Conversational LLM Framework for Secure and Energy-Efficient Wireless Sensor Networks

Conversational LLM Framework for Secure and Energy-Efficient 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 Conversational LLM Framework for Secure and Energy-Efficient Wireless Sensor Networks Sauvik Bal, Lopa Mandal This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7714424/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 A conversational orchestration framework named A-LLM is presented in which large language models (LLMs) are employed to jointly optimize energy efficiency and security in wireless sensor networks (WSNs). Sink–sensor interactions are modeled as structured dialogues, through which compressed network state summaries are translated into actionable policies for cluster-head selection, duty-cycle scheduling, and anomaly mitigation. A risk-aware energy optimization objective that incorporates the cost of LLM queries is formalized, and a secure conversational reconfiguration protocol (SCR-LLM) is proposed to isolate compromised nodes and refresh group keys under adversarial conditions. To improve practicality, a prompt-distillation approach is introduced so that LLM behavior can be transferred to a lightweight edge policy, thereby reducing both latency and query overhead. The framework has been designed for evaluation through extensive simulations and an emulated testbed under heterogeneous traffic patterns and attack scenarios. Improvements in network lifetime, energy consumption, and detection accuracy have been observed when compared to classical baselines. All datasets used were either publicly available or derived from public sources, ensuring reproducibility and methodological transparency. Wireless Sensor Networks Large Language Models Energy Efficiency Anomaly Detection Prompt Optimization Secure Key Management Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 09 Dec, 2025 Reviews received at journal 12 Nov, 2025 Reviews received at journal 29 Oct, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers agreed at journal 15 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 11 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 09 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. 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-7714424","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535473966,"identity":"a845ceba-1bcc-41e1-a164-39ca812dddf9","order_by":0,"name":"Sauvik 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