Optimizing Energy-Conserving Sleep Strategies for NB-IoT Devices in Coordinated 5G Networks within Smart Environments

Optimizing Energy-Conserving Sleep Strategies for NB-IoT Devices in Coordinated 5G Networks within Smart Environments | 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 Optimizing Energy-Conserving Sleep Strategies for NB-IoT Devices in Coordinated 5G Networks within Smart Environments Mrs. J. Jenitha, L. K. Hema, Mr. S. Regilan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4303089/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 An increased interest in energy-efficient communication protocols to extend battery life and improve network scalability has resulted from the fast growth of Internet of Things (IoT) devices, especially Narrowband IoT (NB-IoT) devices. In this study, we suggest a unique method of sleep scheduling using machine learning techniques for NB-IoT networks. Our method seeks to optimize energy usage while maintaining responsive connection by dynamically adjusting the sleep patterns of NB-IoT devices depending on anticipated network activity levels. Using machine learning algorithms trained on historical data gathered from NB-IoT devices and base stations, the suggested process entails developing a prediction model. In order to produce real-time estimates of future network demand, the model analyzes a variety of input parameters, including as the surrounding environment, traffic patterns, and the closeness of the device to the base station. The sleep scheduling mechanism, which coordinates the sleep-wake cycles of NB-IoT devices to coincide with expected periods of low activity, is informed by these forecasts. We illustrate the efficacy of our machine learning-based sleep scheduling technique in attaining noteworthy energy savings while maintaining network performance, utilizing comprehensive simulations and real-world tests. We are able to strike a compromise between energy economy and responsiveness by cleverly scheduling sleep, which keeps NB-IoT devices operational for their monitoring and control functions while preserving battery life. Our study addresses the increasing need for sustainable IoT solutions in smart city settings and beyond by advancing energy-efficient communication protocols for NB-IoT networks. The suggested method opens the door for more effective and durable IoT ecosystems by providing useful advice and insights for applying machine learning-based sleep scheduling algorithms in actual IoT installations. NB-IoT 5G Sleep Scheduling Machine Learning Algorithms CNN based sleep scheduling 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-4303089","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":294137405,"identity":"7e2722ba-805d-4d1c-bcd5-a157d9315ae6","order_by":0,"name":"Mrs. J. 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In this study, we suggest a unique method of sleep scheduling using machine learning techniques for NB-IoT networks. Our method seeks to optimize energy usage while maintaining responsive connection by dynamically adjusting the sleep patterns of NB-IoT devices depending on anticipated network activity levels. Using machine learning algorithms trained on historical data gathered from NB-IoT devices and base stations, the suggested process entails developing a prediction model. In order to produce real-time estimates of future network demand, the model analyzes a variety of input parameters, including as the surrounding environment, traffic patterns, and the closeness of the device to the base station. The sleep scheduling mechanism, which coordinates the sleep-wake cycles of NB-IoT devices to coincide with expected periods of low activity, is informed by these forecasts. We illustrate the efficacy of our machine learning-based sleep scheduling technique in attaining noteworthy energy savings while maintaining network performance, utilizing comprehensive simulations and real-world tests. We are able to strike a compromise between energy economy and responsiveness by cleverly scheduling sleep, which keeps NB-IoT devices operational for their monitoring and control functions while preserving battery life. Our study addresses the increasing need for sustainable IoT solutions in smart city settings and beyond by advancing energy-efficient communication protocols for NB-IoT networks. 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