Preface xvii 1 G-IoT and ML for Smart Computing 1 Karunendra Verma, Vineet Raj Singh Kushwah and Nilesh 1.1 Introduction 2 1.2 Machine Learning 2 1.3 Deep Learning 6 1.4 Correlation Between AI, ML, and dl 7 1.5 Machine Learning–Based Smart Applications 7 1.6 IoT 11 1.7 Green IoT 12 1.8 Green IoT–Based Technologies 13 1.9 Life Cycle of Green IoT 16 1.10 Applications 16 1.11 Challenges and Opportunities for Green IoT 21 1.12 Future of G-IoT 22 1.13 Conclusion 22 References 23 2 Machine Learning–Enabled Techniques for Reducing Energy Consumption of IoT Devices 27 Yogini Dilip Borole, Jaya Dofe and C. G. Dethe 2.1 Introduction 28 2.2 Internet of Things (IoT) 33 2.3 Empowering Tools 35 2.4 IoT in the Energy Sector 48 2.5 Difficulties of Relating IoT 61 2.6 Future Trends 66 2.7 Conclusion 72 References 73 3 Energy-Efficient Routing Infrastructure for Green IoT Network 87 Pradeep Bedi, S. B. Goyal, Jugnesh Kumar and Shailesh Kumar 3.1 Introduction 88 3.2 Overview of IoT 89 3.3 Perspectives of Green Computing: Green IoT 91 3.4 Routing Protocols for Heterogeneous IoT 99 3.5 Machine Learning Application in Green IoT 104 3.6 Conclusion 107 References 107 4 Green IoT Towards Environmentally Friendly, Sustainable and Revolutionized Farming 113 Ravi Manne and Sneha Chowdary Kantheti 4.1 Introduction 114 4.2 How is Machine Learning Used in Agricultural Field? 119 4.3 What is IoT? How Can IoT Be Applied in Agriculture? 127 4.4 What is Green IoT and Use of Green IoT in Agriculture? 130 4.5 Conclusion: Risks of Using G-IoT in Agriculture 135 References 138 5 CIoT: Internet of Green Things for Enhancement of Crop Data Using Analytics and Machine Learning 141 Sahana Shetty and Narayana Swamy Ramaiah 5.1 Introduction 142 5.2 Motivation 146 5.3 Review of Literature 147 5.4 Problem with Traditional Approach 154 5.5 Tool Requirement 155 5.6 Methodology 159 5.7 Conclusion 160 References 161 6 Smart Farming Through Deep Learning 163 Sandeep Mathur, Disha Vaid and Ajay Rana 6.1 Introduction 163 6.2 Literature Review 165 6.3 Deep Learning in Agriculture 169 6.4 Smart Farming 174 6.5 Image Analysis of Agricultural Products 176 6.6 Land-Quality Check 178 6.7 Arduino-Based Soil Moisture Reading Kit 180 6.8 Conclusion 183 6.9 Future Work 184 References 185 7 Green IoT and Machine Learning for Agricultural Applications 189 Keshavi Nalla and Seshu Vardhan Pothabathula 7.1 Introduction 190 7.2 Green IoT 192 7.3 Machine Learning 201 7.4 Conclusion 210 References 210 8 IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production 215 Aneesha Gudavalli, G. JayaLakshmi and Suneetha Manne 8.1 Introduction 216 8.2 Literature Survey 217 8.3 Conclusion 235 References 235 9 Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS) 239 Anil L. Wanare and Sahebrao N. Patil 9.1 Introduction 240 9.2 Relevant Work and Research Motivation for GIoT-AHAS 243 9.3 Conclusion 263 References 264 10 Green IoT for Smart Transportation: Challenges, Issues, and Case Study 269 Pradnya Borkar, Vijaya Balpande, Ujjwala Aher, Roshani Raut and M. Sulas Borkar 10.1 Introduction 270 10.2 Challenges of IoT 270 10.3 Green IoT Communication Components 274 10.4 Applications of IoT and Green IoT 275 10.5 Issues of Concern 279 10.6 Challenges for Green IoT 280 10.7 Green IoT in Smart Transportation: Case Studies 10.8 Conclusion 292 References 292 11 Green Internet of Things (IoT) and Machine Learning (ML): The Combinatory Approach and Synthesis in the Banking Industry 297 Prakashkumar Hasmukhbhai Patel, Chetan K Rathod and Karan Zaveri 11.1 Introduction 298 11.2 Research Objective 299 11.3 Methodology 299 11.4 Result and Discussion 299 11.5 Conclusion 313 References 314 12 Green Internet of Things (G-IoT) Technologies, Application, and Future Challenges 317 Komal Saxena, Abdul Basit and Vinod Kumar Shukla 12.1 Introduction 318 12.2 The Internet of Thing (IoT) 319 12.3 Elements of IoT 322 12.4 The Green IoT: Overview 325 12.5 Green IoT Technologies 326 12.6 Green IoT Applications 329 12.7 IoT in 5G Wireless Technologies 333 12.8 Internet of Things in Smart City 335 12.9 Green IoT Architecture for Smart Cities 337 12.10 Advantages and Disadvantages of Green IoT 342 12.11 Opportunities and Challenges 343 12.12 Future of Green IoT 344 12.13 Conclusion 345 References 345 Index 349

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.