An Integrated Fog Computing Approach to Improve Quality of Service in IoT Environment with Dynamic Task Scheduling and Resource Allocation

An Integrated Fog Computing Approach to Improve Quality of Service in IoT Environment with Dynamic Task Scheduling and Resource Allocation | 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 An Integrated Fog Computing Approach to Improve Quality of Service in IoT Environment with Dynamic Task Scheduling and Resource Allocation Widad Dilawer Issa, Marwan Aziz Mohammed This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6535806/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Exponential growth in IoT application involves managing latency, resource utilization and reliability in traditional cloud based architectures. In this research, we present a dynamic IoT-Fog-Cloud architecture using fuzzy logic, DQN and MOEA/D to optimize QoS metrics in real time. The real-time tasks generated by simulated IoT devices are falling through fuzzy logic at the Fog Master node and classified according to urgency. DQN schedules tasks dynamically so that during runtime it discovers optimal tasks solution on a set of Fog nodes. Resource allocation is enhanced by MOEA/D balancing computational loads with trade-off between latency, reliability and resource utilization. Here, we compare static and cloud-only architectures and show comparative simulations in iFogSim tool that achieve 28% reduction in latency, 19% increase in reliability and 22% improvement in resource utilization. The system’s hierarchical adaptive nature provides for an efficient task processing that is scalable and with superior QoS for IoT applications. The proposed framework is a scalable, robust and efficient means by which one can run real-time IoT tasks in fog-cloud computing environments by synergizing advanced deep learning and evolutionary algorithms. IoT QoS fog computing dynamic scheduling dynamic resource allocation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-6535806","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466297045,"identity":"4fe135a4-0c90-436c-8495-eaac3ccda1dd","order_by":0,"name":"Widad Dilawer Issa","email":"data:image/png;base64,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","orcid":"","institution":"Salahaddin University-Erbil","correspondingAuthor":true,"prefix":"","firstName":"Widad","middleName":"Dilawer","lastName":"Issa","suffix":""},{"id":466297046,"identity":"090c63f7-59e9-4331-9fd1-d348ae7904ae","order_by":1,"name":"Marwan Aziz Mohammed","email":"","orcid":"","institution":"Salahaddin University-Erbil","correspondingAuthor":false,"prefix":"","firstName":"Marwan","middleName":"Aziz","lastName":"Mohammed","suffix":""}],"badges":[],"createdAt":"2025-04-26 15:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6535806/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6535806/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86927486,"identity":"c17fecfb-145d-4114-9da2-1dfd79f2e9fa","added_by":"auto","created_at":"2025-07-17 09:02:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":873292,"visible":true,"origin":"","legend":"","description":"","filename":"WidadDilawerManuscriptCC.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6535806/v1_covered_10e1d081-634c-458b-aa90-9d2c0ca017d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Integrated Fog Computing Approach to Improve Quality of Service in IoT Environment with Dynamic Task Scheduling and Resource Allocation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"IoT, QoS, fog computing, dynamic scheduling, dynamic resource allocation","lastPublishedDoi":"10.21203/rs.3.rs-6535806/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6535806/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Exponential growth in IoT application involves managing latency, resource utilization and reliability in traditional cloud based architectures. 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