Green Internet of Things and Machine Learning: Towards a Smart Sustainable World

by Anil Kumar, Chuan-Ming Liu, Roshani Raut, Sandeep Kautish, Zdzisław Pólkowski

Length: 384 pages
Edition: 1
Language: English
Publisher: Wiley-Scrivener
Publication Date: 2022-02-02
ISBN-10: 1119792037
ISBN-13: 9781119792031
Sales Rank: #9064868 (See Top 100 Books)

Health Economics and Financing

Encapsulates different case studies where green-IOT and machine learning can be used for making significant progress towards improvising the quality of life and sustainable environment.

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.

Green Internet of Things and Machine Learning lays the foundation of in-depth analysis of principles of Green-Internet of Things (G-IoT) using machine learning. It outlines various green ICT technologies, explores the potential towards diverse real-time areas, as well as highlighting various challenges and obstacles towards the implementation of G-IoT in the real world. Also, this book provides insights on how the machine learning and green IOT will impact various applications: It covers the Green-IOT and ML-based smart computing, ML techniques for reducing energy consumption in IOT devices, case studies of G-IOT and ML in the agricultural field, smart farming, smart transportation, banking industry and healthcare.

Audience

The book will be helpful for research scholars and researchers in the fields of computer science and engineering, information technology, electronics and electrical engineering. Industry experts, particularly in R&D divisions, can use this book as their problem-solving guide.

Cover
Table of Contents
Title Page
Copyright
Preface
1 G-IoT and ML for Smart Computing
    1.1 Introduction
    1.2 Machine Learning
    1.3 Deep Learning
    1.4 Correlation Between AI, ML, and DL
    1.5 Machine Learning–Based Smart Applications
    1.6 IoT
    1.7 Green IoT
    1.8 Green IoT–Based Technologies
    1.9 Life Cycle of Green IoT
    1.10 Applications
    1.11 Challenges and Opportunities for Green IoT
    1.12 Future of G-IoT
    1.13 Conclusion
    References
2 Machine Learning–Enabled Techniques for Reducing Energy Consumption of IoT Devices
    2.1 Introduction
    2.2 Internet of Things (IoT)
    2.3 Empowering Tools
    2.4 IoT in the Energy Sector
    2.5 Difficulties of Relating IoT
    2.6 Future Trends
    2.7 Conclusion
    References
3 Energy-Efficient Routing Infrastructure for Green IoT Network
    3.1 Introduction
    3.2 Overview of IoT
    3.3 Perspectives of Green Computing: Green IoT
    3.4 Routing Protocols for Heterogeneous IoT
    3.5 Machine Learning Application in Green IoT
    3.6 Conclusion
    References
4 Green IoT Towards Environmentally Friendly, Sustainable and Revolutionized Farming
    4.1 Introduction
    4.2 How is Machine Learning Used in Agricultural Field?
    4.3 What is IoT? How Can IoT Be Applied in Agriculture?
    4.4 What is Green IoT and Use of Green IoT in Agriculture?
    4.5 Conclusion: Risks of Using G-IoT in Agriculture
    References
5 CIoT: Internet of Green Things for Enhancement of Crop Data Using Analytics and Machine Learning
    5.1 Introduction
    5.2 Motivation
    5.3 Review of Literature
    5.4 Problem with Traditional Approach
    5.5 Tool Requirement
    5.6 Methodology
    5.7 Conclusion
    References
6 Smart Farming Through Deep Learning
    6.1 Introduction
    6.2 Literature Review
    6.3 Deep Learning in Agriculture
    6.4 Smart Farming
    6.5 Image Analysis of Agricultural Products
    6.6 Land-Quality Check
    6.7 Arduino-Based Soil Moisture Reading Kit
    6.8 Conclusion
    6.9 Future Work
    References
7 Green IoT and Machine Learning for Agricultural Applications
    7.1 Introduction
    7.2 Green IoT
    7.3 Machine Learning
    7.4 Conclusion
    References
8 IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production
    8.1 Introduction
    8.2 Literature Survey
    8.3 Conclusion
    References
9 Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS)
    9.1 Introduction
    9.2 Relevant Work and Research Motivation for GIoT-AHAS
    9.3 Conclusion
    References
10 Green IoT for Smart Transportation: Challenges, Issues, and Case Study
    10.1 Introduction
    10.2 Challenges of IoT
    10.3 Green IoT Communication Components
    10.4 Applications of IoT and Green IoT
    10.5 Issues of Concern
    10.6 Challenges for Green IoT
    10.7 Green IoT in Smart Transportation: Case Studies
    10.8 Conclusion
    References
11 Green Internet of Things (IoT) and Machine Learning (ML): The Combinatory Approach and Synthesis in the Banking Industry
    11.1 Introduction
    11.2 Research Objective
    11.3 Methodology
    11.4 Result and Discussion
    11.5 Conclusion
    References
12 Green Internet of Things (G-IoT) Technologies, Application, and Future Challenges
    12.1 Introduction
    12.2 The Internet of Thing (IoT)
    12.3 Elements of IoT
    12.4 The Green IoT: Overview
    12.5 Green IoT Technologies
    12.6 Green IoT Applications
    12.7 IoT in 5G Wireless Technologies
    12.8 Internet of Things in Smart City
    12.9 Green IoT Architecture for Smart Cities
    12.10 Advantages and Disadvantages of Green IoT
    12.11 Opportunities and Challenges
    12.12 Future of Green IoT
    12.13 Conclusion
    References
Index
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