TinyML for Edge Intelligence in IoT and LPWAN Networks

TinyML for Edge Intelligence in IoT and LPWAN Networks
Author: Bharat S Chaudhari (Editor), Sheetal N Ghorpade (Editor), Marco Zennaro (Editor), Rytis Paškauskas (Editor)
Edition 版次: 1st
Publication Date 出版日期: 2024-07-01
Language 语言: English
Print Length 页数: 518 pages
ISBN-10: 0443222029
ISBN-13: 9780443222023


Book Description
By finelybook

TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to the Internet of Things (IoT) and low-power wide area networks (LPWANs). It starts by providing the foundations of IoT/LPWANs, low-power embedded systems and hardware, the role of AI and machine learning in communication networks in general, and cloud/edge intelligence. It then presents the concepts, methods, algorithms, and tools of TinyML. Practical applications of TinyML are given from the healthcare and industrial sectors, providing practical guidance on the design of applications and the selection of appropriate technologies.

  • This book provides one-stop solutions for emerging TinyML for IoT and LPWAN applications.
  • The principles and methods of TinyML are explained, with a focus on how it can be used for IoT, LPWANs, and 5G applications.
  • Applications from the healthcare and industrial sectors are presented.
  • Guidance on the design of applications and the selection of appropriate technologies is provided.

Review

A one-stop resource on TinyML as applied to IOT and LPWANs

From the Back Cover

TinyML for Edge Intelligence in IoT and LPWAN Networks presents the evolution, developments, and advances in TinyML as applied to the Internet of Things (IoT) and low-power wide area networks (LPWANs). It starts by providing the foundations of IoT/LPWANs, low-power embedded systems and hardware, the role of AI and machine learning in communication networks in general, and cloud/edge intelligence. It then presents the concepts, methods, algorithms, and tools of TinyML. Practical applications of TinyML are given from the healthcare and industrial sectors, providing practical guidance on the design of applications and the selection of appropriate technologies.

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