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Submit a ProposalGreen Internet of Things and Machine Learning
 | Towards a Smart Sustainable World Roshani Raut, Sandeep Kautish, Zdzislaw Polkowski, Anil Kumar and Chuan-Ming Liu Copyright: 2022 | Status: Published ISBN: 9781119792031 | Hardcover | 349 pages | 80 illustrations Price: $225 USD  |
One Line Description
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.
Audience
The book will be helpful for research scholars, 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.
The book will be helpful for research scholars, 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.
Description
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 Thing 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.
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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 Thing 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.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
1. G-IoT and ML for Smart Computing
Karunendra Verma, Vineet Raj Singh Kushwah and Nilesh
1.1 Introduction
1.2 Machine Learning
1.2.1 Difference Between Artificial Intelligence and Machine Learning
1.2.2 Types of Machine Learning
1.2.2.1 Supervised Learning
1.2.2.2 Unsupervised Learning
1.2.2.3 Semi-Supervised Learning
1.2.2.4 Reinforcement Learning
1.3 Deep Learning
1.4 Correlation Between AI, ML, and DL
1.5 Machine Learning–Based Smart Applications
1.5.1 Supervised Learning–Based Applications
1.5.1.1 Email Spam Filtering
1.5.1.2 Face Recognition
1.5.1.3 Speech Recognition
1.5.1.4 Handwriting Recognition
1.5.1.5 Intrusion Detection
1.5.1.6 Data Center Optimization
1.5.2 Unsupervised Learning–Based Applications
1.5.2.1 Social Network Analysis
1.5.2.2 Medical Records
1.5.2.3 Speech Activity Detection
1.5.2.4 Analysis of Cancer Diagnosis
1.5.3 Semi-Supervised Learning–Based Applications
1.5.3.1 Mobile Learning Environments
1.5.3.2 Computational Advertisement
1.5.3.3 Sentiment Analysis
1.5.4 Reinforcement Learning–Based Applications
1.5.4.1 Traffic Forecasting Service
1.5.4.2 Computer Games
1.5.4.3 Machinery Applications
1.5.4.4 Stock Market Analysis
1.6 IoT
1.7 Green IoT
1.8 Green IoT–Based Technologies
1.8.1 Identification
1.8.2 Sensing
1.8.3 Communication Technologies
1.8.4 Computation
1.8.5 Services
1.8.6 Semantic
1.9 Life Cycle of Green IoT
1.10 Applications
1.10.1 Industrial Automation
1.10.1.1 Machine to Machine Communications
1.10.1.2 Plant Monitoring
1.10.2 Healthcare
1.10.2.1 Real-Time Tracking
1.10.2.2 Identification
1.10.2.3 Smart Data Collection
1.10.2.4 Smart Sensing
1.10.3 Environment Monitoring
1.10.3.1 Agriculture
1.10.3.2 Smog Control
1.10.3.3 Waste Management
1.10.3.4 Smart Water
1.10.4 Suburban Sector
1.10.4.1 Smart Buildings
1.10.4.2 Garbage Collection
1.10.4.3 Water Sensors
1.10.4.4 Smart Metering
1.10.5 People and Goods Transportation
1.10.5.1 Smart Parking
1.10.5.2 Smart Traffic Congestion Detection
1.10.6 Marketing and Shipment Management
1.10.6.1 Smart Logistics/Shipment
1.10.6.2 Managing Quality
1.10.7 Recycling
1.11 Challenges and Opportunities for Green IoT
1.11.1 Architecture of Green IoT
1.11.2 Green Infrastructure
1.11.3 Green Spectrum Management
1.11.4 Green Communication
1.11.5 Green Security and Servicing Provisioning
1.12 Future of G-IoT
1.13 Conclusion
References
2. Machine Learning–Enabled Techniques for Reducing Energy Consumption of IoT Devices
Yogini Dilip Borole, Jaya Dofe and C. G. Dethe
2.1 Introduction
2.1.2 Motivation
2.1.3 Methodology
2.2 Internet of Things (IoT)
2.3 Empowering Tools
2.3.1 Sensor Devices
2.3.2 Actuators
2.3.3 Communication Technologies
2.3.4 IoT Data and Computing
2.3.4.1 Distributed Computing
2.3.4.2 Fog Computing
2.4 IoT in the Energy Sector
2.4.1 IoT and Energy Generation
2.4.2 Smart Metropolises
2.4.3 Smart Grid
2.4.4 Smart Buildings Structures
2.4.5 Powerful Use of Energy in Industry
2.4.6 Insightful Transportation
2.5 Difficulties of Relating IoT
2.5.1 Energy Consumption
2.5.2 Synchronization of IoT With Subsystems
2.5.3 Client Privacy
2.5.4 Security Challenges
2.5.5 IoT Standardization and Architectural Concept
2.6 Future Trends
2.6.1 IoT and Blockchain
2.6.2 Artificial Intelligence and IoT
2.6.3 Green IoT
2.7 Conclusion
References
3. Energy-Efficient Routing Infrastructure for Green IoT Network
Pradeep Bedi, S. B. Goyal, Jugnesh Kumar and Shailesh Kumar
3.1 Introduction
3.2 Overview of IoT
3.3 Perspectives of Green Computing: Green IoT
3.3.1 Green Radio-Frequency
3.3.2 Green Wireless Sensor Network
3.3.3 Green Data Centers
3.3.4 Green Cloud Computing
3.3.5 Green Edge Computing
3.3.6 Green M2M
3.3.7 Green Information and Communication Technology (ICT) Principles
3.4 Routing Protocols for Heterogeneous IoT
3.4.1 Energy-Harvesting-Aware Routing Algorithm
3.4.2 Priority-Based Routing Algorithm
3.4.3 Cluster-Based Routing Algorithm
3.4.4 Bioinspired Routing Protocols
3.5 Machine Learning Application in Green IoT
3.6 Conclusion
References
4. Green IoT Towards Environmentally Friendly, Sustainable and Revolutionized Farming
Ravi Manne and Sneha Chowdary Kantheti
4.1 Introduction
4.1.1 Lifecycle of Agriculture
4.1.2 An Overview on How Machine Learning Works
4.2 How is Machine Learning Used in Agricultural Field?
4.2.1 Crop Management
4.2.2 Livestock Management
4.2.2.1 Animal Welfare
4.2.2.2 Livestock Production
4.2.3 Literature Review of Research Papers That Used Machine Learning in Agriculture
4.3 What is IoT? How Can IoT Be Applied in Agriculture?
4.3.1 Monitoring Climate Conditions
4.3.2 Greenhouse Automation
4.3.3 Crop Management
4.3.4 Cattle Monitoring and Management
4.3.5 Precision Farming and Agricultural Drones
4.3.6 Contract Farming
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
Sahana Shetty and Narayana Swamy Ramaiah
5.1 Introduction
5.2 Motivation
5.3 Review of Literature
5.4 Problem with Traditional Approach
5.5 Tool Requirement
5.5.1 Arduino UNO
5.5.2 Humidity Sensor
5.5.3 Relay
5.5.4 DC Motor
5.5.5 pH Sensor and CO2 Sensor
5.6 Methodology
5.7 Conclusion
References
6. Smart Farming Through Deep Learning
Sandeep Mathur, Disha Vaid and Ajay Rana
6.1 Introduction
6.2 Literature Review
6.3 Deep Learning in Agriculture
6.3.1 Feedforward Neural Network
6.3.1.1 Single-Layer Perceptron
6.3.1.2 Multi-Layer Perceptron
6.3.2 Recurrent Neural Network
6.3.3 Radial Basis Function Neural Network
6.3.4 Kohenen Self Organizing Neural Network
6.3.5 Modular Neural Network
6.4 Smart Farming
6.5 Image Analysis of Agricultural Products
6.6 Land-Quality Check
6.6.1 Nitrogen Status
6.6.2 Moisture Content
6.7 Arduino-Based Soil Moisture Reading Kit
6.7.1 Wastage of Water
6.7.2 Plants Dying Due to Over Watering
6.7.3 Expensive Product
6.8 Conclusion
6.9 Future Work
References
7. Green IoT and Machine Learning for Agricultural Applications
Keshavi Nalla and Seshu Vardhan Pothabathula
7.1 Introduction
7.2 Green IoT
7.2.1 Components of GAHA
7.2.2 Applications of Green IoT in Agriculture
7.2.2.1 Livestock Tracking and Geofencing
7.2.2.2 Fisheries
7.2.2.3 Smart Crops
7.2.2.4 G-Drones
7.2.2.5 Weeding Robots
7.2.2.6 Machine Navigation
7.2.2.7 Harvesting Robots
7.2.3 Green IoT Security Solutions for Agriculture
7.2.3.1 Precision Technology
7.2.3.2 Facility Agriculture
7.2.3.3 Contract Farming
7.3 Machine Learning
7.3.1 Applications of Machine Learning in Agriculture
7.3.1.1 Species Breeding and Species Selection
7.3.1.2 Species Recognition
7.3.1.3 Weed Detection
7.3.1.4 Crop Quality
7.3.1.5 Disease Detection
7.3.1.6 Yield Prediction
7.3.1.7 Field Conditions Management
7.3.1.8 Plant Seedling Classification
7.3.1.9 Greenhouse Simulation
7.4 Conclusion
References
8. IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production
Aneesha Gudavalli, G. JayaLakshmi and Suneetha Manne
8.1 Introduction
8.2 Literature Survey
8.2.1 Internet of Things in Field of Agriculture
8.2.2 Machine Learning With Internet of Things in Agriculture
8.2.3 Deep Learning With Internet of Things in Agriculture
8.3 Conclusion
References
9. Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS)
Anil L. Wanare and Sahebrao N. Patil
9.1 Introduction
9.2 Relevant Work and Research Motivation for GIoT-AHAS
9.2.1 Ubiquitous Computing
9.2.2 Ubiquitous Agriculture and Healthcare Application Requirement
9.2.3 Green Cloud Computing
9.2.4 Green IoT Agriculture and Healthcare Applications (GIoT-AHAS)
9.2.4.1 GIoT-AHAS Architecture
9.2.4.2 GIoT-AHAS Requirements
9.2.4.3 Applying Green Internet of Things to Agriculture and Healthcare System 9.2.5 Green IoT for AHAS (GIoT-AHAS)
9.2.5.1 GIoT to AHAS Components
9.2.6 Green Digital Wireless Sensor Networks
9.2.7 Green Cloud Computing
9.2.8 Green Machine-to-Machine
9.2.9 Green Data Processing Center (GDPC)
9.3 Conclusion
References
10. Green IoT for Smart Transportation: Challenges, Issues, and Case Study
Pradnya Borkar, Vijaya Balpande, Ujjwala Aher, Roshani Raut and M. Sulas Borkar
10.1 Introduction
10.2 Challenges of IoT
10.2.1 Green IoT
10.2.1.1 Hardware-Based
10.2.1.2 Software-Based
10.2.1.3 Policy-Based
10.2.1.4 Awareness-Based
10.2.1.5 Habitual-Based
10.2.1.6 Recycling-Based
10.3 Green IoT Communication Components
10.3.1 Green Internet Technologies
10.3.2 Green RFID Tags
10.3.3 Green WSN
10.3.4 Green Cloud Computing
10.3.5 Green DC
10.3.6 Green M2M
10.4 Applications of IoT and Green IoT
10.4.1 Green IoT in Transportation
10.5 Issues of Concern
10.5.1 End User Viewpoints
10.5.2 Energy Conservation
10.5.3 Data Security and Privacy
10.5.4 Preserving Contextual Data
10.5.5 Bandwidth Availability and Connectivity
10.6 Challenges for Green IoT
10.6.1 Standard Green IoT Architecture
10.6.2 Security and Quality of Service
10.6.3 Data Mining and Optimization
10.6.4 Various Traffic Management and Scheduling-Based Smart Public Transport Solutions
10.6.5 Scheduling and Admission Control for Independent Vehicle Public
Transportation System
10.6.6 Impacts on Public Transportation Management by Applying AVLs
10.6.7 Lisbon and Portugal’s Bus Ride Study and Prediction of Transport Usage
10.6.8 Smart Assistance for Public Transport System
10.7 Green IoT in Smart Transportation: Case Studies
10.7.1 Smart Traffic Signal
10.7.1.1 Description
10.7.1.2 Application
10.7.1.3 Advantages
10.7.1.4 Disadvantages
10.7.2 Cloud-Based Smart Parking System
10.7.2.1 Hardware—Car Parks CP1 and CP2
10.7.2.2 Smart Airport Management System
10.7.2.3 Intelligent Vehicle Parking System
10.7.2.4 IoT-Based Smart Vehicle Monitoring System
10.8 Conclusion
References
11. Green Internet of Things (IoT) and Machine Learning (ML):The Combinatory Approach and Synthesis in the Banking Industry
Prakashkumar Hasmukhbhai Patel, Chetan K Rathod and Karan Zaveri
11.1 Introduction
11.2 Research Objective
11.3 Methodology
11.4 Result and Discussion
11.4.1 Internet of Thing (IoT) in the Banking Industry
11.4.1.1 Internet of Things Facilitating Banking
11.4.1.2 Benefits of Internet of Things (IoT) in Banking
11.4.1.3 Other Benefits Benefits of the Internet of Thing (IoT) in Banking
11.4.2 Artificial Intelligence and Machine Learning in Banking
11.4.2.1 Significant Roles of Artificial Intelligence and Machine Learning in Banking
and Finance
11.4.2.2 Machine Learning for Safe Bank Transactions
11.4.3 Application of Machine Learning in Banking will Expand in 2021
11.4.4 Technologies that Can Be Adopted for Reducing Carbon Footprints
11.4.4.1 Green Internet of Things (Green IoT)
11.4.4.2 Green Cloud Computing Technology
11.4.4.3 Green Data Centre (GDC) Technology
11.5 Conclusion
References
12. Green Internet of Things (G-IoT) Technologies, Application, and Future Challenges
Komal Saxena, Abdul Basit and Vinod Kumar Shukla
12.1 Introduction
12.2 The Internet of Thing (IoT)
12.2.1 How IoT Works
12.2.2 Evolution of Internet of Things
12.3 Elements of IoT
12.4 The Green IoT: Overview
12.5 Green IoT Technologies
12.5.1 Green RFID Tags
12.5.2 Green Sensing Networks
12.5.3 Green Internet Technologies
12.6 Green IoT Applications
12.6.1 Smart Homes
12.6.2 Modern Automation
12.6.3 Smart Healthcare
12.6.4 Keen Grid
12.6.5 Smart City
12.6.6 Green Cloud Computing Technology
12.7 IoT in 5G Wireless Technologies
12.7.1 Internet of Things and 5G: The Future of 5G Communications
12.7.1.1 5G and IoT: The Possibilities
12.7.1.2 5G and Business IoT
12.8 Internet of Things in Smart City
12.8.1 Physical Experiences of Smart Cities Around the World
12.8.1.1 In Netherlands (Amsterdam)
12.8.1.2 In France (Nice)
12.8.1.3 Padova (Italy)
12.9 Green IoT Architecture for Smart Cities
12.9.1 Perception Layer
12.9.1.1 Smart City Sensor Layer
12.9.2 Network Layer
12.9.2.1 Cloud Services Layer
12.9.3 Application Layer
12.9.3.1 Data Collection Layer
12.9.3.2 Data Processing Layer
12.10 Advantages and Disadvantages of Green IoT
12.10.1 Advantages of Green IoT
12.10.2 Disadvantages of Green IoT
12.11 Opportunities and Challenges
12.12 Future of Green IoT
12.13 Conclusion
References
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Preface
1. G-IoT and ML for Smart Computing
Karunendra Verma, Vineet Raj Singh Kushwah and Nilesh
1.1 Introduction
1.2 Machine Learning
1.2.1 Difference Between Artificial Intelligence and Machine Learning
1.2.2 Types of Machine Learning
1.2.2.1 Supervised Learning
1.2.2.2 Unsupervised Learning
1.2.2.3 Semi-Supervised Learning
1.2.2.4 Reinforcement Learning
1.3 Deep Learning
1.4 Correlation Between AI, ML, and DL
1.5 Machine Learning–Based Smart Applications
1.5.1 Supervised Learning–Based Applications
1.5.1.1 Email Spam Filtering
1.5.1.2 Face Recognition
1.5.1.3 Speech Recognition
1.5.1.4 Handwriting Recognition
1.5.1.5 Intrusion Detection
1.5.1.6 Data Center Optimization
1.5.2 Unsupervised Learning–Based Applications
1.5.2.1 Social Network Analysis
1.5.2.2 Medical Records
1.5.2.3 Speech Activity Detection
1.5.2.4 Analysis of Cancer Diagnosis
1.5.3 Semi-Supervised Learning–Based Applications
1.5.3.1 Mobile Learning Environments
1.5.3.2 Computational Advertisement
1.5.3.3 Sentiment Analysis
1.5.4 Reinforcement Learning–Based Applications
1.5.4.1 Traffic Forecasting Service
1.5.4.2 Computer Games
1.5.4.3 Machinery Applications
1.5.4.4 Stock Market Analysis
1.6 IoT
1.7 Green IoT
1.8 Green IoT–Based Technologies
1.8.1 Identification
1.8.2 Sensing
1.8.3 Communication Technologies
1.8.4 Computation
1.8.5 Services
1.8.6 Semantic
1.9 Life Cycle of Green IoT
1.10 Applications
1.10.1 Industrial Automation
1.10.1.1 Machine to Machine Communications
1.10.1.2 Plant Monitoring
1.10.2 Healthcare
1.10.2.1 Real-Time Tracking
1.10.2.2 Identification
1.10.2.3 Smart Data Collection
1.10.2.4 Smart Sensing
1.10.3 Environment Monitoring
1.10.3.1 Agriculture
1.10.3.2 Smog Control
1.10.3.3 Waste Management
1.10.3.4 Smart Water
1.10.4 Suburban Sector
1.10.4.1 Smart Buildings
1.10.4.2 Garbage Collection
1.10.4.3 Water Sensors
1.10.4.4 Smart Metering
1.10.5 People and Goods Transportation
1.10.5.1 Smart Parking
1.10.5.2 Smart Traffic Congestion Detection
1.10.6 Marketing and Shipment Management
1.10.6.1 Smart Logistics/Shipment
1.10.6.2 Managing Quality
1.10.7 Recycling
1.11 Challenges and Opportunities for Green IoT
1.11.1 Architecture of Green IoT
1.11.2 Green Infrastructure
1.11.3 Green Spectrum Management
1.11.4 Green Communication
1.11.5 Green Security and Servicing Provisioning
1.12 Future of G-IoT
1.13 Conclusion
References
2. Machine Learning–Enabled Techniques for Reducing Energy Consumption of IoT Devices
Yogini Dilip Borole, Jaya Dofe and C. G. Dethe
2.1 Introduction
2.1.2 Motivation
2.1.3 Methodology
2.2 Internet of Things (IoT)
2.3 Empowering Tools
2.3.1 Sensor Devices
2.3.2 Actuators
2.3.3 Communication Technologies
2.3.4 IoT Data and Computing
2.3.4.1 Distributed Computing
2.3.4.2 Fog Computing
2.4 IoT in the Energy Sector
2.4.1 IoT and Energy Generation
2.4.2 Smart Metropolises
2.4.3 Smart Grid
2.4.4 Smart Buildings Structures
2.4.5 Powerful Use of Energy in Industry
2.4.6 Insightful Transportation
2.5 Difficulties of Relating IoT
2.5.1 Energy Consumption
2.5.2 Synchronization of IoT With Subsystems
2.5.3 Client Privacy
2.5.4 Security Challenges
2.5.5 IoT Standardization and Architectural Concept
2.6 Future Trends
2.6.1 IoT and Blockchain
2.6.2 Artificial Intelligence and IoT
2.6.3 Green IoT
2.7 Conclusion
References
3. Energy-Efficient Routing Infrastructure for Green IoT Network
Pradeep Bedi, S. B. Goyal, Jugnesh Kumar and Shailesh Kumar
3.1 Introduction
3.2 Overview of IoT
3.3 Perspectives of Green Computing: Green IoT
3.3.1 Green Radio-Frequency
3.3.2 Green Wireless Sensor Network
3.3.3 Green Data Centers
3.3.4 Green Cloud Computing
3.3.5 Green Edge Computing
3.3.6 Green M2M
3.3.7 Green Information and Communication Technology (ICT) Principles
3.4 Routing Protocols for Heterogeneous IoT
3.4.1 Energy-Harvesting-Aware Routing Algorithm
3.4.2 Priority-Based Routing Algorithm
3.4.3 Cluster-Based Routing Algorithm
3.4.4 Bioinspired Routing Protocols
3.5 Machine Learning Application in Green IoT
3.6 Conclusion
References
4. Green IoT Towards Environmentally Friendly, Sustainable and Revolutionized Farming
Ravi Manne and Sneha Chowdary Kantheti
4.1 Introduction
4.1.1 Lifecycle of Agriculture
4.1.2 An Overview on How Machine Learning Works
4.2 How is Machine Learning Used in Agricultural Field?
4.2.1 Crop Management
4.2.2 Livestock Management
4.2.2.1 Animal Welfare
4.2.2.2 Livestock Production
4.2.3 Literature Review of Research Papers That Used Machine Learning in Agriculture
4.3 What is IoT? How Can IoT Be Applied in Agriculture?
4.3.1 Monitoring Climate Conditions
4.3.2 Greenhouse Automation
4.3.3 Crop Management
4.3.4 Cattle Monitoring and Management
4.3.5 Precision Farming and Agricultural Drones
4.3.6 Contract Farming
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
Sahana Shetty and Narayana Swamy Ramaiah
5.1 Introduction
5.2 Motivation
5.3 Review of Literature
5.4 Problem with Traditional Approach
5.5 Tool Requirement
5.5.1 Arduino UNO
5.5.2 Humidity Sensor
5.5.3 Relay
5.5.4 DC Motor
5.5.5 pH Sensor and CO2 Sensor
5.6 Methodology
5.7 Conclusion
References
6. Smart Farming Through Deep Learning
Sandeep Mathur, Disha Vaid and Ajay Rana
6.1 Introduction
6.2 Literature Review
6.3 Deep Learning in Agriculture
6.3.1 Feedforward Neural Network
6.3.1.1 Single-Layer Perceptron
6.3.1.2 Multi-Layer Perceptron
6.3.2 Recurrent Neural Network
6.3.3 Radial Basis Function Neural Network
6.3.4 Kohenen Self Organizing Neural Network
6.3.5 Modular Neural Network
6.4 Smart Farming
6.5 Image Analysis of Agricultural Products
6.6 Land-Quality Check
6.6.1 Nitrogen Status
6.6.2 Moisture Content
6.7 Arduino-Based Soil Moisture Reading Kit
6.7.1 Wastage of Water
6.7.2 Plants Dying Due to Over Watering
6.7.3 Expensive Product
6.8 Conclusion
6.9 Future Work
References
7. Green IoT and Machine Learning for Agricultural Applications
Keshavi Nalla and Seshu Vardhan Pothabathula
7.1 Introduction
7.2 Green IoT
7.2.1 Components of GAHA
7.2.2 Applications of Green IoT in Agriculture
7.2.2.1 Livestock Tracking and Geofencing
7.2.2.2 Fisheries
7.2.2.3 Smart Crops
7.2.2.4 G-Drones
7.2.2.5 Weeding Robots
7.2.2.6 Machine Navigation
7.2.2.7 Harvesting Robots
7.2.3 Green IoT Security Solutions for Agriculture
7.2.3.1 Precision Technology
7.2.3.2 Facility Agriculture
7.2.3.3 Contract Farming
7.3 Machine Learning
7.3.1 Applications of Machine Learning in Agriculture
7.3.1.1 Species Breeding and Species Selection
7.3.1.2 Species Recognition
7.3.1.3 Weed Detection
7.3.1.4 Crop Quality
7.3.1.5 Disease Detection
7.3.1.6 Yield Prediction
7.3.1.7 Field Conditions Management
7.3.1.8 Plant Seedling Classification
7.3.1.9 Greenhouse Simulation
7.4 Conclusion
References
8. IoT-Enabled AI-Based Model to Assess Land Suitability for Crop Production
Aneesha Gudavalli, G. JayaLakshmi and Suneetha Manne
8.1 Introduction
8.2 Literature Survey
8.2.1 Internet of Things in Field of Agriculture
8.2.2 Machine Learning With Internet of Things in Agriculture
8.2.3 Deep Learning With Internet of Things in Agriculture
8.3 Conclusion
References
9. Green Internet of Things (GIoT): Agriculture and Healthcare Application System (GIoT-AHAS)
Anil L. Wanare and Sahebrao N. Patil
9.1 Introduction
9.2 Relevant Work and Research Motivation for GIoT-AHAS
9.2.1 Ubiquitous Computing
9.2.2 Ubiquitous Agriculture and Healthcare Application Requirement
9.2.3 Green Cloud Computing
9.2.4 Green IoT Agriculture and Healthcare Applications (GIoT-AHAS)
9.2.4.1 GIoT-AHAS Architecture
9.2.4.2 GIoT-AHAS Requirements
9.2.4.3 Applying Green Internet of Things to Agriculture and Healthcare System 9.2.5 Green IoT for AHAS (GIoT-AHAS)
9.2.5.1 GIoT to AHAS Components
9.2.6 Green Digital Wireless Sensor Networks
9.2.7 Green Cloud Computing
9.2.8 Green Machine-to-Machine
9.2.9 Green Data Processing Center (GDPC)
9.3 Conclusion
References
10. Green IoT for Smart Transportation: Challenges, Issues, and Case Study
Pradnya Borkar, Vijaya Balpande, Ujjwala Aher, Roshani Raut and M. Sulas Borkar
10.1 Introduction
10.2 Challenges of IoT
10.2.1 Green IoT
10.2.1.1 Hardware-Based
10.2.1.2 Software-Based
10.2.1.3 Policy-Based
10.2.1.4 Awareness-Based
10.2.1.5 Habitual-Based
10.2.1.6 Recycling-Based
10.3 Green IoT Communication Components
10.3.1 Green Internet Technologies
10.3.2 Green RFID Tags
10.3.3 Green WSN
10.3.4 Green Cloud Computing
10.3.5 Green DC
10.3.6 Green M2M
10.4 Applications of IoT and Green IoT
10.4.1 Green IoT in Transportation
10.5 Issues of Concern
10.5.1 End User Viewpoints
10.5.2 Energy Conservation
10.5.3 Data Security and Privacy
10.5.4 Preserving Contextual Data
10.5.5 Bandwidth Availability and Connectivity
10.6 Challenges for Green IoT
10.6.1 Standard Green IoT Architecture
10.6.2 Security and Quality of Service
10.6.3 Data Mining and Optimization
10.6.4 Various Traffic Management and Scheduling-Based Smart Public Transport Solutions
10.6.5 Scheduling and Admission Control for Independent Vehicle Public
Transportation System
10.6.6 Impacts on Public Transportation Management by Applying AVLs
10.6.7 Lisbon and Portugal’s Bus Ride Study and Prediction of Transport Usage
10.6.8 Smart Assistance for Public Transport System
10.7 Green IoT in Smart Transportation: Case Studies
10.7.1 Smart Traffic Signal
10.7.1.1 Description
10.7.1.2 Application
10.7.1.3 Advantages
10.7.1.4 Disadvantages
10.7.2 Cloud-Based Smart Parking System
10.7.2.1 Hardware—Car Parks CP1 and CP2
10.7.2.2 Smart Airport Management System
10.7.2.3 Intelligent Vehicle Parking System
10.7.2.4 IoT-Based Smart Vehicle Monitoring System
10.8 Conclusion
References
11. Green Internet of Things (IoT) and Machine Learning (ML):The Combinatory Approach and Synthesis in the Banking Industry
Prakashkumar Hasmukhbhai Patel, Chetan K Rathod and Karan Zaveri
11.1 Introduction
11.2 Research Objective
11.3 Methodology
11.4 Result and Discussion
11.4.1 Internet of Thing (IoT) in the Banking Industry
11.4.1.1 Internet of Things Facilitating Banking
11.4.1.2 Benefits of Internet of Things (IoT) in Banking
11.4.1.3 Other Benefits Benefits of the Internet of Thing (IoT) in Banking
11.4.2 Artificial Intelligence and Machine Learning in Banking
11.4.2.1 Significant Roles of Artificial Intelligence and Machine Learning in Banking
and Finance
11.4.2.2 Machine Learning for Safe Bank Transactions
11.4.3 Application of Machine Learning in Banking will Expand in 2021
11.4.4 Technologies that Can Be Adopted for Reducing Carbon Footprints
11.4.4.1 Green Internet of Things (Green IoT)
11.4.4.2 Green Cloud Computing Technology
11.4.4.3 Green Data Centre (GDC) Technology
11.5 Conclusion
References
12. Green Internet of Things (G-IoT) Technologies, Application, and Future Challenges
Komal Saxena, Abdul Basit and Vinod Kumar Shukla
12.1 Introduction
12.2 The Internet of Thing (IoT)
12.2.1 How IoT Works
12.2.2 Evolution of Internet of Things
12.3 Elements of IoT
12.4 The Green IoT: Overview
12.5 Green IoT Technologies
12.5.1 Green RFID Tags
12.5.2 Green Sensing Networks
12.5.3 Green Internet Technologies
12.6 Green IoT Applications
12.6.1 Smart Homes
12.6.2 Modern Automation
12.6.3 Smart Healthcare
12.6.4 Keen Grid
12.6.5 Smart City
12.6.6 Green Cloud Computing Technology
12.7 IoT in 5G Wireless Technologies
12.7.1 Internet of Things and 5G: The Future of 5G Communications
12.7.1.1 5G and IoT: The Possibilities
12.7.1.2 5G and Business IoT
12.8 Internet of Things in Smart City
12.8.1 Physical Experiences of Smart Cities Around the World
12.8.1.1 In Netherlands (Amsterdam)
12.8.1.2 In France (Nice)
12.8.1.3 Padova (Italy)
12.9 Green IoT Architecture for Smart Cities
12.9.1 Perception Layer
12.9.1.1 Smart City Sensor Layer
12.9.2 Network Layer
12.9.2.1 Cloud Services Layer
12.9.3 Application Layer
12.9.3.1 Data Collection Layer
12.9.3.2 Data Processing Layer
12.10 Advantages and Disadvantages of Green IoT
12.10.1 Advantages of Green IoT
12.10.2 Disadvantages of Green IoT
12.11 Opportunities and Challenges
12.12 Future of Green IoT
12.13 Conclusion
References
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