Machine Learning Paradigm for Internet of Things Applications. Группа авторов
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Scrivener Publishing 100 Cummings Center, Suite 541J Beverly, MA 01915-6106
Next-Generation Computing and Communication Engineering
Series Editors: Dr. G. R. Kanagachidambaresan and Dr. Kolla Bhanu Prakash
Developments in artificial intelligence are made more challenging because the involvement of multi-domain technology creates new problems for researchers. Therefore, in order to help meet the challenge, this book series concentrates on next generation computing and communication methodologies involving smart and ambient environment design. It is an publishing platform for monographs, handbooks, and edited volumes on Industry 4.0, agriculture, smart city development, new computing and communication paradigms. Although the series mainly focuses on design, it also addresses analytics and investigation of industry-related real-time problems.
Publishers at Scrivener
Martin Scrivener ([email protected]) Phillip Carmical ([email protected])
Machine Learning Paradigm for Internet of Things Applications
Edited by
Shalli Rani,
R. Maheswar
G. R. Kanagachidambaresan
Sachin Ahuja
and
Deepali Gupta
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-76047-4
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
Preface
Machine learning (ML) is the key tool for fast processing and decision-making applied to smart city applications and next-generation IoT devices, which require ML to satisfy their working objective. Machine learning has become a common subject to all people like engineers, doctors, pharmacy companies and business people. The book addresses the problem and new algorithms, their accuracy and fitness ratio for existing real-time problems. Tapping into that data to extract useful information is a challenge that’s starting to be met using the pattern-matching abilities of ML, which is a subset of the field of artificial intelligence (AI). In order to provide a smarter environment, there needs to be implemented IoT devices with machine learning. Machine learning will allow these smart devices to be smarter in a literal sense. They can analyze the data generated by the connected devices and get an insight into human behavioral patterns. Hence, it would not be wrong to say that if the IoT is the digital nervous system, then ML acts as its medulla oblongata. Without implementing ML, it would really be difficult for smart devices and the IoT to make smart decisions in real-time, severely limiting their capabilities. This book provides the challenges and the solution in these areas.
This book provides the state-of-the-art applications of Machine Learning in IoT environment. The most common use cases for machine learning and IoT data are predictive maintenance, followed by analyzing CCTV surveillance, smart home applications, smart-healthcare, in-store ‘contextualized marketing’ and intelligent transportation systems. Readers will gain an insight into the integration of Machine Learning with IoT in various application domains.
Lastly, we would like to thanks all the authors who contributed whole heartedly in bringing their ideas and research in the form of chapters.
Shalli Rani
R. Maheswar
G. R. KanagachidambaresanSachin AhujaDeepali