Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks

Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G 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 Deep Learnıng Based Enhanced Secure Emergency Vıdeo Streamıng Approach by Leveraging Blockchain Technology for Vehicular AdHoc 5G Networks Muhammad Awais, Abid Ali, Yousaf saeed, Sohail Jabbar, Awais Ahmad, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3925594/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Aug, 2024 Read the published version in Journal of Cloud Computing → Version 1 posted 7 You are reading this latest preprint version Abstract VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology. Vehicular Ad-Hoc Network Security Emergency Video Streaming Collaborative Routing Protocol Machine Learning Blockchain Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Aug, 2024 Read the published version in Journal of Cloud Computing → Version 1 posted Editorial decision: Revision requested 23 Feb, 2024 Reviews received at journal 19 Feb, 2024 Reviewers agreed at journal 13 Feb, 2024 Reviewers invited by journal 13 Feb, 2024 Editor assigned by journal 06 Feb, 2024 Submission checks completed at journal 05 Feb, 2024 First submitted to journal 03 Feb, 2024 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-3925594","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":271276689,"identity":"3e40e739-7bdd-4bfa-9bad-23765ecee7e7","order_by":0,"name":"Muhammad Awais","email":"","orcid":"","institution":"The University of Haripur","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Awais","suffix":""},{"id":271276690,"identity":"1d0d06c5-a42f-4fc2-a963-02a9937a1526","order_by":1,"name":"Abid Ali","email":"","orcid":"","institution":"GANK(S) DC 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