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Title	Tinyml Network Applications For Smart Cities
ID_Doc	57461
Authors	Ahmed Z.E.; Hashim A.A.; Saeed R.A.; Saeed M.M.
Year	2024
Published	TinyML for Edge Intelligence in IoT and LPWAN Networks
DOI	http://dx.doi.org/10.1016/B978-0-44-322202-3.00023-3
Abstract	As smart cities continue to evolve, the amount of data generated by sensors and devices has grown exponentially, making it challenging to analyze and process these data in real time. TinyML offers a solution to this problem by enabling on-device machine learning that can analyze and process data locally, reducing latency and improving security. By leveraging TinyML, smart city applications can become more efficient, sustainable, and effective in improving the quality of life for citizens. In this chapter, we explore the potential of TinyML in enhancing various smart city domains, such as traffic management, energy efficiency, predictive maintenance, and environmental applications. We also highlight real-world examples of TinyML applications in smart cities and the benefits they have provided. Moreover, we discuss the challenges that need to be addressed when implementing TinyML in smart cities and the potential of emerging technologies that may impact the use of TinyML in the future. By the end of this chapter, readers will have a better understanding of the potential of TinyML in enhancing smart cities and improving the quality of life for citizens. © 2024 by Elsevier Inc. All rights reserved, including those for text and data mining, AI training, and similar technologies.
Author Keywords	efficiency; machine learning; smart cities; sustainability; TinyML

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