Green Internet of Things and Machine Learning

Towards a Smart Sustainable World

Inbunden, Engelska, 2022

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-02-04
Mått:10 x 10 x 10 mm
Vikt:454 g
Format:Inbunden
Språk:Engelska
Antal sidor:384
Förlag:John Wiley & Sons Inc
ISBN:9781119792031

Utforska kategorier

Artificiell intelligens inom Data och IT

Mer om författaren

Roshani Raut, PhD is an associate professor in the Department of Information Technology at Pimpri Chinchwad College of Engineering, Pune University, India. She has presented and published more than 70 research communications in national/international conferences and journals and has published 13 patents.Sandeep Kautish, PhD is a professor & Dean of Academics with LBEF Campus, Kathmandu Nepal. He has published more than 40 papers in international journals. Zdzislaw Polkowski, PhD is a professor in the Faculty of Technical Sciences, Jan Wyzykowski University, Polkowice, Poland. He has published more than 75 research articles in peer-reviewed journals. Anil Kumar, PhD is a professor of CSE and Head of Department of Information Technology, DIT University, India. He has published more than 200 research papers. Chuan-Ming Liu, PhD is a professor in the Department of Computer Science and Information Engineering (CSIE), National Taipei University of Technology (Taipei Tech), Taiwan. He has published more than 100 research article is international journals.

Innehållsförteckning

Preface xvii1 G-IoT and ML for Smart Computing 1Karunendra Verma, Vineet Raj Singh Kushwah and Nilesh1.1 Introduction 21.2 Machine Learning 21.3 Deep Learning 61.4 Correlation Between AI, ML, and dl 71.5 Machine Learning–Based Smart Applications 71.6 IoT 111.7 Green IoT 121.8 Green IoT–Based Technologies 131.9 Life Cycle of Green IoT 161.10 Applications 161.11 Challenges and Opportunities for Green IoT 211.12 Future of G-IoT 221.13 Conclusion 22References 232 Machine Learning–Enabled Techniques for Reducing Energy Consumption of IoT Devices 27Yogini Dilip Borole, Jaya Dofe and C. G. Dethe2.1 Introduction 282.2 Internet of Things (IoT) 332.3 Empowering Tools 352.4 IoT in the Energy Sector 482.5 Difficulties of Relating IoT 612.6 Future Trends 662.7 Conclusion 72References 733 Energy-Efficient Routing Infrastructure for Green IoT Network 87Pradeep Bedi, S. B. Goyal, Jugnesh Kumar and Shailesh Kumar3.1 Introduction 883.2 Overview of IoT 893.3 Perspectives of Green Computing: Green IoT 913.4 Routing Protocols for Heterogeneous IoT 993.5 Machine Learning Application in Green IoT 1043.6 Conclusion 107References 1074 Green IoT Towards Environmentally Friendly, Sustainable and Revolutionized Farming 113Ravi Manne and Sneha Chowdary Kantheti4.1 Introduction 1144.2 How is Machine Learning Used in Agricultural Field? 1194.3 What is IoT? How Can IoT Be Applied in Agriculture? 1274.4 What is Green IoT and Use of Green IoT in Agriculture? 1304.5 Conclusion: Risks of Using G-IoT in Agriculture 135References 1385 CIoT: Internet of Green Things for Enhancement of Crop Data Using Analytics and Machine Learning 141Sahana Shetty and Narayana Swamy Ramaiah5.1 Introduction 1425.2 Motivation 1465.3 Review of Literature 1475.4 Problem with Traditional Approach 1545.5 Tool Requirement 1555.6 Methodology 1595.7 Conclusion 160References 1616 Smart Farming Through Deep Learning 163Sandeep Mathur, Disha Vaid and Ajay Rana6.1 Introduction 1636.2 Literature Review 1656.3 Deep Learning in Agriculture 1696.4 Smart Farming 1746.5 Image Analysis of Agricultural Products 1766.6 Land-Quality Check 1786.7 Arduino-Based Soil Moisture Reading Kit 1806.8 Conclusion 1836.9 Future Work 184References 1857 Green IoT and Machine Learning for Agricultural Applications 189Keshavi Nalla and Seshu Vardhan Pothabathula7.1 Introduction 1907.2 Green IoT 1927.3 Machine Learning 2017.4 Conclusion 210References 2108 IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production 215Aneesha Gudavalli, G. JayaLakshmi and Suneetha Manne8.1 Introduction 2168.2 Literature Survey 2178.3 Conclusion 235References 2359 Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS) 239Anil L. Wanare and Sahebrao N. Patil9.1 Introduction 2409.2 Relevant Work and Research Motivation for GIoT-AHAS 2439.3 Conclusion 263References 26410 Green IoT for Smart Transportation: Challenges, Issues, and Case Study 269Pradnya Borkar, Vijaya Balpande, Ujjwala Aher, Roshani Raut and M. Sulas Borkar10.1 Introduction 27010.2 Challenges of IoT 27010.3 Green IoT Communication Components 27410.4 Applications of IoT and Green IoT 27510.5 Issues of Concern 27910.6 Challenges for Green IoT 28010.7 Green IoT in Smart Transportation: Case Studies10.8 Conclusion 292References 29211 Green Internet of Things (IoT) and Machine Learning (ML): The Combinatory Approach and Synthesis in the Banking Industry 297Prakashkumar Hasmukhbhai Patel, Chetan K Rathod and Karan Zaveri11.1 Introduction 29811.2 Research Objective 29911.3 Methodology 29911.4 Result and Discussion 29911.5 Conclusion 313References 31412 Green Internet of Things (G-IoT) Technologies, Application, and Future Challenges 317Komal Saxena, Abdul Basit and Vinod Kumar Shukla12.1 Introduction 31812.2 The Internet of Thing (IoT) 31912.3 Elements of IoT 32212.4 The Green IoT: Overview 32512.5 Green IoT Technologies 32612.6 Green IoT Applications 32912.7 IoT in 5G Wireless Technologies 33312.8 Internet of Things in Smart City 33512.9 Green IoT Architecture for Smart Cities 33712.10 Advantages and Disadvantages of Green IoT 34212.11 Opportunities and Challenges 34312.12 Future of Green IoT 34412.13 Conclusion 345References 345Index 349