IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network

IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural 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 IODnet: Indoor/Outdoor Telecommunication Signal Detection through Deep Neural Network Meisam Abdollahi, Sepideh Mashhadi, Ramin Sabzalizadeh, Alireza Mirzaei, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3112795/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 In many Internet of Things (IoT) applications, knowing the device's location can be quite important for some reasons such as asset tracking and inventory management, geolocation services, safety and security, environmental monitoring, and proximity-based interactions. Mobile users often experience mobile services/applications within an indoor or outdoor environment. Operators and other service providers can offer more appropriate services to users if they predict their location. Several attempts have been made to categorize the location of users, but this paper proposes a methodology that increases the accuracy of the predicted values through Deep Neural Networks. Based on the proposed method, the accuracy of operator-side labels can be improved by comparing operator-side labeled datasets with real-world labeled drive-test collected datasets. The goal was to develop an accurate model to correct unassured labels of a dataset with more accurate labels based on three datasets collected with crowd-sourcing and drive-testing approaches. Also, the proposed method was compared with state-of-the-art learning algorithms in order to justify its superiority. The experimental results indicate that the F1-score metric may be as high as 98% in some datasets. Deep Neural Network Indoor/Outdoor Localization Crowd-sourcing Fingerprinting Hyperparameter Tuning 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-3112795","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":213830989,"identity":"1f90f9c0-aa44-4a74-974c-73c03f65426f","order_by":0,"name":"Meisam 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