An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks using Variable Velocity Strategy Human Conception Optimization

An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks using Variable Velocity Strategy Human Conception 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 An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks using Variable Velocity Strategy Human Conception Optimization Subrat Kumar Panda, Debasis Acharya, Dushmanta Kumar Das, R Kumar Rajagopal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3870137/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 Wireless sensor network (WSN), is utilized in a wide range of real-world uses at the present time. WSN has the capability to detect objects, collect information, analyze that information, and then communicate it once again. The importance of optimization approaches is vital for the precise and trustworthy estimation of the position of sensor nodes. The standard Distance Vector Hop (DV-Hop) localization approach is not completely accurate enough for positioning. Nevertheless, it is distinguished by its straightforwardness, durability, practicality, and low hardware prerequisites. This article presents an Improved Distance Vector Hop (IDV-Hop) localization algorithm that utilizes Variable Velocity Strategy Human Conception Optimization (VVS-HCO) to improve positioning accuracy of a sensor node without incurring additional hardware costs. To improve the convergence and precision of the original HCO, the VVS-HCO introduces a new technique for updating velocity. The suggested method provides a parameter that may be adjusted to change the hop size of the anchor nodes. In addition, it analyzes using the traditional distance vector hop algorithm, the improved distance vector hop algorithm, the distance vector hop based Particle Swarm Optimization (PSO), the distance vector hop based Class Topper Optimization (CTO), the distance vector hop based Squirrel Search Algorithm (SSA), the distance vector hop based Social Learning Class Topper Optimization (SLCTO) and the distance vector hop based original Human Conception Optimization (HCO) algorithm. The simulation results confirm that the proposed algorithm outperforms the competing algorithms by reducing the localization error, localization error variance, and improving the localization accuracy with varying the number of anchor nodes, total number of nodes, and the communication range. Unknown nodes Anchor nodes Accuracy· Coverage DV-Hop Error Localization algorithm VVS-HCO Wireless Sensor Network LabView Software 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-3870137","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":267790021,"identity":"2be4aa10-4670-4d83-b9d4-85d511b9ddd0","order_by":0,"name":"Subrat Kumar Panda","email":"","orcid":"","institution":"National Institute of Technology Nagaland","correspondingAuthor":false,"prefix":"","firstName":"Subrat","middleName":"Kumar","lastName":"Panda","suffix":""},{"id":267790022,"identity":"987b49bf-b508-4c2a-9234-fda2e4c0c950","order_by":1,"name":"Debasis Acharya","email":"","orcid":"","institution":"National Institute of Technology Nagaland","correspondingAuthor":false,"prefix":"","firstName":"Debasis","middleName":"","lastName":"Acharya","suffix":""},{"id":267790023,"identity":"21aca62c-bad7-40fd-84d9-3280548a747d","order_by":2,"name":"Dushmanta Kumar Das","email":"data:image/png;base64,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","orcid":"","institution":"National Institute of Technology Nagaland","correspondingAuthor":true,"prefix":"","firstName":"Dushmanta","middleName":"Kumar","lastName":"Das","suffix":""},{"id":267790024,"identity":"83a3f852-9245-4dbd-b3ef-810db8f0b4fc","order_by":3,"name":"R Kumar Rajagopal","email":"","orcid":"","institution":"National Institute of Technology Nagaland","correspondingAuthor":false,"prefix":"","firstName":"R","middleName":"Kumar","lastName":"Rajagopal","suffix":""}],"badges":[],"createdAt":"2024-01-16 14:45:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3870137/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3870137/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53214576,"identity":"20d3b4f7-b48b-4871-a125-7833901201ff","added_by":"auto","created_at":"2024-03-22 02:52:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":955383,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3870137/v1_covered_5dd167b1-4606-4de2-869d-10b20c0a70ff.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Enhanced DV-Hop Localization Algorithm for Wireless Sensor Networks using Variable Velocity Strategy Human Conception 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":"Unknown nodes, Anchor nodes, Accuracy· Coverage, DV-Hop, Error, Localization algorithm, VVS-HCO, Wireless Sensor Network, LabView Software","lastPublishedDoi":"10.21203/rs.3.rs-3870137/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3870137/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Wireless sensor network (WSN), is utilized in a wide range of real-world uses at the present time. WSN has the capability to detect objects, collect information, analyze that information, and then communicate it once again. The importance of optimization approaches is vital for the precise and trustworthy estimation of the position of sensor nodes. The standard Distance Vector Hop (DV-Hop) localization approach is not completely accurate enough for positioning. Nevertheless, it is distinguished by its straightforwardness, durability, practicality, and low hardware prerequisites. This article presents an Improved Distance Vector Hop (IDV-Hop) localization algorithm that utilizes Variable Velocity Strategy Human Conception Optimization (VVS-HCO) to improve positioning accuracy of a sensor node without incurring additional hardware costs. To improve the convergence and precision of the original HCO, the VVS-HCO introduces a new technique for updating velocity. The suggested method provides a parameter that may be adjusted to change the hop size of the anchor nodes. In addition, it analyzes using the traditional distance vector hop algorithm, the improved distance vector hop algorithm, the distance vector hop based Particle Swarm Optimization (PSO), the distance vector hop based Class Topper Optimization (CTO), the distance vector hop based Squirrel Search Algorithm (SSA), the distance vector hop based Social Learning Class Topper Optimization (SLCTO) and the distance vector hop based original Human Conception Optimization (HCO) algorithm. 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