Contactless Indoor Temperature Sensing via Wi-Fi Channel State Information: A Machine Learning Approach with Real-Time ESP32 Deployment

Contactless Indoor Temperature Sensing via Wi-Fi Channel State Information: A Machine Learning Approach with Real-Time ESP32 Deployment | 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 Contactless Indoor Temperature Sensing via Wi-Fi Channel State Information: A Machine Learning Approach with Real-Time ESP32 Deployment Saurav Chaudhari, Ketan Pise, Dinesh Fukate, Shantanu Gawande This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8690571/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 Indoor temperature monitoring is essential for smart buildings, HVAC optimization, and occupant comfort, yet conventional thermistor-based sensors require dense deployment and maintenance. This paper introduces a novel contactless temperature sensing methodology leveraging Wi-Fi Channel State Information (CSI) modulated by temperature-dependent air refractive index variations. We formulate the CSI-temperature relationship via electromagnetic wave propagation theory and develop a two-stage machine learning pipeline: (1) CSI feature extraction using statistical moments and discrete wavelet transform coefficients across 30 OFDM subcarriers, and (2) regression via Gradient Boosting (XGBoost) trained on 15,000 CSI snapshots spanning 15--35 \((^{\circ})\) C in controlled climate chamber experiments. Theoretical analysis establishes sensitivity bounds of \((0.8\times10^{-6})\) refractive index change per \((^{\circ})\) C at 2.4 GHz, translating to observable CSI amplitude variations of 0.3--0.7 dB. Experimental validation across three indoor environments (laboratory, office, residential) achieves mean absolute error (MAE) of 0.68 \((^{\circ})\) C and root mean square error (RMSE) of 0.91 \((^{\circ})\) C, with inference latency of 42 ms on ESP32 microcontrollers. Comparative analysis demonstrates 23% accuracy improvement over polynomial regression baselines and robustness under multipath-rich conditions. Real-time deployment over 30 days shows 94.2% uptime with drift correction via periodic calibration. The system enables cost-effective ( \((<)\) $ 15/node), maintenance-free ambient monitoring for IoT-enabled smart buildings without physical sensor installation. Wi-Fi sensing Channel State Information Temperature monitoring Machine learning IoT sensors Smart buildings Wireless sensing ESP32 Gradient boosting Environmental monitoring 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-8690571","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609938047,"identity":"0ff18ef7-1244-4189-9f01-a978c97c15e3","order_by":0,"name":"Saurav Chaudhari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYBADOTb25gNAWkKGaC3G/DzHEkBaeIjWkjhzRo4BiEFYi3z7GbMPP9vsEjfcyPn86kaNBQ8D++GjG/BpMTiTYzyzty3ZeMOZt9usc44BHcaTlnYDrxaGHGMGnjMHZDccz91mnMMG1CLBY4ZXi3z/G2PGP2cOMG44kPPMOOcfEVoYbuQYM/NUHFCc2ZHD/Di3jQgtBjeeFTPLVCSDAtmMObdPgoeNkF/k+5M3M74xsANF5ePPOd/q5PjZDx/D7zAkwCYBJolVDgLMH0hRPQpGwSgYBSMHAACkikd8c2GbPAAAAABJRU5ErkJggg==","orcid":"","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":true,"prefix":"","firstName":"Saurav","middleName":"","lastName":"Chaudhari","suffix":""},{"id":609938048,"identity":"0ff67630-ea6a-4520-9d59-f08935e435b4","order_by":1,"name":"Ketan Pise","email":"","orcid":"","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":false,"prefix":"","firstName":"Ketan","middleName":"","lastName":"Pise","suffix":""},{"id":609938049,"identity":"29488d48-fa0b-4afe-ba8b-16218bb6551f","order_by":2,"name":"Dinesh Fukate","email":"","orcid":"","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":false,"prefix":"","firstName":"Dinesh","middleName":"","lastName":"Fukate","suffix":""},{"id":609938050,"identity":"d2a812f1-80b2-4e5d-9f33-6474c7269e5d","order_by":3,"name":"Shantanu Gawande","email":"","orcid":"","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":false,"prefix":"","firstName":"Shantanu","middleName":"","lastName":"Gawande","suffix":""}],"badges":[],"createdAt":"2026-01-25 06:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8690571/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8690571/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564942,"identity":"0516b20d-43f5-40ba-9559-c847fd22e737","added_by":"auto","created_at":"2026-03-27 12:51:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":324071,"visible":true,"origin":"","legend":"","description":"","filename":"DS32.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8690571/v1_covered_2bf5a23e-e27d-4570-8ce1-9718e1c9be22.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contactless Indoor Temperature Sensing via Wi-Fi Channel State Information: A Machine Learning Approach with Real-Time ESP32 Deployment","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Wi-Fi sensing, Channel State Information, Temperature monitoring, Machine learning, IoT sensors, Smart buildings, Wireless sensing, ESP32, Gradient boosting, Environmental monitoring","lastPublishedDoi":"10.21203/rs.3.rs-8690571/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8690571/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIndoor temperature monitoring is essential for smart buildings, HVAC optimization, and occupant comfort, yet conventional thermistor-based sensors require dense deployment and maintenance. This paper introduces a novel contactless temperature sensing methodology leveraging Wi-Fi Channel State Information (CSI) modulated by temperature-dependent air refractive index variations. We formulate the CSI-temperature relationship via electromagnetic wave propagation theory and develop a two-stage machine learning pipeline: (1) CSI feature extraction using statistical moments and discrete wavelet transform coefficients across 30 OFDM subcarriers, and (2) regression via Gradient Boosting (XGBoost) trained on 15,000 CSI snapshots spanning 15--35\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((^{\\circ})\\)\u003c/span\u003e\u003c/span\u003eC in controlled climate chamber experiments. Theoretical analysis establishes sensitivity bounds of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((0.8\\times10^{-6})\\)\u003c/span\u003e\u003c/span\u003e refractive index change per \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((^{\\circ})\\)\u003c/span\u003e\u003c/span\u003eC at 2.4 GHz, translating to observable CSI amplitude variations of 0.3--0.7 dB. Experimental validation across three indoor environments (laboratory, office, residential) achieves mean absolute error (MAE) of 0.68\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((^{\\circ})\\)\u003c/span\u003e\u003c/span\u003eC and root mean square error (RMSE) of 0.91\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((^{\\circ})\\)\u003c/span\u003e\u003c/span\u003eC, with inference latency of 42 ms on ESP32 microcontrollers. Comparative analysis demonstrates 23% accuracy improvement over polynomial regression baselines and robustness under multipath-rich conditions. Real-time deployment over 30 days shows 94.2% uptime with drift correction via periodic calibration. The system enables cost-effective (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((\u0026lt;)\\)\u003c/span\u003e\u003c/span\u003e\u003cspan\u003e$\u003c/span\u003e15/node), maintenance-free ambient monitoring for IoT-enabled smart buildings without physical sensor installation.\u003c/p\u003e","manuscriptTitle":"Contactless Indoor Temperature Sensing via Wi-Fi Channel State Information: A Machine Learning Approach with Real-Time ESP32 Deployment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 13:28:51","doi":"10.21203/rs.3.rs-8690571/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":"a69f83fd-a6bd-4270-9308-43783d2357a3","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T13:28:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 13:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8690571","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8690571","identity":"rs-8690571","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}