{"paper_id":"2bcae20f-eec9-4dd6-9930-55c28e57cfd5","body_text":"HGGRKO: An Optimized Hybrid Approach for Precision Node Localization in Wireless Sensor Networks | 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 HGGRKO: An Optimized Hybrid Approach for Precision Node Localization in Wireless Sensor Networks Sucheta Panda, SUSHREE BIBHUPRADA B. PRIYADARSHINI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6053367/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 Localization, or position in Wireless Sensor Networks (WSNs), is one of the most challenging and crucial tasks in a range of tracking and monitoring applications. This problem is brought on by the need to disperse the network over large areas and provides recently acquired location data to unidentified devices. With conventional localization methods, scalability and computation time constraints are frequent problems. In this paper, a novel hybrid optimization strategy is proposed to enhance the precision and robustness of node localization within WSNs. The recently proposed HybridisedGreylag Goose Red Kite Optimization (HGGRKO) represents a hybrid optimization strategy that combines two efficient metaheuristic techniques from the Red Kite Optimization (RKO) and Greylag Goose Optimization (GGO) algorithms to accomplish the objective of the framework. The main objective of the HGGRKO-based architecture is to minimize the localization error between the detected and actual locations of each node in the WSN. The HGGRKO technique uses the exploration capabilities of the GGO algorithm and the exploitation capacities of the RKO algorithm to improve localization accuracy. The method selects anchor nodes carefully to further reduce localization errors. The HGGRKO algorithm can be used to reduce the number of nodes, boost coverage rates, and maintain network connections. To evaluate the effectiveness of the HGGRKO approach, MATLAB software is utilized. The findings demonstrate that the approach outperforms conventional optimization algorithms in terms of speed, localized node count, localization error minimization across a variety of anchor node counts, and execution time. Anchor nodes Greylag Goose Optimization (GGO) Node localization Red Kite Optimization (RKO) WSNs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Full Text 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. 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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-6053367\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":431580845,\"identity\":\"4f952f35-a0bd-4889-84b8-c4a73b891afe\",\"order_by\":0,\"name\":\"Sucheta Panda\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Siksha O Anusandhan University Institute of Technical Education and Research\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sucheta\",\"middleName\":\"\",\"lastName\":\"Panda\",\"suffix\":\"\"},{\"id\":431580846,\"identity\":\"e8f213bb-9121-4c39-833e-31f667ea30ea\",\"order_by\":1,\"name\":\"SUSHREE BIBHUPRADA B. PRIYADARSHINI\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Siksha O Anusandhan University Institute of Technical Education and Research\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"SUSHREE\",\"middleName\":\"BIBHUPRADA B.\",\"lastName\":\"PRIYADARSHINI\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-02-18 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Networks\",\"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\":\"info@researchsquare.com\",\"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\":\"Anchor nodes, Greylag Goose Optimization (GGO), Node localization, Red Kite Optimization (RKO), WSNs\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6053367/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6053367/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Localization, or position in Wireless Sensor Networks (WSNs), is one of the most challenging and crucial tasks in a range of tracking and monitoring applications. This problem is brought on by the need to disperse the network over large areas and provides recently acquired location data to unidentified devices. With conventional localization methods, scalability and computation time constraints are frequent problems. In this paper, a novel hybrid optimization strategy is proposed to enhance the precision and robustness of node localization within WSNs. The recently proposed HybridisedGreylag Goose Red Kite Optimization (HGGRKO) represents a hybrid optimization strategy that combines two efficient metaheuristic techniques from the Red Kite Optimization (RKO) and Greylag Goose Optimization (GGO) algorithms to accomplish the objective of the framework. The main objective of the HGGRKO-based architecture is to minimize the localization error between the detected and actual locations of each node in the WSN. The HGGRKO technique uses the exploration capabilities of the GGO algorithm and the exploitation capacities of the RKO algorithm to improve localization accuracy. The method selects anchor nodes carefully to further reduce localization errors. The HGGRKO algorithm can be used to reduce the number of nodes, boost coverage rates, and maintain network connections. To evaluate the effectiveness of the HGGRKO approach, MATLAB software is utilized. The findings demonstrate that the approach outperforms conventional optimization algorithms in terms of speed, localized node count, localization error minimization across a variety of anchor node counts, and execution time.\",\"manuscriptTitle\":\"HGGRKO: An Optimized Hybrid Approach for Precision Node Localization in Wireless Sensor Networks\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-03-24 08:36:43\",\"doi\":\"10.21203/rs.3.rs-6053367/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"f48cf6f1-332b-48b0-bffb-fab666ac2238\",\"owner\":[],\"postedDate\":\"March 24th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-10-09T02:49:36+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-03-24 08:36:43\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6053367\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6053367\",\"identity\":\"rs-6053367\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}