Development of Task Allocation in Mobile Crowdsensing Using User Mobility Prediction, Participation Rate Prediction, and Population-Based Metaheuristic Algorithms

Development of Task Allocation in Mobile Crowdsensing Using User Mobility Prediction, Participation Rate Prediction, and Population-Based Metaheuristic Algorithms | 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 Development of Task Allocation in Mobile Crowdsensing Using User Mobility Prediction, Participation Rate Prediction, and Population-Based Metaheuristic Algorithms Seyed Reza Amerizadeh, Mohammad Reza Khayyambashi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7312271/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Wireless Networks → Version 1 posted 17 You are reading this latest preprint version Abstract This paper presents a two-phase framework for task allocation in mobile crowdsensing (MCS), consisting of offline training and task allocation phases. In the offline training phase, user mobility patterns and participation rates in sensing blocks are trained and predicted. In the task allocation phase, tasks are assigned to users based on their predicted future locations and participation rates in the blocks. The task allocation is performed using Binary Enhanced Whale Optimization (BE-WOA), U-shaped Binary Particle Swarm Optimization (UBPSO), and Ant Colony System Optimization (ACS) to achieve the dual objectives of maximizing data quality and minimizing users' energy consumption. Two real-world datasets were utilized in the offline training phase to predict users' future movement paths, achieving an average MAE of 0.004579 and 0.00699, while the prediction of participation rates in blocks yielded an MAE of 0.2465. In the task allocation phase, 11 distinct task allocation scenarios comprising 110 simulations were conducted, resulting in an average 70% energy savings for users and maximum data quality. Additionally, the proposed approach demonstrated a 37.6% improvement in coverage rate compared to prior research. Based on the cost function, BE-WOA outperformed the random and greedy algorithms by 124% and 65%, respectively, and was identified as the best task allocation algorithm in this study. This work provides valuable insights into IoT-driven data collection systems, offering an effective and energy-efficient solution to maintain high data quality and user engagement with minimal resource consumption. Mobile Crowdsensing (MCS) Task Allocation Binary Enhanced Whale Optimization Algorithm (BE-WOA) U-Shaped Binary Particle Swarm Optimization (UBPSO) Ant Colony System (ACS) Gated Recurrent Unit (GRU) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2026 Read the published version in Wireless Networks → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 16 Nov, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 30 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 21 Oct, 2025 Submission checks completed at journal 12 Aug, 2025 First submitted to journal 06 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. 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-7312271","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533293539,"identity":"f48295ab-d6a1-4825-b64d-7e78f4cfb32b","order_by":0,"name":"Seyed Reza Amerizadeh","email":"","orcid":"","institution":"University of Isfahan","correspondingAuthor":false,"prefix":"","firstName":"Seyed","middleName":"Reza","lastName":"Amerizadeh","suffix":""},{"id":533293540,"identity":"bd86e959-8158-4bec-8347-b3675f9d0643","order_by":1,"name":"Mohammad Reza 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In the offline training phase, user mobility patterns and participation rates in sensing blocks are trained and predicted. In the task allocation phase, tasks are assigned to users based on their predicted future locations and participation rates in the blocks. The task allocation is performed using Binary Enhanced Whale Optimization (BE-WOA), U-shaped Binary Particle Swarm Optimization (UBPSO), and Ant Colony System Optimization (ACS) to achieve the dual objectives of maximizing data quality and minimizing users' energy consumption. Two real-world datasets were utilized in the offline training phase to predict users' future movement paths, achieving an average MAE of 0.004579 and 0.00699, while the prediction of participation rates in blocks yielded an MAE of 0.2465. In the task allocation phase, 11 distinct task allocation scenarios comprising 110 simulations were conducted, resulting in an average 70% energy savings for users and maximum data quality. Additionally, the proposed approach demonstrated a 37.6% improvement in coverage rate compared to prior research. Based on the cost function, BE-WOA outperformed the random and greedy algorithms by 124% and 65%, respectively, and was identified as the best task allocation algorithm in this study. This work provides valuable insights into IoT-driven data collection systems, offering an effective and energy-efficient solution to maintain high data quality and user engagement with minimal resource consumption.\u003c/p\u003e","manuscriptTitle":"Development of Task Allocation in Mobile Crowdsensing Using User Mobility Prediction, Participation Rate Prediction, and Population-Based Metaheuristic Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-31 03:21:39","doi":"10.21203/rs.3.rs-7312271/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T13:39:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T03:47:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167885570212892899454426472141430053036","date":"2025-11-17T03:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96491330154303398030049924247910109857","date":"2025-11-17T02:39:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-17T02:30:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"74836161869284539858624189216407341606","date":"2025-11-17T02:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281021442859290573250172475474993456624","date":"2025-11-17T01:43:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T16:36:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"166179700651893725890594772346412089347","date":"2025-11-16T15:13:25+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-04T10:27:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223939269324681555788727694231701019473","date":"2025-10-31T02:43:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172831017011984011595608455049339211494","date":"2025-10-31T02:26:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"182391601258518656858835655753558777301","date":"2025-10-22T06:40:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T06:35:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T06:16:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T06:10:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wireless Networks","date":"2025-08-06T18:03:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"59f6185c-1c87-4346-9ca3-79ea933ed70c","owner":[],"postedDate":"October 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-09T16:03:15+00:00","versionOfRecord":{"articleIdentity":"rs-7312271","link":"https://doi.org/10.1007/s11276-025-04084-1","journal":{"identity":"wireless-networks","isVorOnly":false,"title":"Wireless Networks"},"publishedOn":"2026-03-04 15:58:38","publishedOnDateReadable":"March 4th, 2026"},"versionCreatedAt":"2025-10-31 03:21:39","video":"","vorDoi":"10.1007/s11276-025-04084-1","vorDoiUrl":"https://doi.org/10.1007/s11276-025-04084-1","workflowStages":[]},"version":"v1","identity":"rs-7312271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7312271","identity":"rs-7312271","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}