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Title	Advanced Machine Learning Innovations In Embedded Systems And Narrowband Internet Of Things (Nb-Iot) Devices
ID_Doc	6522
Authors	Dhanalakshmi M.; Nand Kishor Kumar G.; Himabindu G.; Gosavi V.R.; Sundar Ganesh C.S.; Premanand R.
Year	2025
Published	Integrating Artificial Intelligence Into the Energy Sector
DOI	http://dx.doi.org/10.4018/979-8-3693-7112-1.ch016
Abstract	This chapter explores the convergence of machine learning (ML) and embedded systems in the context of Narrowband Internet of Things (NB-IoT) devices. It highlights the latest innovations that enable real-time data processing, decision-making, and predictive analytics on resource-constrained devices. The chapter delves into key ML techniques such as federated learning, edge AI, and lightweight neural networks that are transforming the capabilities of embedded systems and NB-IoT. Additionally, it discusses the challenges of implementing ML models in low-power environments and the strategies to overcome these limitations, including model compression, hardware accelerators, and efficient algorithms. Through case studies and practical applications, the chapter demonstrates how these advancements are driving the deployment of intelligent IoT solutions across various industries, including smart cities, healthcare, and industrial automation, paving the way for more autonomous, efficient, and scalable IoT ecosystems. © 2025, IGI Global Scientific Publishing.
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