A Reputation-Based Incentive and Allocation Model Using Double Auction for Mobile Crowdsensing

A Reputation-Based Incentive and Allocation Model Using Double Auction for Mobile Crowdsensing | 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 Reputation-Based Incentive and Allocation Model Using Double Auction for Mobile Crowdsensing Chieh-Yi Hsuan, Yu-Ling Hsueh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8088245/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 7 You are reading this latest preprint version Abstract Mobile crowdsensing (MCS) leverages distributed mobile users to complete large-scale sensing tasks. With the rapid advancement of sensing capabilities in urban environments, data acquisition has become increasingly efficient and scalable. However, promoting user participation and improving task quality remain significant challenges. While auction-based models in MCS aim to optimize task allocation efficiency and incentivize high-quality data contributions, they often fail to account for the effective evaluation of worker distribution and task quality. To address these limitations, we propose a Reputation-Based Incentive Mechanism Double-Auction Model (RBIM), which considers both uneven worker arrivals and the diminishing returns of task quality. RBIM categorizes tasks based on difficulty levels, allowing workers to choose tasks aligned with their preferences. The task quality is evaluated by integrating platform costs and requester satisfaction, which subsequently influence both worker compensation and reputation scores. Moreover, the payment scheme incorporates reputation-based adjustments to further incentivize reliable participation and enhance task completion rates. Experimental results demonstrate that RBIM consistently outperforms several benchmark algorithms, significantly improving task completion rates, task quality, and overall system utility. Additionally, the proposed mechanism satisfies essential economic properties including individual rationality (IR), truthfulness (TF), and budget feasibility, while maximizing social welfare within given budget constraints (BC). Incentive mechanism Mobile crowdsensing Task allocation and Reputation system Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 25 Feb, 2026 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 02 Dec, 2025 Reviewers invited by journal 02 Dec, 2025 Editor assigned by journal 13 Nov, 2025 Submission checks completed at journal 13 Nov, 2025 First submitted to journal 11 Nov, 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. 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-8088245","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":554438994,"identity":"36f46a51-26ae-410c-b3a2-53a2c99527c4","order_by":0,"name":"Chieh-Yi Hsuan","email":"","orcid":"","institution":"National Chung Cheng University","correspondingAuthor":false,"prefix":"","firstName":"Chieh-Yi","middleName":"","lastName":"Hsuan","suffix":""},{"id":554438995,"identity":"6f7a0213-2187-4bc3-86ca-4558a6af71ac","order_by":1,"name":"Yu-Ling Hsueh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYFAC5oYDDAxAxMDD+ABE8hHWwgjXwmwAItmI0cIA1cImAeIT1GJwI7HxcMGvO/YGx3uPVX7NsZNhY2B++OgGfi0Nh2f2PUvccOZc2m3ZbclAh7EZG+cQ0sLbczjB4EaO2W3JbcxALTxs0sRosTe4/8asWHJbPZFaeH4cZtxwg8eM8eO2w4S1SJ55CLSl4XDizDM5xtKM247zsDET8Avf8eTDn3n+HLbnO37G8OPPbdX2/OzNDx/j06JwAEgwtkE4zDxgEo9yEJBvAJF/IBzGHwRUj4JRMApGwcgEABESUsdjtRBNAAAAAElFTkSuQmCC","orcid":"","institution":"National Chung Cheng University","correspondingAuthor":true,"prefix":"","firstName":"Yu-Ling","middleName":"","lastName":"Hsueh","suffix":""}],"badges":[],"createdAt":"2025-11-11 15:08:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8088245/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8088245/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97483633,"identity":"a9f92b98-3468-4ace-b9db-210a331adfb9","added_by":"auto","created_at":"2025-12-04 23:11:17","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5118,"visible":true,"origin":"","legend":"","description":"","filename":"e6df2a4ef3b64b94b8c763f6ffe66f17.json","url":"https://assets-eu.researchsquare.com/files/rs-8088245/v1/cf63b5bf0eb0c5b809fbd257.json"},{"id":97668842,"identity":"18eef898-501c-4da2-98ea-c29a207842db","added_by":"auto","created_at":"2025-12-08 09:26:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1011497,"visible":true,"origin":"","legend":"","description":"","filename":"snarticle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8088245/v1_covered_daf763f5-76f5-4459-afe6-1b1012206b94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Reputation-Based Incentive and Allocation Model Using Double Auction for Mobile Crowdsensing","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":"geoinformatica","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gein","sideBox":"Learn more about [GeoInformatica](http://link.springer.com/journal/10707)","snPcode":"10707","submissionUrl":"https://submission.nature.com/new-submission/10707/3","title":"GeoInformatica","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Incentive mechanism, Mobile crowdsensing, Task allocation, and Reputation system","lastPublishedDoi":"10.21203/rs.3.rs-8088245/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8088245/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mobile crowdsensing (MCS) leverages distributed mobile users to complete large-scale sensing tasks. 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