A Distributed Adaptive QoS-Aware TSCH Scheduling to Support Heterogeneous Traffic in IIoT Using Fuzzy Reinforcement Learning

A Distributed Adaptive QoS-Aware TSCH Scheduling to Support Heterogeneous Traffic in IIoT Using Fuzzy 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 Research Article A Distributed Adaptive QoS-Aware TSCH Scheduling to Support Heterogeneous Traffic in IIoT Using Fuzzy Reinforcement Learning Mehdi Zirak, Yasser Sedaghat, Mohammad Hossein Yaghmaee Moghaddam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7470984/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract The Industrial Internet of Things (IIoT), a pillar of Industry 4.0, collects environmental data via Low-Power and Lossy Networks (LLNs) and employs Time Slotted Channel Hopping (TSCH) to schedule communications among LLN nodes. Designing an efficient TSCH scheduler is challenging due to resource limitations, scalability concerns, environmental dynamics, traffic heterogeneity, and stringent Quality of Service (QoS) requirements. Various TSCH scheduling approaches leverage Reinforcement Learning (RL) to enable a distributed, dynamic, self-learning method that fine-tunes scheduling without prior knowledge of the environment. However, the main drawbacks of these approaches lie in their inability to support heterogeneous traffic QoS requirements and interpret scheduling states. Consequently, these algorithms often employ a best-effort strategy, which is prone to issues such as state-space explosion and slow convergence. In this paper, we propose a Distributed, Adaptive, and QoS-aware (DAQ) approach that support heterogeneous traffic using a hybrid design based on Fuzzy Rule-Based System (FRBS) and RL. The FRBS component, empowered by granular computing, discretizes large and continuous state spaces into a limited number of manageable states, thereby reducing state-space complexity and accelerating RL convergence. These states model TSCH scheduling as a Markov Decision Process, and RL component attempts to discover an optimal scheduling policy. Evaluation results indicate that, compared to existing approaches, the DAQ maintains performance for Non-Real-Time traffic while achieving significant improvements for Real-Time traffic. Specifically, it improves reliability, average delay, and maximum delay by up to 7%, 44%, and 43%, respectively, and reduces energy consumption by up to 44%. Industrial internet of things Time slotted channel hopping Scheduling Quality of service Heterogeneous traffic Reinforcement learning Fuzzy rule-based system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Mar, 2026 Reviews received at journal 07 Nov, 2025 Reviewers agreed at journal 02 Oct, 2025 Reviews received at journal 19 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 27 Aug, 2025 First submitted to journal 27 Aug, 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. 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. 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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-7470984","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":514502821,"identity":"bce9a2e9-529e-4135-b4ff-ac11d41585ff","order_by":0,"name":"Mehdi Zirak","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Mehdi","middleName":"","lastName":"Zirak","suffix":""},{"id":514502822,"identity":"6f50428a-aee0-4f4c-90ff-671ca868a616","order_by":1,"name":"Yasser Sedaghat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYLACHgaGBAYG5gMQHjPRWtjYEkjWwmNAnJsMDrA/fPCmpjaPf37PN6kbDHbyDOy8Dwho4TE2nHPseLHEMd5t0jkMyYYNzOz4rQNqYZPmYTuW2ADRwpzAwMxG0GHPf/P8O5Y4/xjPM6CWemK0MJgx87bVJG44BrQuh+EwYS2SQL9Izu07kLjxWJqxdY7BccM2Qlr4gCH24c23usR5hw8/vJ1TUS3Pz38MvxaF+w9A1GGYOxkYCNjBwCDfAKbqCKkbBaNgFIyCkQwAdYE/KyEOFK0AAAAASUVORK5CYII=","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":true,"prefix":"","firstName":"Yasser","middleName":"","lastName":"Sedaghat","suffix":""},{"id":514502823,"identity":"f236f9d6-819c-4352-a9cf-ed6a90dee739","order_by":2,"name":"Mohammad Hossein Yaghmaee Moghaddam","email":"","orcid":"","institution":"Ferdowsi University of Mashhad","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Hossein Yaghmaee","lastName":"Moghaddam","suffix":""}],"badges":[],"createdAt":"2025-08-27 11:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7470984/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7470984/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91609150,"identity":"6d3e2411-d2f4-434c-ba17-fa267468d65a","added_by":"auto","created_at":"2025-09-18 09:39:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":879923,"visible":true,"origin":"","legend":"","description":"","filename":"ADistributedAdaptiveQoSAwareTSCHSchedulingtoSupportHeterogeneousTrafficinIIoTUsingFuzzyReinforcementLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7470984/v1_covered_b7604482-1237-4751-81e8-2a8ded50e1bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Distributed Adaptive QoS-Aware TSCH Scheduling to Support Heterogeneous Traffic in IIoT Using Fuzzy 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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Industrial internet of things, Time slotted channel hopping, Scheduling, Quality of service, Heterogeneous traffic, Reinforcement learning, Fuzzy rule-based system","lastPublishedDoi":"10.21203/rs.3.rs-7470984/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7470984/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Industrial Internet of Things (IIoT), a pillar of Industry 4.0, collects environmental data via Low-Power and Lossy Networks (LLNs) and employs Time Slotted Channel Hopping (TSCH) to schedule communications among LLN nodes. Designing an efficient TSCH scheduler is challenging due to resource limitations, scalability concerns, environmental dynamics, traffic heterogeneity, and stringent Quality of Service (QoS) requirements. Various TSCH scheduling approaches leverage Reinforcement Learning (RL) to enable a distributed, dynamic, self-learning method that fine-tunes scheduling without prior knowledge of the environment. However, the main drawbacks of these approaches lie in their inability to support heterogeneous traffic QoS requirements and interpret scheduling states. Consequently, these algorithms often employ a best-effort strategy, which is prone to issues such as state-space explosion and slow convergence. In this paper, we propose a Distributed, Adaptive, and QoS-aware (DAQ) approach that support heterogeneous traffic using a hybrid design based on Fuzzy Rule-Based System (FRBS) and RL. The FRBS component, empowered by granular computing, discretizes large and continuous state spaces into a limited number of manageable states, thereby reducing state-space complexity and accelerating RL convergence. These states model TSCH scheduling as a Markov Decision Process, and RL component attempts to discover an optimal scheduling policy. Evaluation results indicate that, compared to existing approaches, the DAQ maintains performance for Non-Real-Time traffic while achieving significant improvements for Real-Time traffic. 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