Green Internet of Things and Machine Learning

1 Cover

2 Title Page

3 Copyright

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

12 8 IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production 8.1 Introduction 8.2 Literature Survey 8.3 Conclusion References

13 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

14 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

15 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

16 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

17 Index

18 End User License Agreement

1 Cover

2 Table of Contents

3 Title page

4 Copyright

5 Preface

6 Begin Reading

7 Index

8 End User License Agreement

1 Chapter 1 Figure 1.1 Classification of machine learning. Figure 1.2 Process of supervised learning. Figure 1.3 Process of unsupervised learning. Figure 1.4 Process of reinforcement learning. Figure 1.5 Correlation between AI, ML, and DL. Figure 1.6 Life cycle of Green IoT. Figure 1.7 Green IoT–based applications.

2 Chapter 2 Figure 2.1 Three sections of energy segment. Figure 2.2 Benefits of IoT in renewable energy sources. Figure 2.3 Chart representing the parts of an IoT stage. Figure 2.4 Different types of sensors for different applications. Figure 2.5 Classic wireless sensor node design. Figure 2.6 An integrated information connectivity in a smart city model. Figure 2.7 Segment of domestic energy intake. Figure 2.8 Claims of IoT in an incorporated smart energy arrangement.Figure 2.9 Innovation in energy sector with blockchain and IoT.Figure 2.10 Virtual power plant with artificial intelligence and IoT.Figure 2.11 Green Internet of Things (G-IoT) as a crucial technology.

3 Chapter 3Figure 3.1 Key elements of IoT.Figure 3.2 IoT system.Figure 3.3 Components of green computing.Figure 3.4 Components of green computing.Figure 3.5 Green data center approaches.

4 Chapter 4Figure 4.1 Lifecycle of agriculture.Figure 4.2 Machine Learning workflow.Figure 4.3 Machine Learning relation with others.Figure 4.4 Applications of Green Internet of Things.

5 Chapter 5Figure 5.1 Arduino board.Figure 5.2 Humidity sensor.Figure 5.3 Relay.Figure 5.4 DC 12-V water motor.Figure 5.5 System architecture of CIoT. https://techatronic.com/smart-agricultur...

6 Chapter 6Figure 6.1 Typical crop cycle.Figure 6.2 Graph showing the growth in purchase of IoT devices [15].Figure 6.3 Graph showing the growth in data generation by IoT in agriculture [15...Figure 6.4 Connection of our prototype.

7 Chapter 7Figure 7.1 Applications of Green IoT.Figure 7.2 Sensor-cloud architecture for Green IoT [6].Figure 7.3 Important factors supporting GAHA architecture.Figure 7.4 Complementary pathways for implementing Green Computing in computers ...Figure 7.5 Components of GAHA.Figure 7.6 Machine learning as a subset of artificial intelligence [17].Figure 7.7 Edge detection of a rice blast disease using the Robert cross method ...

8 Chapter 8Figure 8.1 IoT-based precision farming cycle.Figure 8.2 Cloud of Things.Figure 8.3 Irrigation system using CloudIoT.Figure 8.4 IoT protocols.Figure 8.5 LEACH network topology.Figure 8.6 Synthetic Minority Oversampling Technique (SMOTE).Figure 8.7 Block diagram of IoT-based drone for enhancement of crop quality in a...Figure 8.8 Support vector machine.Figure 8.9 Raspberry Pi 3 board and the Intel Movidius Neural Compute Stick.Figure 8.10 DNN.Figure 8.11 MDFC-ResNet.Figure 8.12 RNN, LSTM, and GRU structure.Figure 8.13 Progressive neural network.Figure 8.14 Single-task and multi-task policy distillation.Figure 8.15 Different memory-based deep Q-network.

9 Chapter 9Figure 9.1 Basic Green IoT for both agriculture and health network.Figure 9.2 (a) GIoT-AHAS network platform with gateway.Figure 9.2 (b) GIoT AHAS network platform with security management system.Figure 9.3 Green IoT agriculture and healthcare applications (GIoT-AHAS).Figure 9.4 Building blocks of GIoT to AHAS connected to IoT-based system.Figure 9.5 Agriculture and farm remote monitoring and low-power green WSN topolo...Figure 9.6 Remote monitoring in healthcare system and low-power green WSN topolo...

10 Chapter 10Figure 10.1 Green cycle.Figure 10.2 Green IoT applications.Figure 10.3 Smart traffic signal system.Figure 10.4 Overall architecture of cloud-based parking system.Figure 10.5 Overall block diagram.Figure 10.6 Architecture of airport management system.Figure 10.7 Architecture of intelligent vehicle monitoring system.Figure 10.8 Overall architecture of the SVMS system.Figure 10.9 Internal architecture of IoT system.

11 Chapter 11Figure 11.1 Some potential application domains of Internet of Things.Figure 11.2 Artificial Intelligence in banking.Figure 11.3 Internet of things in banking.

12 Chapter 12Figure 12.1 Green Internet of Things (G-IoT) for sustainable development.Figure 12.2 Internet of Things (IoT) [34].Figure 12.3 Elements of IoT.Figure 12.4 Key component of Green IoT.Figure 12.5 Key technologies for Green IoT [37].Figure 12.6 Green industrial IoT architecture: an energy-efficient perspective [...Figure 12.7 Green IoT architecture for smart city.Figure 12.8 IoT sensors [35].