Improved DV-HOP Localization Algorithm Based on Grey Wolf Optimization

Improved DV-HOP Localization Algorithm Based on Grey Wolf Optimization | 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 Improved DV-HOP Localization Algorithm Based on Grey Wolf Optimization Siqi Yang, Xiaohua Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5797039/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 DV-HOP is a widely used localization algorithm, commonly applied in areas such as node localization in Wireless Sensor Networks (WSN), deployment of Internet of Things (IoT) devices, and navigation for mobile robots. However, the DV-HOP algorithm faces challenges in practical applications, including cumulative hop count errors between nodes, inaccuracies in estimated hop distances, and computational bias introduced by the least squares method when dealing with nonlinear problems. To address these issues, this paper proposes an improved DV-HOP localization algorithm based on Grey Wolf Optimization (GWO). By incorporating dual communication radii, weighted hop distance correction, and an improved Grey Wolf Optimization algorithm (IGWO), the proposed approach enhances the localization accuracy of nodes in WSN. First, the dual communication radii strategy is utilized to refine the hop count between nodes, improving the accuracy of hop estimations. Second, a hop adjustment factor is introduced to further correct the minimum hop count between anchor nodes, resulting in more precise average hop distances. Weighted optimization of estimated hop distances from unknown nodes to anchor nodes is achieved using the minimum mean square error criterion. Finally, the improved Grey Wolf Optimization algorithm replaces the least squares method for solving the coordinates of unknown nodes. Simulation results demonstrate that the proposed improved DV-HOP algorithm consistently achieves lower localization errors under various experimental conditions. Compared with other methods, it provides higher localization accuracy, verifying its effectiveness and advantages. Wireless Sensor Networks DV-HOP Localization Algorithm Grey Wolf Optimization Algorithm Hop Count Correction 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-5797039","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":401990032,"identity":"df45ba2e-31e2-41bf-9df8-c1021de33fdf","order_by":0,"name":"Siqi Yang","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Siqi","middleName":"","lastName":"Yang","suffix":""},{"id":401990037,"identity":"4064d05d-3f39-496d-b3b2-050de6be037a","order_by":1,"name":"Xiaohua Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDACCTBpgyRygDgtaaRrOUyCFoPbPYafC36dl+NnYD74ubCNQY7vRgLj5wI8WiTnnDGWntl321iygS1ZemYbg7HkjQRm6Rl4tPBL5G6Q5u25nbjhAI8ZM28bQ+KGGwlszDx4tLBJ5G7+zdtzrn7/Af5vIC31BLUAbdkmzfPjQIIBAw8bSEuCASEtkjPyv1nzNiQbzjjMZizNc07CcOaZh83S+LQY3EhLvs3zx06ev7354WeeMht5vuPJBz/j0wIGjG1AghnMBEUTYwMhDUDwhwg1o2AUjIJRMHIBADW4RPIdBjBbAAAAAElFTkSuQmCC","orcid":"","institution":"East China Normal University","correspondingAuthor":true,"prefix":"","firstName":"Xiaohua","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-01-09 13:53:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5797039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5797039/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":76373575,"identity":"0ef0d777-4c86-4467-a36a-3fa8ab85aa57","added_by":"auto","created_at":"2025-02-15 20:46:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425953,"visible":true,"origin":"","legend":"","description":"","filename":"ImprovedDVHOPLocalizationAlgorithmBasedonGreyWolfOptimization.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5797039/v1_covered_7d249829-0638-470d-a5cf-a9e9ae02bea2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved DV-HOP Localization Algorithm Based on Grey Wolf Optimization","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":"Wireless Sensor Networks, DV-HOP Localization Algorithm, Grey Wolf Optimization Algorithm, Hop Count Correction","lastPublishedDoi":"10.21203/rs.3.rs-5797039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5797039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDV-HOP is a widely used localization algorithm, commonly applied in areas such as node localization in Wireless Sensor Networks (WSN), deployment of Internet of Things (IoT) devices, and navigation for mobile robots. However, the DV-HOP algorithm faces challenges in practical applications, including cumulative hop count errors between nodes, inaccuracies in estimated hop distances, and computational bias introduced by the least squares method when dealing with nonlinear problems. To address these issues, this paper proposes an improved DV-HOP localization algorithm based on Grey Wolf Optimization (GWO). By incorporating dual communication radii, weighted hop distance correction, and an improved Grey Wolf Optimization algorithm (IGWO), the proposed approach enhances the localization accuracy of nodes in WSN. First, the dual communication radii strategy is utilized to refine the hop count between nodes, improving the accuracy of hop estimations. Second, a hop adjustment factor is introduced to further correct the minimum hop count between anchor nodes, resulting in more precise average hop distances. Weighted optimization of estimated hop distances from unknown nodes to anchor nodes is achieved using the minimum mean square error criterion. Finally, the improved Grey Wolf Optimization algorithm replaces the least squares method for solving the coordinates of unknown nodes. Simulation results demonstrate that the proposed improved DV-HOP algorithm consistently achieves lower localization errors under various experimental conditions. Compared with other methods, it provides higher localization accuracy, verifying its effectiveness and advantages.\u003c/p\u003e","manuscriptTitle":"Improved DV-HOP Localization Algorithm Based on Grey Wolf Optimization","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-16 15:09:47","doi":"10.21203/rs.3.rs-5797039/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8a051552-3af9-4206-80ee-3515160e48d9","owner":[],"postedDate":"January 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-15T20:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-16 15:09:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5797039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5797039","identity":"rs-5797039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}