An Intelligent Duty Cycle Forecasting and Optimized Clustering Algorithm for Improving Energy Efficiency in Multi-hop WSNs

An Intelligent Duty Cycle Forecasting and Optimized Clustering Algorithm for Improving Energy Efficiency in Multi-hop WSNs | 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 Intelligent Duty Cycle Forecasting and Optimized Clustering Algorithm for Improving Energy Efficiency in Multi-hop WSNs N. Aravinthan, K. Geetha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5355720/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 2 You are reading this latest preprint version Abstract Wireless Sensor Networks (WSNs) rely on clustering for energy-efficient routing. This involves dividing networks into clusters and optimizing routing paths based on energy and distance. Various clustering routing algorithms have been developed, with the Sine Cosine method and Lévy mutation (SCA-Lévy) showing superior energy efficiency and network lifespan. However, this method can lead to Quality-of-Service (QoS) issues, such as increased delay in intra- and inter-cluster transmission as network capacity grows, and transmission range limitations in multi-hop WSNs. This results in an ineffective tradeoff between energy usage and delay. Therefore, this paper introduces the Intelligent Duty Cycle adapted SCA-Lévy Clustering (IDCSC) based routing algorithm for multi-hop WSN. At first, the SCA-Lévy algorithm is applied during the setup phase to create the WSN clusters and choose the optimal Cluster Head (CH) in each cluster based on the node’s residual energy and distance. Then, during the data transmission phase, a joint inter- and intra-cluster energy reduction strategy is proposed to select the multi-hop path for transmitting data from nodes to the Base Station (BS). For intra-cluster communication, this strategy involves implementing a Forecast-based Duty-Cycle Adaptation (FDCA) using the Recurrent Neural Network (RNN) model to minimize energy consumption based on the distance between CH and child nodes. For inter-cluster communication, the path with the lowest energy consumption is selected, resulting in low energy dissipation and delay in multi-hop WSNs. Finally, extensive simulations demonstrate that the IDCSC algorithm attains a greater QoS efficiency in contrast with the conventional clustering routing algorithms. Multi-hop WSN Clustering Routing SCA-Lévy Inter-cluster Intra-cluster Duty cycle Recurrent neural network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Submission checks completed at journal 03 Nov, 2024 First submitted to journal 29 Oct, 2024 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-5355720","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":373570204,"identity":"84b4ed16-f2fe-4eb2-befa-7d282e63261d","order_by":0,"name":"N. 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This involves dividing networks into clusters and optimizing routing paths based on energy and distance. Various clustering routing algorithms have been developed, with the Sine Cosine method and L\u0026eacute;vy mutation (SCA-L\u0026eacute;vy) showing superior energy efficiency and network lifespan. However, this method can lead to Quality-of-Service (QoS) issues, such as increased delay in intra- and inter-cluster transmission as network capacity grows, and transmission range limitations in multi-hop WSNs. This results in an ineffective tradeoff between energy usage and delay. Therefore, this paper introduces the Intelligent Duty Cycle adapted SCA-L\u0026eacute;vy Clustering (IDCSC) based routing algorithm for multi-hop WSN. At first, the SCA-L\u0026eacute;vy algorithm is applied during the setup phase to create the WSN clusters and choose the optimal Cluster Head (CH) in each cluster based on the node\u0026rsquo;s residual energy and distance. Then, during the data transmission phase, a joint inter- and intra-cluster energy reduction strategy is proposed to select the multi-hop path for transmitting data from nodes to the Base Station (BS). For intra-cluster communication, this strategy involves implementing a Forecast-based Duty-Cycle Adaptation (FDCA) using the Recurrent Neural Network (RNN) model to minimize energy consumption based on the distance between CH and child nodes. For inter-cluster communication, the path with the lowest energy consumption is selected, resulting in low energy dissipation and delay in multi-hop WSNs. Finally, extensive simulations demonstrate that the IDCSC algorithm attains a greater QoS efficiency in contrast with the conventional clustering routing algorithms.\u003c/p\u003e","manuscriptTitle":"An Intelligent Duty Cycle Forecasting and Optimized Clustering Algorithm for Improving Energy Efficiency in Multi-hop WSNs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-14 11:48:48","doi":"10.21203/rs.3.rs-5355720/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"checksComplete","content":"","date":"2024-11-04T02:22:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wireless Networks","date":"2024-10-29T15:39:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"wireless-networks","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wine","sideBox":"Learn more about [Wireless Networks](http://link.springer.com/journal/11276)","snPcode":"11276","submissionUrl":"https://submission.nature.com/new-submission/11276/3","title":"Wireless Networks","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"10f6e326-8166-42f0-9986-50c172f649ea","owner":[],"postedDate":"November 14th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-14T11:48:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-14 11:48:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5355720","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5355720","identity":"rs-5355720","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}