Joint Contactless Temperature, Humidity, and Occupancy Sensing via Wi-Fi Channel State Information on ESP32 Nodes

Joint Contactless Temperature, Humidity, and Occupancy Sensing via Wi-Fi Channel State Information on ESP32 Nodes | 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 Joint Contactless Temperature, Humidity, and Occupancy Sensing via Wi-Fi Channel State Information on ESP32 Nodes Saurav Chaudhari, Ketan Pise, Dinesh Fukate, Shantanu Gawande This is a preprint; it has not been peer reviewed by a journal.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-8690669/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 Smart buildings increasingly rely on dense instrumentation to monitor indoor temperature, humidity, and occupancy for energy-efficient HVAC control, yet conventional sensor deployments incur significant hardware, wiring, and maintenance costs. This paper proposes a joint contactless sensing framework that estimates ambient temperature, relative humidity, and occupancy state from Wi-Fi Channel State Information (CSI) using low-cost ESP32 nodes. Building on refractive-index based models of microwave propagation, we extend the Gladstone–Dale relation to incorporate water vapor density and derive sensitivity expressions that link multi-subcarrier CSI amplitude and phase to both temperature and humidity. We then exploit human-induced multipath perturbations and Doppler signatures to infer room occupancy without dedicated motion sensors. A multi-task learning pipeline is developed in two stages: (1) hybrid CSI feature extraction using statistical descriptors and discrete wavelet transform coefficients across 30 OFDM subcarriers, and (2) a shared-gradient boosting backbone with three task-specific heads for temperature regression, humidity regression, and occupancy classification. Experiments with ESP32-WROOM-32 devices in a climate chamber (15–35◦C, 30–80% RH) and three real-world indoor environments (laboratory, office, residential) demonstrate that the proposed approach achieves mean absolute error (MAE) of 0.72◦C for temperature, 4.6% for relative humidity, and 95.1% F1-score for binary occupancy detection, with 47 ms inference latency on-device. Compared to single-task baselines trained separately for each variable, the multi-task model improves temperature MAE by 13% and humidity MAE by 18%, while maintaining comparable occupancy accuracy. A 21 day deployment shows stable performance under routine human activity, HVAC cycles, and moderate Wi-Fi interference, with drift-controlled operation via daily calibration. The system enables cost-effective, maintenance-light ambient monitoring and occupancy-aware control using existing Wi-Fi infrastructure, reducing the need for dedicated environmental and motion sensors in smart buildings. Wi-Fi sensing Channel State Information Temperature monitoring Humidity sensing Occupancy detection Multi-task learning ESP32 Smart buildings Wireless sensing IoT Sensors Full Text Additional Declarations The authors declare no competing interests. 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-8690669","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":579982394,"identity":"08af0f8c-ab27-4326-9bee-7ad249b51bab","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":"https://orcid.org/0000-0003-1191-9015","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":true,"prefix":"","firstName":"Saurav","middleName":"","lastName":"Chaudhari","suffix":""},{"id":579982395,"identity":"2784bdfc-1e0e-4b6d-9302-2c8b867aaa20","order_by":1,"name":"Ketan Pise","email":"","orcid":"https://orcid.org/0000-0003-3315-6960","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":false,"prefix":"","firstName":"Ketan","middleName":"","lastName":"Pise","suffix":""},{"id":579982396,"identity":"0d43b956-4132-486c-81d6-e5ab19dd7fcc","order_by":2,"name":"Dinesh Fukate","email":"","orcid":"https://orcid.org/0009-0006-3193-5900","institution":"XZENT SOLUTIONS PRIVATE LIMITED","correspondingAuthor":false,"prefix":"","firstName":"Dinesh","middleName":"","lastName":"Fukate","suffix":""},{"id":579982397,"identity":"3afcaba6-e3d1-4c48-87fb-08966d8aa37d","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 07:01:01","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8690669/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8690669/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101206803,"identity":"eef23d7a-d049-40dc-af68-d143d588f382","added_by":"auto","created_at":"2026-01-27 09:56:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":274681,"visible":true,"origin":"","legend":"","description":"","filename":"DS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8690669/v1_covered_7c9dabd4-51af-4bb7-b4c9-ff1f2a7fe7b1.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eJoint Contactless Temperature, Humidity, and Occupancy Sensing via Wi-Fi Channel State Information on ESP32 Nodes\u003c/p\u003e","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, Humidity sensing, Occupancy detection, Multi-task learning, ESP32, Smart buildings, Wireless sensing, IoT Sensors","lastPublishedDoi":"10.21203/rs.3.rs-8690669/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8690669/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSmart buildings increasingly rely on dense instrumentation to monitor indoor temperature, humidity, and occupancy for energy-efficient HVAC control, yet conventional sensor deployments incur significant hardware, wiring, and maintenance costs. This paper proposes a joint contactless sensing framework that estimates ambient temperature, relative humidity, and occupancy state from Wi-Fi Channel State Information (CSI) using low-cost ESP32 nodes. Building on refractive-index based models of microwave propagation, we extend the Gladstone–Dale relation to incorporate water vapor density and derive sensitivity expressions that link multi-subcarrier CSI amplitude and phase to both temperature and humidity. We then exploit human-induced multipath perturbations and Doppler signatures to infer room occupancy without dedicated motion sensors. A multi-task learning pipeline is developed in two stages: (1) hybrid CSI feature extraction using statistical descriptors and discrete wavelet transform coefficients across 30 OFDM subcarriers, and (2) a shared-gradient boosting backbone with three task-specific heads for temperature regression, humidity regression, and occupancy classification. 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