Adaptive Reinforcement Learning with Temporal Prediction for Routing in Congestion-Aware Dynamic IoT Network

Adaptive Reinforcement Learning with Temporal Prediction for Routing in Congestion-Aware Dynamic IoT Network | 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 Reinforcement Learning with Temporal Prediction for Routing in Congestion-Aware Dynamic IoT Network Anuja Priyam, Anita Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7728035/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Despite significant progress in Wireless networks, especially Mobile Ad Hoc Networks (MANETs) and Internet of Things (IoTs) networks, current approaches still face challenges in providing reliable, low-latency and energy-efficient communication in highly dynamic environments. Existing routing protocols, such as Ad hoc On-Demand Distance Vector (AODV), suffer from frequent route interruptions, high end-to-end delay, and high energy consumption, making them unsuitable for large-scale and real-time IoT applications. Reinforcement learning (RL) and Q-learning based protocols remain largely reactive, responding only to present or historical conditions without predictive foresight, which leads to higher packet loss and delay in highly dynamic environments. Similarly, LSTM and other ML- based models excel at temporal prediction but are rarely integrated into real-time routing frameworks. Moreover, most existing protocols focus on optimizing a single performance metric, such as delay or reliability, while neglecting the need for multi-metric optimization that simultaneously balances latency, Packet Delivery Ratio, and energy efficiency. To address these shortcomings, this research introduces a new LSTM-based Q- Learning routing protocol (LSTM- Q) that combines reinforcement learning with long short-term memory (LSTM) networks to enable adaptive and predictive route selection. The LSTM subsystem enables the protocol to capture temporal dependencies in behavior so that link stability and congestion patterns can be predicted more accurately, while Q-learning facilitates efficient decision-making through reward- based adaptation. Extensive simulations across varying node densities demonstrate that the designed protocol outperforms AODV and baseline Q-learning methods. In particular, LSTM-Q achieves a Packet Delivery Ratio (PDR) of 98.32%, reduces end-to-end delay to 14.42 ms, increases throughput to 19.15 Kbps and decreases average energy consumption to 0.57 J, signifying improvement of more than 50% in reliability and 80% in energy efficiency over AODV. These results substantiate that LSTM-Q offers an efficient, scalable, and energy-aware routing methodology, rendering it highly efficient for dynamic wireless networks like IoT, vehicular networks, and mission-critical communication networks. Mobile Ad-hoc Networks LSTM Q-Learning Routing Protocols Deep Reinforcement Learning Network Performance Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 25 Oct, 2025 Reviews received at journal 24 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers invited by journal 23 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 17 Oct, 2025 First submitted to journal 27 Sep, 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. 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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-7728035","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":538623431,"identity":"f620b0b3-ee6c-45e1-82b0-b80e4a7c3625","order_by":0,"name":"Anuja 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Network","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":"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":"Mobile Ad-hoc Networks, LSTM, Q-Learning, Routing Protocols, Deep Reinforcement Learning, Network Performance Optimization","lastPublishedDoi":"10.21203/rs.3.rs-7728035/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7728035/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite significant progress in Wireless networks, especially Mobile Ad Hoc Networks (MANETs) and Internet of Things (IoTs) networks, current approaches still face challenges in providing reliable, low-latency and energy-efficient communication in highly dynamic environments. Existing routing protocols, such as Ad hoc On-Demand Distance Vector (AODV), suffer from frequent route interruptions, high end-to-end delay, and high energy consumption, making them unsuitable for large-scale and real-time IoT applications. Reinforcement learning (RL) and Q-learning based protocols remain largely reactive, responding only to present or historical conditions without predictive foresight, which leads to higher packet loss and delay in highly dynamic environments. Similarly, LSTM and other ML- based models excel at temporal prediction but are rarely integrated into real-time routing frameworks. Moreover, most existing protocols focus on optimizing a single performance metric, such as delay or reliability, while neglecting the need for multi-metric optimization that simultaneously balances latency, Packet Delivery Ratio, and energy efficiency. To address these shortcomings, this research introduces a new LSTM-based Q- Learning routing protocol (LSTM- Q) that combines reinforcement learning with long short-term memory (LSTM) networks to enable adaptive and predictive route selection. The LSTM subsystem enables the protocol to capture temporal dependencies in behavior so that link stability and congestion patterns can be predicted more accurately, while Q-learning facilitates efficient decision-making through reward- based adaptation. Extensive simulations across varying node densities demonstrate that the designed protocol outperforms AODV and baseline Q-learning methods. In particular, LSTM-Q achieves a Packet Delivery Ratio (PDR) of 98.32%, reduces end-to-end delay to 14.42 ms, increases throughput to 19.15 Kbps and decreases average energy consumption to 0.57 J, signifying improvement of more than 50% in reliability and 80% in energy efficiency over AODV. These results substantiate that LSTM-Q offers an efficient, scalable, and energy-aware routing methodology, rendering it highly efficient for dynamic wireless networks like IoT, vehicular networks, and mission-critical communication networks.\u003c/p\u003e","manuscriptTitle":"Adaptive Reinforcement Learning with Temporal Prediction for Routing in Congestion-Aware Dynamic IoT Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 15:27:18","doi":"10.21203/rs.3.rs-7728035/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"214525713861483269865327402913186803257","date":"2025-10-25T10:43:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-24T08:05:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"136838469565302196538722088594604509364","date":"2025-10-23T20:36:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-23T07:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-23T07:28:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-17T05:14:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Wireless Personal Communications","date":"2025-09-27T11:08:33+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"fb0de4b9-8e6b-41e6-9e8f-f21c46a6bd43","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-04T15:27:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 15:27:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7728035","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7728035","identity":"rs-7728035","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}