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Title Tinyml For 5G Networks
ID_Doc 57460
Authors Saeed M.M.; Saeed R.A.; Ahmed Z.E.
Year 2024
Published TinyML for Edge Intelligence in IoT and LPWAN Networks
DOI http://dx.doi.org/10.1016/B978-0-44-322202-3.00014-2
Abstract This chapter examines how machine learning and 5G networks interact, with a particular emphasis on creating machine learning algorithms that can operate on devices with limited resources at the network's edge. In order to enable cognitive processing at the network's edge, the chapter opens with an introduction to 5G networks, the 5G network architecture, requirements for 5G use cases, and security in the wide scope of 5G. The chapter then covers various aspects of TinyML, including data collection and preprocessing, feature extraction, model training, and model deployment on 5G networks. The chapter provides a detailed overview of each of these topics, including the challenges and trade-offs involved in designing TinyML algorithms for 5G networks. The chapter also includes several case studies and real-world examples of TinyML applications in the context of 5G networks, including autonomous vehicles, smart cities, and industrial automation. These case studies demonstrate the potential of TinyML to enable intelligent processing and decision-making in real time and to transform various industries and domains. A significant resource for engineers, academics, and developers who are interested in using TinyML to create intelligent systems on 5G networks, the chapter offers a thorough overview of TinyML for 5G networks. In addition to highlighting TinyML's ability to support a variety of applications and use cases within the context of 5G networks, this chapter offers helpful advice for creating and deploying TinyML algorithms on resource-constrained devices at the network's edge. © 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.
Author Keywords 5G networks; edge intelligence; LPWAN; machine learning; security; TinyML


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