Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN

Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN | 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 Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN Kanika Chauhan, Deepak Kumar Sharma, Nonita Sharma, Bhagyashri Tushir This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4398792/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 The utilization of Free-Space Optical (FSO) communication in underwater environments is gaining momentum, particularly in Software Defined Underwater Wireless Sensor Networks(SDUWSNs). However, challenges such as high-energy loss and limited propagation distance persist in data transmission within SDUWSNs. In addressing these issues, this study introduces an innovative approach known as Software Defined Free Space Optical Underwater Wireless Sensor Networks, where FSO communication is seamlessly integrated with SDUWSNs to enhance network longevity. To optimize the performance of SD-FSO-UWSNs, the implementation of clustering and routing is explored as an effective strategy for energy-efficient data delivery. Nevertheless, the selection of optimal control nodes (CNs) in clustering poses a significant challenge. In response, a novel self-adaptive cheetah optimization-based clustering approach (SACO-CA) is proposed by incorporating self-adaptive inertia weights to identify optimal CNs based on a devised fitness value. The fitness function considers important parameters such as energy levels and distances among network devices, aiming to balance cluster sizes effectively. Moreover, the NS3 simulator is used to run network simulation while, SDN policies are implemented through the Open Network Oper-ating System (ONOS) controller. The simulation result metrics, including stability period,alive nodes, average residual energy, packets transmitted to the control server, and averagedelay, indicate that SACO-CA outperforms existing state-of-the-art methods. The results underscore the efficacy of the nature-inspired SACO-CA approach in optimizing CNs and improving overall network performance in SD-FSO-UWSNs. Clustering Wireless Sensor Networks (WSN) Software Defined Sensor Networks (SDWSN) Free Space Optical (FSO) Underwater Sensor Networks (UWSN) Open Network Operating System (ONOS) 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-4398792","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":308760875,"identity":"c39bb9d4-7ebc-4dd5-9f4e-46e32c113593","order_by":0,"name":"Kanika Chauhan","email":"data:image/png;base64,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","orcid":"","institution":"Indira Gandhi Delhi Technical University for Women","correspondingAuthor":true,"prefix":"","firstName":"Kanika","middleName":"","lastName":"Chauhan","suffix":""},{"id":308760879,"identity":"ef5d31c9-dc33-4e4c-8f81-7c1b979c7011","order_by":1,"name":"Deepak Kumar Sharma","email":"","orcid":"","institution":"Indira Gandhi Delhi Technical University for Women","correspondingAuthor":false,"prefix":"","firstName":"Deepak","middleName":"Kumar","lastName":"Sharma","suffix":""},{"id":308760880,"identity":"5c794d80-7184-4215-8ac3-c1cee9a9c5d2","order_by":2,"name":"Nonita Sharma","email":"","orcid":"","institution":"Indira Gandhi Delhi Technical University for Women","correspondingAuthor":false,"prefix":"","firstName":"Nonita","middleName":"","lastName":"Sharma","suffix":""},{"id":308760881,"identity":"c6c6103a-e3cf-4c22-a186-246bddb18416","order_by":3,"name":"Bhagyashri Tushir","email":"","orcid":"","institution":"Santa Clara University","correspondingAuthor":false,"prefix":"","firstName":"Bhagyashri","middleName":"","lastName":"Tushir","suffix":""}],"badges":[],"createdAt":"2024-05-10 07:06:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4398792/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4398792/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95523925,"identity":"c2540484-2cad-4869-b5fd-16c910c0bc30","added_by":"auto","created_at":"2025-11-10 10:01:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":813439,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4398792/v1_covered_dd08ebe8-0b3a-4bf4-8dc7-038ececcadd9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Energy Efficient Optimized Clustering for Free Space Optical Communication in Software Defined Underwater WSN","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":"Clustering, Wireless Sensor Networks (WSN), Software Defined Sensor Networks (SDWSN), Free Space Optical (FSO), Underwater Sensor Networks (UWSN), Open Network Operating System (ONOS)","lastPublishedDoi":"10.21203/rs.3.rs-4398792/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4398792/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe utilization of Free-Space Optical (FSO) communication in underwater environments is gaining momentum, particularly in Software Defined Underwater Wireless Sensor Networks(SDUWSNs). However, challenges such as high-energy loss and limited propagation distance persist in data transmission within SDUWSNs. In addressing these issues, this study introduces an innovative approach known as Software Defined Free Space Optical Underwater Wireless Sensor Networks, where FSO communication is seamlessly integrated with SDUWSNs to enhance network longevity. To optimize the performance of SD-FSO-UWSNs, the implementation of clustering and routing is explored as an effective strategy for energy-efficient data delivery. Nevertheless, the selection of optimal control nodes (CNs) in clustering poses a significant challenge. In response, a novel self-adaptive cheetah optimization-based clustering approach (SACO-CA) is proposed by incorporating self-adaptive inertia weights to identify optimal CNs based on a devised fitness value. The fitness function considers important parameters such as energy levels and distances among network devices, aiming to balance cluster sizes effectively. Moreover, the NS3 simulator is used to run network simulation while, SDN policies are implemented through the Open Network Oper-ating System (ONOS) controller. The simulation result metrics, including stability period,alive nodes, average residual energy, packets transmitted to the control server, and averagedelay, indicate that SACO-CA outperforms existing state-of-the-art methods. 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