Communication Protocols and Data Transmission in Machine Learning and IoT-based Traffic Management System

Communication Protocols and Data Transmission in Machine Learning and IoT-based Traffic Management System | 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 Communication Protocols and Data Transmission in Machine Learning and IoT-based Traffic Management System Aiswarya S, Dr.s.Parthiban This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7757110/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 As urbanization picks up speed, effectively managing traffic is a significant obstacle to the creation of smart cities. In order to address this issue, a novel strategy is proposed in this research that combines machine learning (ML) with Internet of Things (IoT) technologies. Designing and putting into place a flexible traffic-management system that maximizes traffic flow, reduces congestion, and improves overall transportation efficiency is the main goal. The suggested solution makes use of real-time data gathered from various Internet of Things (IoT) devices, including congestion sensors and cameras, and GPS-enabled automobiles. The ML algorithms can continuously examine and gain insight into patterns, trends, and irregularities in traffic patterns thanks to these data sources' comprehensive views of the traffic conditions. These data are used by the machine learning (ML) algorithms used in the system to forecast possible traffic events, identify crowded regions, and predict traffic patterns. These forecasts give the traffic- management system the ability to dynamically change the timing of traffic signals, redirect cars, and distribute resources in response to the shifting traffic conditions. The article also explores the structure and communication protocols required to link the IoT devices with the machine learning (ML) traffic-management system in order to achieve seamless integration. In order to ensure the system's durability and dependability in real-world circumstances, it also handles the issues related to confidentiality of information, security, and scalability. Cell Communication and Signaling Traffic Flow Improvement Transmission Protocols Greener Transportation Machine Learning (ML) Internet of Things (IoT) 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-7757110","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":523218003,"identity":"5066c36d-4957-4ac0-ba7a-f84a1026c4a2","order_by":0,"name":"Aiswarya S","email":"","orcid":"https://orcid.org/0009-0004-0184-6558","institution":"saveetha school of 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In order to address this issue, a novel strategy is proposed in this research that combines machine learning (ML) with Internet of Things (IoT) technologies. Designing and putting into place a flexible traffic-management system that maximizes traffic flow, reduces congestion, and improves overall transportation efficiency is the main goal. The suggested solution makes use of real-time data gathered from various Internet of Things (IoT) devices, including congestion sensors and cameras, and GPS-enabled automobiles. The ML algorithms can continuously examine and gain insight into patterns, trends, and irregularities in traffic patterns thanks to these data sources' comprehensive views of the traffic conditions. These data are used by the machine learning (ML) algorithms used in the system to forecast possible traffic events, identify crowded regions, and predict traffic patterns. These forecasts give the traffic- management system the ability to dynamically change the timing of traffic signals, redirect cars, and distribute resources in response to the shifting traffic conditions. The article also explores the structure and communication protocols required to link the IoT devices with the machine learning (ML) traffic-management system in order to achieve seamless integration. In order to ensure the system's durability and dependability in real-world circumstances, it also handles the issues related to confidentiality of information, security, and scalability.\u003c/p\u003e","manuscriptTitle":"Communication Protocols and Data Transmission in Machine Learning and IoT-based Traffic Management System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 11:05:18","doi":"10.21203/rs.3.rs-7757110/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":"f7642852-947f-43f5-be5d-1a4156f7bb19","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55720095,"name":"Cell Communication and Signaling"}],"tags":[],"updatedAt":"2025-10-03T11:05:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-03 11:05:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7757110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7757110","identity":"rs-7757110","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}