Green Internet of Things and Machine Learning. Группа авторов
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Raut
Sandeep Kautish
Zdzislaw Polkowski
Anil Kumar
and
Chuan-Ming Liu
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-79203-1
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
The Internet of Things (IoT) is an evolving idea which is responsible for connecting billions of devices that acquire, perceive, and communicate data from their surroundings. Because this transmission of data uses significant energy, improving energy efficiency in IOT devices is a significant topic for research. The green internet of things (G-IoT) makes it possible for IoT devices to use less energy since intelligent processing and analysis are fundamental to constructing smart IOT applications with large data sets. Machine learning (ML) algorithms that can predict sustainable energy consumption can be used to prepare guidelines to make IoT device implementation easier.
The G-IoT, an updated version of the IoT, is applied for different applications to improve energy efficiency. Therefore, the material collected in this book has been edited to enhance the reader’s knowledge about the current research achievements and challenges in the area of G-IoT and ML applications. The book targets senior and junior engineers, undergraduate and postgraduate students, researchers, and anyone else interested in the trends, developments, and opportunities afforded by the G-IoT and ML concepts.
Although it was impossible to include all current aspects of the research in the targeted area in the twelve chapters of this book, it will be a useful tool in terms of the various possible methodologies that can provide G-IoT applications through the use of ML. A brief description of the various application fields and methodologies covered in each chapter follows:
– Chapter 1 provides a brief introduction to various artificial intelligence (AI), ML, and IoT-based approaches with their real-life applications. It encompasses the many aspects of the G-IoT based on IoT device use and presents an outline of how each web search engine, such as Bing or Google, is used to search the internet, as they employ strategies such as learning algorithms to rank the websites. For example, every time Facebook is used to identify the photo of friends, online fraud detection, online customer assistance, video surveillance, face recognition, email spam, and malware are based on various learning algorithms.
– Chapter 2 presents an AI equipment design that focuses on IoT applications. The design is established on the standard of learning automata, characterized by utilizing propositional logic. The reasoning-based support empowers low-vitality impressions just as high learning accuracy throughout is used for preparing and surmising, which are vital criteria for competent AI with a long working life. Methodical encoding of the input information into optimally equal reasoning blocks is essential to this circuit. The distribution of these squares is improved through a plan investigation and robotization stream utilizing field programmable entryway exhibit-based quick models and programming recreations. The plan stream considers an assisted hyperparameter search for matching the clashing demands of economy and high accuracy. Broad approvals of the equipment execution of the new engineering employing single- and multiclass AI datasets reveal potential for fundamentally reduced vitality compared to the current AI equipment structures.
– Chapter 3 focuses on the IoT as a technology that connects everything to the smart world. To develop a practical smart scenario, this chapter explores numerous technologies along with the difficulties involved in fulfilling the goal of a G-IoT. Since energy-efficient communication in large-scale IoT devices has become a key concern in recent years, there is a fundamental need in such networks to minimize overall battery-powered device power consumption to reduce data transmission costs and lengthen network life. This study requires a scalable and energy-efficient approach for G-IoT heterogeneous nodes. These nodes need to create energyefficient power conservation mechanisms to provide a longlived network.
– Chapter 4 discusses the role of ML and the G-IoT in agriculture. Machine learning and its various application domains are explained and extensively covered in the first part of the chapter. In agricultural and farming applications, there are pros and cons to using the IoT to preserve biodiversity and reduce greenhouse gas emissions. To minimize the drawbacks, the smart food and agricultural enterprises built using G-IoT and green nanotechnology are discussed. Furthermore, how the IoT and G-IoT empower agriculture with accurate and sustainable farming approaches is also discussed.
– Chapter 5 demonstrates the use of analytics and machine learning to improve crop data. For optimal crop growth, pH, water content, and humidity are key. Inadequate inputs might lead to a growth imbalance