Green Internet of Things and Machine Learning Towards a Smart Sustainable World 1st Edition by Roshani Raut, Sandeep Kautish, Zdzislaw Polkowski, Anil Kumar, Chuan Ming Liu ISBN 9781119792031 1119792037

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ISBN 10: 1119792037
ISBN 13: 9781119792031
Author: Roshani Raut, Sandeep Kautish, Zdzislaw Polkowski, Anil Kumar, Chuan Ming Liu

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

Green Internet of Things and Machine Learning Towards a Smart Sustainable World 1st Edition Table of contents:

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

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Tags: Roshani Raut, Sandeep Kautish, Zdzislaw Polkowski, Anil Kumar, Chuan Ming Liu, Green Internet