A Self-Evolving Wireless Sensor Network Architecture Integrating Temporal Graph Neural Networks and Multi-Agent Deep Reinforcement Learning

A Self-Evolving Wireless Sensor Network Architecture Integrating Temporal Graph Neural Networks and Multi-Agent Deep Reinforcement Learning | 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 Article A Self-Evolving Wireless Sensor Network Architecture Integrating Temporal Graph Neural Networks and Multi-Agent Deep Reinforcement Learning Mojtaba Jahanian, Hossein Yarahmadi, Maryam Hajiei, Alireza Enami This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8398972/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 Wireless sensor networks (WSNs) operating in dynamic, interference-prone, and energy-limited environments must cope with continuously evolving topologies, uneven energy depletion, and unpredictable node and link failures. Despite significant progress, most existing approaches remain fundamentally reactive : they adapt only after disruptions occur or treat prediction and control as loosely coupled components, preventing proactive resilience and long-term stability. We present SE-WSN , a self-evolving network architecture that unifies temporal graph intelligence with coordinated multi-agent control. SE-WSN models the WSN as a time-evolving graph and employs a Temporal Graph Neural Network (Temporal-GNN) to forecast short-horizon structural risks—ranging from node-failure likelihood to link-quality degradation and cluster-role instability. These predictions are distilled into a compact structural risk context that conditions a MADDPG-based controller, enabling distributed nodes to make risk-aware decisions for routing, transmission-power regulation, duty-cycling, and cluster-role adaptation. A lightweight self-healing engine translates these decisions into continuous, online topology reconfiguration, forming a closed-loop, prediction-driven adaptation cycle unprecedented in prior WSN designs. Comprehensive simulations across heterogeneous densities, traffic patterns, and failure scenarios demonstrate that SE-WSN delivers consistent and statistically robust gains in network lifetime, reliability, and energy balance over both classical protocols and state-of-the-art learning-based baselines—while maintaining low computational and communication overhead. Ablation studies further reveal the complementary roles of temporal graph forecasting and risk-aware reward shaping in stabilizing learning dynamics and preventing cascading failures. These results indicate that tightly coupling temporal graph representation learning with coordinated multi-agent control establishes a powerful foundation for next-generation autonomic and self-healing WSNs. Physical sciences/Engineering Physical sciences/Mathematics and computing Self-Evolving WSNs Temporal Graph Neural Networks Multi-Agent Deep Reinforcement Learning Risk Aware Routing Topology Control Autonomic and Self-Healing Networks 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-8398972","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":566442754,"identity":"d24a588e-c010-4498-ae62-7397f03a0c75","order_by":0,"name":"Mojtaba 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Learning","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":"Self-Evolving WSNs, Temporal Graph Neural Networks, Multi-Agent Deep Reinforcement Learning, Risk Aware Routing, Topology Control, Autonomic and Self-Healing Networks","lastPublishedDoi":"10.21203/rs.3.rs-8398972/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8398972/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWireless sensor networks (WSNs) operating in dynamic, interference-prone, and energy-limited environments must cope with continuously evolving topologies, uneven energy depletion, and unpredictable node and link failures. Despite significant progress, most existing approaches remain fundamentally \u003cem\u003ereactive\u003c/em\u003e: they adapt only after disruptions occur or treat prediction and control as loosely coupled components, preventing proactive resilience and long-term stability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe present \u003cstrong\u003eSE-WSN\u003c/strong\u003e, a self-evolving network architecture that unifies temporal graph intelligence with coordinated multi-agent control. SE-WSN models the WSN as a time-evolving graph and employs a Temporal Graph Neural Network (Temporal-GNN) to forecast short-horizon structural risks—ranging from node-failure likelihood to link-quality degradation and cluster-role instability. These predictions are distilled into a compact \u003cem\u003estructural risk context\u003c/em\u003e that conditions a MADDPG-based controller, enabling distributed nodes to make risk-aware decisions for routing, transmission-power regulation, duty-cycling, and cluster-role adaptation. A lightweight self-healing engine translates these decisions into continuous, online topology reconfiguration, forming a closed-loop, prediction-driven adaptation cycle unprecedented in prior WSN designs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComprehensive simulations across heterogeneous densities, traffic patterns, and failure scenarios demonstrate that SE-WSN delivers consistent and statistically robust gains in network lifetime, reliability, and energy balance over both classical protocols and state-of-the-art learning-based baselines—while maintaining low computational and communication overhead. Ablation studies further reveal the complementary roles of temporal graph forecasting and risk-aware reward shaping in stabilizing learning dynamics and preventing cascading failures. These results indicate that tightly coupling temporal graph representation learning with coordinated multi-agent control establishes a powerful foundation for next-generation autonomic and self-healing WSNs.\u003c/p\u003e","manuscriptTitle":"A Self-Evolving Wireless Sensor Network Architecture Integrating Temporal Graph Neural Networks and Multi-Agent Deep Reinforcement Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-30 06:23:00","doi":"10.21203/rs.3.rs-8398972/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"d1c575ad-136e-42f5-94ff-9341c3b0c968","owner":[],"postedDate":"December 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":60294848,"name":"Physical sciences/Engineering"},{"id":60294849,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-07T16:24:44+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-30 06:23:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8398972","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8398972","identity":"rs-8398972","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}