D2DA: Machine Learning-empowered Distributed Authorization Model in Smart Homes

D2DA: Machine Learning-empowered Distributed Authorization Model in Smart Homes | 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 D2DA: Machine Learning-empowered Distributed Authorization Model in Smart Homes Hongjuan Kang, Bing Guo, Na Shi, Dejun Huang, Awen Luo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6750529/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted 10 You are reading this latest preprint version Abstract In the context of the IoT platform, the smart home represents a quintessential application scenario. Here, device-to-device (D2D) collaboration serves as the core element of its ecosystem, playing a crucial role in implementing diversified automated execution scenarios that are customized to fulfill user requirements. The progressive integration of edge computing and AI technologies has enhanced the collaboration among heterogeneous devices. Nevertheless, the conventional centralized D2D collaboration authorization decision-making supported by a single IoT Hub violates the Principle of Least Privilege (PoLP), which is a foundational design tenet that has been empirically validated as an optimal engineering practice for enhancing system security and reliability in IoT ecosystems. If there is a trade-off of PoLP violations, it fails to meet the users’ Quality of Experience (QoE). To address this issue, we propose D2DA, a distributed authorization decision-making model for smart home D2D collaboration, which constructs a distributed decision-making consensus network suitable for the edge side of smart homes by leveraging distributed ledger technology. D2DA presents a machine learning algorithm with a time complexity of O(n). Through this algorithm, consensus nodes can be efficiently and dynamically selected. Furthermore, D2DA ensures the security of the D2D collaboration process via wallets and hash verification. Extensive experiments conducted on a real-world smart home scenario validate that the decision-making latency of D2DA is on par with that of a single 1 IoT Hub mode. The average latency for verifying the correctness of the newly added execution results is only 0.08% of the system time of D2DA, which is negligible. IoT Access Control Distributed Authorization D2D Collaboration Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Peer-to-Peer Networking and Applications → Version 1 posted Editorial decision: Revision requested 18 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 19 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers invited by journal 17 Aug, 2025 Editor assigned by journal 17 Aug, 2025 Submission checks completed at journal 30 May, 2025 First submitted to journal 26 May, 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-6750529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":502361241,"identity":"31152a53-f974-4c9a-a491-7c2176cfdd0c","order_by":0,"name":"Hongjuan Kang","email":"data:image/png;base64,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","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Kang","suffix":""},{"id":502361242,"identity":"0e5fc0e3-873a-4235-9ca6-e5311c30219f","order_by":1,"name":"Bing Guo","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Bing","middleName":"","lastName":"Guo","suffix":""},{"id":502361243,"identity":"ad6a3b00-0158-43a8-a874-c7e328a7db26","order_by":2,"name":"Na Shi","email":"","orcid":"","institution":"Sichuan Changhong Electric Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Shi","suffix":""},{"id":502361244,"identity":"5c777e38-d734-40d2-a277-22e77fca9994","order_by":3,"name":"Dejun Huang","email":"","orcid":"","institution":"Sichuan Changhong Electric Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Dejun","middleName":"","lastName":"Huang","suffix":""},{"id":502361245,"identity":"ab4d9586-fc29-4f36-b931-be1bceacdc76","order_by":4,"name":"Awen Luo","email":"","orcid":"","institution":"Sichuan Changhong Electric Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Awen","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-05-26 11:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6750529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6750529/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12083-025-02164-9","type":"published","date":"2025-11-15T15:58:29+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96105123,"identity":"2062e6bd-9767-44d0-a80f-146246257d05","added_by":"auto","created_at":"2025-11-17 16:08:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":703653,"visible":true,"origin":"","legend":"","description":"","filename":"P2P250526D2DAMachineLearningempoweredDistributedAuthorizationModelinSmartHomes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6750529/v1_covered_4f03c3aa-5671-4981-a385-14bddbf70ed9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"D2DA: Machine Learning-empowered Distributed Authorization Model in Smart Homes","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"peer-to-peer-networking-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ppna","sideBox":"Learn more about [Peer-to-Peer Networking and Applications](http://link.springer.com/journal/12083)","snPcode":"12083","submissionUrl":"https://submission.nature.com/new-submission/12083/3","title":"Peer-to-Peer Networking and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"IoT, Access Control, Distributed Authorization, D2D Collaboration, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-6750529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6750529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In the context of the IoT platform, the smart home represents a quintessential application scenario. 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