"auto","created_at":"2026-03-11 12:04:39","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":979842,"visible":true,"origin":"","legend":"","description":"","filename":"final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8801619/v1_covered_3dd7cce9-885b-4082-b2d4-d046e7ad6e0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A DePIN Architecture for Large Language Model-driven Integrated Sensing and Communications","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":"Integrated sensing and communications, Blockchain, Large language models, Deep reinforcement learning","lastPublishedDoi":"10.21203/rs.3.rs-8801619/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8801619/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIntegrated Sensing and Communications (ISAC) enables real-time perception and coor- dination in the Internet of Things. 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