Recently, Tiny Machine Learning (Tiny ML) has gained incredible importance due to its capabilities of creating lightweight machine learning (ML) frameworks aiming at low latency, lower energy consumption, lower bandwidth requirement, improved data security and privacy, and other performance necessities. As billions of battery-operated embedded Io T and low power wide area networks (LPWAN) nodes with very low on-board memory and computational capabilities are getting connected to the Internet each year, there is a critical need to have a special computational framework like Tiny ML. Tiny ML for Edge Intelligence in Io T and LPWAN Networks presents the evolution, developments, and advances in Tiny ML as applied to Io T and LPWANs. It starts by providing the foundations of Io T/LPWANs, low power embedded systems and hardware, the role of artificial intelligence and machine learning in communication networks in general and cloud/edge intelligence. It then presents the concepts, methods, algorithms and tools of Tiny ML. Practical applications of the use of Tiny ML are given from health and industrial fields which provide practical guidance on the design of applications and the selection of appropriate technologies. Tiny ML for Edge Intelligence in Io T and LPWAN Networks is highly suitable for academic researchers and professional system engineers, architects, designers, testers, deployment engineers seeking to design ultra-lower power and time-critical applications. It would also help in designing the networks for emerging and future applications for resource-constrained nodes. – This book provides one-stop solutions for emerging Tiny ML for Io T and LPWAN applications. – The principles and methods of Tiny ML are explained, with a focus on how it can be used for Io T, 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.
来自同一作者的更多电子书 / 编辑
18,646 此类电子书
