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Table of Contents
1 Cover
4 Preface
5 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
6 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
7 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
8 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
9 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
10 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
11 7 Green IoT and Machine Learning for Agricultural Applications 7.1 Introduction 7.2 Green IoT 7.3 Machine Learning 7.4 Conclusion References
