IoT-based Intelligent Modelling for Environmental and Ecological Engineering: IoT Next Generation EcoAgro Systems (Lecture Notes on Data Engineering and Communications Technologies)

✍ Scribed by Paul Krause (editor), Fatos Xhafa (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 318
• Category: Library

✦ Synopsis

This book brings to readers thirteen chapters with contributions to the benefits of using IoT and Cloud Computing to agro-ecosystems from a multi-disciplinary perspective. IoT and Cloud systems have prompted the development of a Cloud digital ecosystem referred to as Cloud-to-thing continuum computing. The key success of IoT computing and the Cloud digital ecosystem is that IoT can be integrated seamlessly with the physical environment and therefore has the potential to leverage innovative services in agro-ecosystems. Areas such as ecological monitoring, agriculture, and biodiversity constitute a large area of potential application of IoT and Cloud technologies. In contrast to traditional agriculture systems that have employed aggressive policies to increase productivity, new agro-ecosystems aim to increase productivity but also achieve efficiency and competitiveness in modern sustainable agriculture and contribute, more broadly, to the green economy and sustainable food-chain industry.

Fundamental research as well as concrete applications from various real-life scenarios, such as smart farming, precision agriculture, green agriculture, sustainable livestock and sow farming, climate threat, and societal and environmental impacts, is presented. Research issues and challenges are also discussed towards envisioning efficient and scalable solutions to agro-ecosystems based on IoT and Cloud technologies. Our fundamental belief is that we can collectively trigger a new revolution that will transition agriculture into an equable system that not only feeds the world, but also contributes to mitigating the climate change and biodiversity crises that our historical actions have triggered.

✦ Table of Contents

Preface
Acknowledgments
Contents
Contributors
IoT-Based Computational Modeling for Next Generation Agro-Ecosystems: Research Issues, Emerging Trends and Challenges
1 Introduction
2 Fundamental Concepts and Computational Models
2.1 Cloud-to-Thing Continuum: IoT, Edge, Fog, and Cloud Computing Technologies
2.2 Agro-Ecosystem and Ecological Networks
2.3 Connecting the Two Worlds: Under-Ground and Above-Ground
2.4 IoT Infrastructure and Deployment for Agro-Ecosystems
2.5 Life Cycle for IoT Device and Infrastructure Management
3 Modeling and Alert Systems for Agro-Ecosystems Based on Anomaly Detection
3.1 Symbiotic Modeling and the Digital Twin
3.2 Anomaly Detection Algorithms
4 Data Cycle for Agro-Ecosystems: From IoT Data Collection to Decision Making and End-to-End Management Systems
4.1 Data Collection and Acquisition
4.2 Real Time Data Pre-processing and Anomaly Detection
4.3 Real Time Anomaly Prediction in Data Streams
4.4 Data Structuring and Semantic Data Enrichment
4.5 Knowledge and Analytics Integration with Back-End Management and Business Intelligence Systems
5 Machine Learning: Killer Applications Are from Agriculture
6 Emerging Research Trends and Further Issues
6.1 Data Quality and Benchmarks for Agro-Ecosystems
6.2 Digital Agro-Ecosystems Adoption, Cooperation with Local Stakeholders and Policy-Makers
6.3 Ethics, Health & Safety Issues in Agro-Ecosystems
7 Conclusion
References
An IoT-Based Time Constrained Spectrum Trading in Wireless Communication for Tertiary Market
1 Introduction
2 Spectrum Trading in Tertiary Market
2.1 Evolution of Spectrum Allocation Market
2.2 Problem Statement and Scenario
2.3 Definitions and Terminology
2.4 Proposed Mechanisms: STOM-OSM and STOM-TSM
2.5 Analysis of the Algorithms
3 IoT-Based Spectrum Trading in Tertiary Market
3.1 Proposed Mechanisms
3.2 Realistic Implementation of Proposed Framework
4 Experiments and Computational Results
4.1 Simulation Set-Up
4.2 Analysis of the Results
5 Conclusion
References
AgriEdge: Edge Intelligent 5G Narrow Band Internet of Drone Things for Agriculture 4.0
1 Introduction
2 Literature Review
3 Advantage of Dynamic Sensors Over Static Sensors in Agriculture 4.0
4 Security Issues in Agriculture 4.0
5 Advantage of Using 5G NB-IoDT Over Wi-Fi and Wi-Fi 6 in Agriculture 4.0
5.1 Comparison of Download Speed Among Wi-Fi, 4G, and 5G Network
5.2 Advantage of 5G NB-IoDT Over Wi-Fi and Wi-Fi 6 from Cyber Security Perspective
5.3 Contactless Voice User Interface for 5G NB-IoDT
5.4 Satellite-Based 5G NB-IoDT Implementation
6 Future Open Research Challenges
7 Conclusion
References
Drones for Intelligent Agricultural Management
1 Introduction
2 Related Works
3 Use of Drone in Precision Agricultural Management
4 Methodology and Description
4.1 Drone Hardware and Description
4.2 Software Description
4.3 Mission Planner
5 Spraying Mechanism
5.1 Analysis of the Sprinkling Mechanism
6 Monitoring Crops and Lands
7 Results and Applications
8 Conclusion
References
Multi-Modal Sensor Nodes in Experimental Scalable Agricultural IoT Application Scenarios
1 Introduction
2 IoT Wireless Technologies in Agriculture
3 Sensor Nodes Architecture Issues
4 Implementation Details and Evaluation Techniques
5 Results and Discussion
6 Conclusions
References
Design Architecture of Intelligent Agri-Infrastructure Incorporating IoT and Cloud: Link Budget and Socio-Economic Impact
1 Introduction
2 Related Works and Literature
3 System Architecture, Software Architecture, and Frameworks
3.1 System Description
3.2 System Framework
3.3 Functional Framework
3.4 Circuit Design
3.5 Communication Networks and Protocols
3.6 Agri-Robotics
3.7 Physical Implementation
3.8 Data Communication
3.9 Code Architecture
3.10 Data Visualization and Storing
3.11 Power
3.12 Agri-Copter
3.13 Link Budget and Link-Level Analysis
4 Results
5 Socio-Economic Impacts
6 Conclusion
References
Remote Sensing and Soil Quality
1 Introduction
1.1 Structure of this Chapter
2 Soil Health
3 Satellite Imagery and NDVI
4 Soil Health Monitoring
5 Supervised Learning for Land Cover Classification
5.1 Data Collection and Preparation
5.2 Data Preparation
6 Land Cover Map 2015
6.1 Data Preparation
7 Modelling
7.1 Data Augmentation and Refinement
7.2 Test Design
8 Test and Evaluation
8.1 Test Metrics
9 Results
10 Evaluation
11 Conclusions and Next Steps
References
Machine Learning Modelling-Powered IoT Systems for Smart Applications
1 Introduction
2 Internet of Things (IoT): An Overview and Challenges
2.1 Overview of IoT Technologies
2.2 Challenges of IoT Technologies
3 ML Categories-Based IoT Applications
3.1 Machine Learning Categories
3.2 ML Tools-Based IoT Applications
4 IoT-Based ML Open Issues
4.1 Lightweight IoT-Based ML Approaches
4.2 Distributed IoT-Based ML Tools
4.3 Federated IoT-Based ML Tools
4.4 IoT-Based ML at the Edge
4.5 IoT-Based ML Data Munging
5 Proposed IoT-Based ML Scenario for Smart Agriculture 4.0: Smart Irrigation System
5.1 Overview of the Agriculture 4.0
5.2 Proposed Smart Irrigation System
5.3 Results and Discussions
6 Conclusion
References
Enabling IoT Wireless Technologies in Sustainable Livestock Farming Toward Agriculture 4.0
1 Introduction
2 Context of IoT Wireless Technologies in PLF
3 Applications of IoT Wireless Technologies in PLF
3.1 Animal Monitoring—Forecast and Control of Imminent Animal Diseases
3.2 Ambient Conditions and Meteorological Data Monitoring
3.3 Security Surveillance and Control of Livestock Facilities
4 Discussion
5 Conclusion
References
A Cloud-Based Decision Support System to Support Decisions in Sow Farms
1 Introduction
2 Sow Herd Management and DSS Tools
2.1 Sow Herd Management Models
2.2 Review of Decision Support Systems
3 The eSow DSS Overview
3.1 Model Management Subsystem
3.2 Data Base Management Subsystem
3.3 Interface Subsystem and Workflow
4 Cloud Architecture
5 Case Study
5.1 Data Integration
5.2 Data Explorer
5.3 Optimal Recommendations
5.4 Scenario Analysis
6 Conclusions
References
Automatic Plant Leaf Disease Detection and Auto-Medicine Using IoT Technology
1 Introduction
2 Scope of the Project
3 Literature Survey
4 Plant Diseases Analysis and Its Symptoms
4.1 Bacterial Disease Symptoms
4.2 Viral Disease Symptoms
4.3 Fungal Disease Symptoms
5 Proposed Methodology
6 Software Description
7 Experimentation and Results
7.1 Advantages of the Proposed System
8 Conclusion
Discuss briefly if the proposed system would be the suitable choice for a small to medium size Farming enterprise? What is the area to be covered by the proposed system? What would be the cost of implanting such a system? Please expand on this part.
References
Dynamic Shift from Cloud Computing to Industry 4.0: Eco-Friendly Choice or Climate Change Threat
1 Introduction
2 Cloud Computing
3 Industry 4.0
3.1 Healthcare 4.0
3.2 Agriculture 4.0
4 Climate Change: Projected Warming in the 21st Century
5 Quantifying Carbon Footprints of Cloud Computing in a Warming World
6 Dynamic Shift from Cloud Computing to Industry 4.0
7 Eco-friendly Choice or Climate Change Threat
8 Impact of Carbon Footprints on Climate Change
9 Future Research Challenges and Opportunities
9.1 Role of Cloud Computing in Global Warming and Its Quantification
9.2 Increasing Digitization of Economies
9.3 Development of Novel Frameworks for Large-Scale Hybrid Infrastructures
9.4 Pandemic Induced Reduction in Emissions Due to Cloud Computing
9.5 Role of Artificial Intelligence in Tackling Climate Change
9.6 Earth System Modelling and Advances Statistical Techniques Using Cloud Computing for Climate Change Research
9.7 Role of Quantum Computing in Climate Control
10 Summary and Conclusions
References
A Research Roadmap for IoT Monitoring and Computational Modelling for Next Generation Agriculture
1 Introduction
2 The Breakthrough Vision
3 Building the Community
4 Expected Impacts
5 The Fifth Geosphere
6 Conclusion
References