Adaptive Mobile Sink Scheduling with Proximal Policy Optimization in Wireless Sensor Networks

Adaptive Mobile Sink Scheduling with Proximal Policy Optimization in Wireless Sensor Networks | 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 Adaptive Mobile Sink Scheduling with Proximal Policy Optimization in Wireless Sensor Networks Bindu Kumari, Santosh Soni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7790119/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Wireless Personal Communications → Version 1 posted 10 You are reading this latest preprint version Abstract WSNs has emerged as one of the foundational technologies in real-time monitoring, communication, and decision-making fields in services like environmental monitoring, industry automation, and smart cities. Irrespective of their potential, WSNs have encountered consistent problems, such as, constraints to energy, changing topology, and inequal distribution of steep computational power. In mitigating such limitations, the present paper is made through a sensitivity analysis of the WSNs through Adaptive Mobile Networks (AMNs) that reconfigures sensor positioning and route configuration dynamically with respect to different traffic patterns and environmental changes to overcome the limitations previously mentioned. The presented framework uses a Transformer-enhanced Proximal Policy Optimization (PPO) agent that regards network routing policies as adaptive and is applied to enhance load balancing, reduce makespan, and increase network stability. The parameters that require sensitivity are the node density, traffic variability as well as the fault-tolerance parameter, all are systematically investigated to determine the impact they have on the variation of throughput and reliability. Both synthetical and real-world experiments are run and their performance compared against classical strategies in scheduling such as Random, Round Robin, Weighted Round Robin, Min-Min, and Max-Min, the experiments demonstrate that AMNs can provide a more balanced load distribution and remain stable even in the cases of variable network conditions. Moreover, the confidence intervals about bootstrap are also employed to provide the statistical robustness. This work highlights the significance of producing adaptive mobility on the development of resilient, scalable, and efficient in terms of energy usage WSNs, which provides useful information to the design of the next-generation sensor systems. \textbf{Keywords:} VSN (Velocity Sensor Network), AMPN (Adaptive Mobile Networks), Sensitivity Analysis, Resilience Application, Dynamic Load balancing, Proximal, Policy Optimization, Transformer, Network Reliability VSN (Velocity Sensor Network) AMPN (Adaptive Mobile Networks) Sensitivity Analysis Resilience Application Dynamic Load balancing Proximal Policy Optimization Transformer Network Reliability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Apr, 2026 Read the published version in Wireless Personal Communications → Version 1 posted Editorial decision: Revision requested 19 Dec, 2025 Reviews received at journal 04 Dec, 2025 Reviews received at journal 26 Oct, 2025 Reviewers agreed at journal 26 Oct, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 21 Oct, 2025 Submission checks completed at journal 14 Oct, 2025 First submitted to journal 06 Oct, 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. 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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-7790119","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535294699,"identity":"9b7db394-5771-458b-8802-4e28362148a5","order_by":0,"name":"Bindu Kumari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYBACA2YGBgkwAoIDDBVAkpm5gQgtCVAtB86AtDAS0MIA1gLlHWwDkQS0mLPzHrzN+8PCrl/6AOPhj/Nqo/nbgVp+VGzDqcWymS/ZmidBInlmXwLDgYPbjufOOMzYwNhz5jZuhx3mMZMGaTEAegOo5VhuA1ALM2MbEVrswVrmHMudT6wWOwMekJaGmtwNhLRYNvMYW85Jk0iQOMPYcODMsQO5G4FaDuLzizn/GcMbb2zq7Pl7mA9/qKipy513/vDBBz8qcGuBgcQGSHQcBvMOEFQPBPZQuo4YxaNgFIyCUTDCAAC9kFlEC+/NiwAAAABJRU5ErkJggg==","orcid":"","institution":"Guru Ghasidas Vishwavidyalaya (Central University)","correspondingAuthor":true,"prefix":"","firstName":"Bindu","middleName":"","lastName":"Kumari","suffix":""},{"id":535294700,"identity":"24ab5ceb-bbb3-43c6-9ffc-f4f5ce956e69","order_by":1,"name":"Santosh Soni","email":"","orcid":"","institution":"Guru Ghasidas Vishwavidyalaya (Central University)","correspondingAuthor":false,"prefix":"","firstName":"Santosh","middleName":"","lastName":"Soni","suffix":""}],"badges":[],"createdAt":"2025-10-06 09:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7790119/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7790119/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11277-026-12019-y","type":"published","date":"2026-04-10T15:58:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94904502,"identity":"46c6898a-975f-4938-b600-265adc29f679","added_by":"auto","created_at":"2025-11-01 07:11:41","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5262,"visible":true,"origin":"","legend":"","description":"","filename":"4136c89cd3f94b4cb8474e674a8859c9.json","url":"https://assets-eu.researchsquare.com/files/rs-7790119/v1/e2d197a26b1f6abd2420d054.json"},{"id":106808904,"identity":"b833c118-5cd8-46c3-b5f3-ac2c5bf7cd41","added_by":"auto","created_at":"2026-04-13 16:04:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":834249,"visible":true,"origin":"","legend":"","description":"","filename":"SensitivityAnalysis109.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7790119/v1_covered_1fff1ec1-6dc1-485a-ab3e-58fdce4c6a97.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Adaptive Mobile Sink Scheduling with Proximal Policy Optimization in Wireless Sensor Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"wireless-personal-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wire","sideBox":"Learn more about [Wireless Personal Communications](https://www.springer.com/journal/11277)","snPcode":"11277","submissionUrl":"https://submission.nature.com/new-submission/11277/3","title":"Wireless Personal Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"VSN (Velocity Sensor Network), AMPN (Adaptive Mobile Networks), Sensitivity Analysis, Resilience Application, Dynamic Load balancing, Proximal, Policy Optimization, Transformer, Network Reliability","lastPublishedDoi":"10.21203/rs.3.rs-7790119/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7790119/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"WSNs has emerged as one of the foundational technologies in real-time monitoring, communication, and decision-making fields in services like environmental monitoring, industry automation, and smart cities. Irrespective of their potential, WSNs have encountered consistent problems, such as, constraints to energy, changing topology, and inequal distribution of steep computational power. In mitigating such limitations, the present paper is made through a sensitivity analysis of the WSNs through Adaptive Mobile Networks (AMNs) that reconfigures sensor positioning and route configuration dynamically with respect to different traffic patterns and environmental changes to overcome the limitations previously mentioned. The presented framework uses a Transformer-enhanced Proximal Policy Optimization (PPO) agent that regards network routing policies as adaptive and is applied to enhance load balancing, reduce makespan, and increase network stability. The parameters that require sensitivity are the node density, traffic variability as well as the fault-tolerance parameter, all are systematically investigated to determine the impact they have on the variation of throughput and reliability. 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