CTWS: A Crowd-Powered Framework for Scheduling Decomposable Complex-Tasks

CTWS: A Crowd-Powered Framework for Scheduling Decomposable Complex-Tasks | 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 CTWS: A Crowd-Powered Framework for Scheduling Decomposable Complex-Tasks Suneel Kumar, Sarvesh Pandey This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4258155/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 A crowdsourcing framework involves a pool of workers competing to solve a distributed task posted by a requester on a crowdsourcing platform. Each worker divides a decomposable task (i.e., a complex distributed task) into multiple subtasks, considering their uniqueness, and shares such decomposition with the platform. It is challenging for a requester to map an optimal worker set with a decomposable task, ensuring a cost-effective solution. We present the Complex-Task Worker-Set (CTWS) method to address this challenge. The algorithm optimizes the solution cost of a decomposable task and reduces the computation time of selecting different workers. This paper discusses a model integrated with CTWS of a crowdsourcing environment where workers and requester(s) work together through the crowdsourcing platform. We use an aging technique, which plays a crucial role in tie-break when an ambiguity arises to select the best path among a set of solution paths. Thereafter, the optimized solution is provided to the requesters based on the requester's constraints and requirements. The results confirm the effectiveness of the CTWS against the state-of-the-art. Crowdsource model Dijkstra Algorithm Time Complexity analysis Aging technique 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-4258155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290759195,"identity":"5c5f2f5a-c11d-4337-a730-0823f0ce8a93","order_by":0,"name":"Suneel Kumar","email":"","orcid":"","institution":"BHU","correspondingAuthor":false,"prefix":"","firstName":"Suneel","middleName":"","lastName":"Kumar","suffix":""},{"id":290759199,"identity":"0466569a-5508-4f62-a429-4cb77c1716b8","order_by":1,"name":"Sarvesh Pandey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYPCCAxDqYwOUkUCsFsaZJGth5oVpwQfk288+k/jw504e/+zmY9K2O+4kNrAffsDwcAduLQZn0s0kZ7Y9K5a4cyxNOvfMs8QGnjQDhsQzeLQwpDEb8zYcTmy4kWMmndsGZDDkMDAktuFxWP8zZuM/fw4nzr+R/03aEqSF/w1+LQw30hgfM7AdTtxwI4dNmhGkRYKALQY3njE+7G07XGx4I83YsvfMYeM2iWcGB/A7LI3hwI8/h/PkbiQ/vPFzx2HZfv7khw9/4nMYFCQAMYsEiMXGAE8MhLUwfyBG5SgYBaNgFIw8AAB8g1zgUL2I+QAAAABJRU5ErkJggg==","orcid":"","institution":"MMV, BHU","correspondingAuthor":true,"prefix":"","firstName":"Sarvesh","middleName":"","lastName":"Pandey","suffix":""}],"badges":[],"createdAt":"2024-04-12 13:46:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4258155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4258155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76400532,"identity":"c4f2abc3-3f1a-47c1-848a-fbe34765796c","added_by":"auto","created_at":"2025-02-16 15:47:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972866,"visible":true,"origin":"","legend":"","description":"","filename":"SuperComputingmanuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4258155/v1_covered_9e80c27b-33b9-4170-9a47-eac8c5c91a25.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CTWS: A Crowd-Powered Framework for Scheduling Decomposable Complex-Tasks","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":"Crowdsource model, Dijkstra Algorithm, Time Complexity analysis, Aging technique","lastPublishedDoi":"10.21203/rs.3.rs-4258155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4258155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA crowdsourcing framework involves a pool of workers competing to solve a distributed task posted by a requester on a crowdsourcing platform. 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