Energy efficient clustering routing algorithm based on improved FCM

Energy efficient clustering routing algorithm based on improved FCM | 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 Energy efficient clustering routing algorithm based on improved FCM Qian Sun, Xiangyue Meng, Zhiyao Zhao, Jiping Xu, Huiyan Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4452725/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 networks (WSNs) play a crucial role in the Internet of Things (IoT). The sensor nodes(SNs) in WSNs are powered by batteries, making energy efficiency and network lifetime key issues in WSNs research. Cluster-routing algorithms are a focal point for addressing energy efficiency challenges. Selecting cluster heads (CHs) based on clustering algorithms can reduce the energy consumption of SNs and enhance overall network stability and sustainability. This paper introduces a method for selecting the number of clusters ( \({N_C}\) ) and CHs based on fuzzy clustering. The fuzzy C-means (FCM) clustering algorithm requires pre-setting the number of clusters, with no inclusion of CHs information in the output after running the algorithm. The number of clusters and selection of CHs were determined using the elbow rule and scoring criteria for CHs selection. The performance of the network under different monitoring areas is simulated and analyzed in this paper. Experiments demonstrate that the proposed algorithm outperforms existing algorithms in terms of network energy consumption and lifetime. Wireless sensor networks Cluster head election FCM Clustering routing elbow method 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-4452725","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305186739,"identity":"8026f1e5-8c1c-45f5-8d5b-e7e5b3093950","order_by":0,"name":"Qian Sun","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Sun","suffix":""},{"id":305186740,"identity":"bac45c4f-1676-48d1-8b2f-9aae6c7e7e3e","order_by":1,"name":"Xiangyue Meng","email":"","orcid":"","institution":"Beijing Technology and Business 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Xu","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiping","middleName":"","lastName":"Xu","suffix":""},{"id":305186743,"identity":"b5ec6d00-7ca9-483a-8749-c869ea003b72","order_by":4,"name":"Huiyan Zhang","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Huiyan","middleName":"","lastName":"Zhang","suffix":""},{"id":305186744,"identity":"12348113-1514-4769-a98c-bea8e0cdc9aa","order_by":5,"name":"Li Wang","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Wang","suffix":""},{"id":305186745,"identity":"885dafe3-375f-4910-8a70-75258e7be9c4","order_by":6,"name":"Jiabin Yu","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jiabin","middleName":"","lastName":"Yu","suffix":""},{"id":305186746,"identity":"eae288f3-0ed4-4b86-af29-2f1d68561bfa","order_by":7,"name":"Guoxin Liu","email":"","orcid":"","institution":"Beijing Technology and Business University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guoxin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-05-21 06:54:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4452725/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4452725/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66304697,"identity":"6b532ee7-7dfb-46e2-b561-cf4b259e075e","added_by":"auto","created_at":"2024-10-10 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method","lastPublishedDoi":"10.21203/rs.3.rs-4452725/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4452725/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWireless sensor networks (WSNs) play a crucial role in the Internet of Things (IoT). The sensor nodes(SNs) in WSNs are powered by batteries, making energy efficiency and network lifetime key issues in WSNs research. Cluster-routing algorithms are a focal point for addressing energy efficiency challenges. Selecting cluster heads (CHs) based on clustering algorithms can reduce the energy consumption of SNs and enhance overall network stability and sustainability. This paper introduces a method for selecting the number of clusters (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({N_C}\\)\u003c/span\u003e\u003c/span\u003e) and CHs based on fuzzy clustering. The fuzzy C-means (FCM) clustering algorithm requires pre-setting the number of clusters, with no inclusion of CHs information in the output after running the algorithm. The number of clusters and selection of CHs were determined using the elbow rule and scoring criteria for CHs selection. The performance of the network under different monitoring areas is simulated and analyzed in this paper. 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