Flock Management through IoT and Multi-Metric Routing in Agricultural Environments

Flock Management through IoT and Multi-Metric Routing in Agricultural Environments | 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 Flock Management through IoT and Multi-Metric Routing in Agricultural Environments Swarup Kumar J.N.V.R., Venkateswararao Kuna, Vamsi T.M.N. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7326867/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Flock management is a crucial part of the agriculture industry, particularly in livestock safety, reducing economic losses, and enhancing resource utilization. This paper models that each sheep in the flock is given a wearable IoT device that can track its location and communicate with the others. It is also assigned to specific areas for grazing. If any of the tracking sheep goes beyond its designated area, the farmer gets notified. So that the farmer can be notified of any possible dangers, this paper presents a novel algorithm, which is a latency-aware objective function. The default metrics, like hop count or ETX, use RPL objective functions like OF0 and MRHOF, however, overlook latency, mobility, and energy constraints, making them inappropriate for dynamic and mobility-driven agricultural environments. This proposed model uses four key metrics, like latency, link quality, residual energy, and local congestion, to offer a balanced routing to the needs of wearable flock IoT systems. The Cooja simulator is used to run simulations with the proposed algorithm. There is a minimum variation of 10% in the efficiency in terms of PDR and energy consumption of the proposed algorithm compared to the already existing algorithms, which are OF-EC and AHP-OF. This study is an application of implementing IoT in the agriculture sector for tracking and securing the flocks. The proposed method is developed to perform well outdoors and instantly offer monitoring data. The best part of this work is integrating domain-specific constraints, such as mobility models and power-aware routing, into IoT systems for rural real-world livestock movement. Moreover, improves the efficiency of transmitting the packets, aims to lessen the losses in the sheep population, and enhances resource management. IoT Wearable Flock Management Low-Power and Lossy Networks (LLNs) Agriculture Real-time tracking Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 10 Aug, 2025 First submitted to journal 08 Aug, 2025 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-7326867","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516157686,"identity":"2947ac2c-59ac-484e-905a-08b90c2e7b3c","order_by":0,"name":"Swarup Kumar J.N.V.R.","email":"","orcid":"","institution":"GITAM University","correspondingAuthor":false,"prefix":"","firstName":"Swarup","middleName":"Kumar","lastName":"J.N.V.R.","suffix":""},{"id":516157687,"identity":"47e6f9c1-6264-42e4-88fa-5518c39a3a17","order_by":1,"name":"Venkateswararao Kuna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYDACHhBRAaEYGNiI1nKGgYeHNC2MbVCtRGkxOHPG7MHPebUy9uynExg+lB0mQsvZHnPD3m3HeXh4cjcwzjhHhBbJfh4zCd5tx4B+yd3AzNtGpBbJv3OAWvjfbmD+S4wWft4eM2nehhoeHgmgLYxEaeE5ViYtc+wAD8+NtxsO9pxLJ6yFjSd5m+Sbmjp79v7cjQ9+lFkT1gIFEPccIFo9ENSRongUjIJRMApGGgAAJc81LvfuKZ4AAAAASUVORK5CYII=","orcid":"","institution":"GITAM University","correspondingAuthor":true,"prefix":"","firstName":"Venkateswararao","middleName":"","lastName":"Kuna","suffix":""},{"id":516157688,"identity":"8bbc5f6c-6ca1-4626-a9ea-97ffef32dd82","order_by":2,"name":"Vamsi T.M.N.","email":"","orcid":"","institution":"GITAM University","correspondingAuthor":false,"prefix":"","firstName":"Vamsi","middleName":"","lastName":"T.M.N.","suffix":""}],"badges":[],"createdAt":"2025-08-08 11:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7326867/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7326867/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91581901,"identity":"bd481927-5f8b-4d8a-8bb4-e26d98a89f58","added_by":"auto","created_at":"2025-09-18 04:38:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":569794,"visible":true,"origin":"","legend":"","description":"","filename":"FlockManagementthroughIoTandMultiMetricRoutinginAgriculturalEnvironments.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7326867/v1_covered_1d03b125-25f1-4312-b69f-a057c1b2f749.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Flock Management through IoT and Multi-Metric Routing in Agricultural Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cluster-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Cluster Computing](https://www.springer.com/journal/10586)","snPcode":"10586","submissionUrl":"https://submission.nature.com/new-submission/10586/3","title":"Cluster Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"IoT, Wearable, Flock Management, Low-Power and Lossy Networks (LLNs), Agriculture, Real-time tracking","lastPublishedDoi":"10.21203/rs.3.rs-7326867/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7326867/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlock management is a crucial part of the agriculture industry, particularly in livestock safety, reducing economic losses, and enhancing resource utilization. This paper models that each sheep in the flock is given a wearable IoT device that can track its location and communicate with the others. It is also assigned to specific areas for grazing. If any of the tracking sheep goes beyond its designated area, the farmer gets notified. So that the farmer can be notified of any possible dangers, this paper presents a novel algorithm, which is a latency-aware objective function. The default metrics, like hop count or ETX, use RPL objective functions like OF0 and MRHOF, however, overlook latency, mobility, and energy constraints, making them inappropriate for dynamic and mobility-driven agricultural environments. This proposed model uses four key metrics, like latency, link quality, residual energy, and local congestion, to offer a balanced routing to the needs of wearable flock IoT systems. The Cooja simulator is used to run simulations with the proposed algorithm. 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