A cooperative optimization method for coverage and scheduling of traffic WSN relay nodes considering carbon emission cost constraints

A cooperative optimization method for coverage and scheduling of traffic WSN relay nodes considering carbon emission cost constraints | 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 A cooperative optimization method for coverage and scheduling of traffic WSN relay nodes considering carbon emission cost constraints Tian Xiaxiao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8385726/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 Against the backdrop of the green and low-carbon transformation of traffic sensing systems, the high energy consumption and carbon emissions of relay nodes in wireless sensor networks are becoming increasingly prominent. To control the carbon emission costs of relay node operation while ensuring coverage quality, this paper proposes a collaborative optimization method for coverage and scheduling of wireless sensor network relay nodes considering carbon emission cost constraints. First, a coverage model and mathematical framework coupling "energy consumption - carbon emission - coverage efficiency" are established, integrating multi-objective optimization objectives and multiple constraints. Second, a multi-stage coverage optimization algorithm jointly evaluating carbon penalties and coverage gains is designed. Finally, a collaborative strategy combining carbon budget allocation and dynamic scheduling is proposed. Experiments show that the method converges in 40 out of 100 iterations, achieving a final coverage rate of 92%, which is 9% higher than the multi-objective differential evolution algorithm (83%) and the multi-objective particle swarm optimization algorithm (75%). The lifecycle carbon emissions under medium traffic density are 147. Compared to the comparative method, this method reduces CO 2 emissions by more than 36g, while maintaining a data transmission success rate of 94% to 96%. This method provides a new solution for the efficient and low-carbon operation of green transportation sensing systems. Traffic wireless sensor network Relay node Carbon emission constraints Coverage optimization Scheduling coordination 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-8385726","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":569459659,"identity":"bba24bcb-46bb-4d53-aafc-02213788c167","order_by":0,"name":"Tian 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constraints","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":"Traffic wireless sensor network, Relay node, Carbon emission constraints, Coverage optimization, 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To control the carbon emission costs of relay node operation while ensuring coverage quality, this paper proposes a collaborative optimization method for coverage and scheduling of wireless sensor network relay nodes considering carbon emission cost constraints. First, a coverage model and mathematical framework coupling \"energy consumption - carbon emission - coverage efficiency\" are established, integrating multi-objective optimization objectives and multiple constraints. Second, a multi-stage coverage optimization algorithm jointly evaluating carbon penalties and coverage gains is designed. Finally, a collaborative strategy combining carbon budget allocation and dynamic scheduling is proposed. Experiments show that the method converges in 40 out of 100 iterations, achieving a final coverage rate of 92%, which is 9% higher than the multi-objective differential evolution algorithm (83%) and the multi-objective particle swarm optimization algorithm (75%). The lifecycle carbon emissions under medium traffic density are 147. Compared to the comparative method, this method reduces CO\u003csub\u003e2\u003c/sub\u003e emissions by more than 36g, while maintaining a data transmission success rate of 94% to 96%. 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