Internet of Things and Fog Computing-Enabled Solutions for Real-Life Challenges

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
Contributors
1 Fog Computing Fundamentals in the Internet of Things: A Taxonomy, Survey, and Future Directions
1.1 Introduction
1.2 What Is Internet of Things
1.2.1 How IoT Works
1.2.2 IoT-Enabling Technology
1.3 Cloud Computing
1.3.1 Cloud Layers
1.3.2 Cloud Computing Architecture
1.3.3 Benefits of Cloud Computing
1.4 Fog Computing
1.4.1 History of Fog Computing
1.4.2 How Fog Computing Works
1.4.3 Application of Fog Computing
1.4.4 Fog Computing Benefits and Drawbacks
1.4.5 What Materializes Inside the Nodes of Fog and the Cloud?
1.4.6 IoT and Fog Computing
1.4.7 How IoT Actually Relates to Your Enterprise
1.4.8 How IoT Actually Relates to Your Infrastructure
1.5 How to Trace and Save You in COVID-19 Network Transmission With the Aid of Fog Computing
1.5.1 Contextual and Problems
1.5.2 Scope
1.5.2.1 Implementation Overview
1.6 Conclusion
References
2 Role of IoT to Control the Movement of Infections During Pandemic
2.1 Introduction
2.2 The Internet of Things
2.3 Controlling Social Distances Using IoT
2.4 Early Diagnosis of Infection
2.5 Remote Patient Monitoring
2.6 Quarantine Control
2.7 IoT Applications
2.8 Future of the Internet of Medical Things
2.9 Conclusion
References
3 Internet of Things (IoT)-Based Smart Farming System: A Broad Study of Emerging Technologies
3.1 Introduction
3.2 Significant Applications
3.2.1 Soil Sampling and Mapping
3.2.2 Water System
3.2.3 Manure
3.2.4 Yield Observation, Anticipating, and Accumulation
3.3 Advanced Agricultural Practices
3.3.1 Green House Farming
3.3.2 Vertical Farming
3.3.3 Hydroponic
3.3.4 Phenotyping
3.4 Significant Equipment and Technologies
3.4.1 Modern Tractor
3.4.1.1 Event Detection Algorithm
3.4.1.2 Design of the Descending Seed Course Monitoring (DSCM) Appliance
3.4.2 Collecting Robots
3.4.3 Correspondence in Agriculture
3.4.3.1 Cellular Communication
3.4.3.2 Zigbee
3.4.3.3 Bluetooth
3.4.3.4 LoRa
3.4.3.5 Sigfox
3.5 Cloud Computing
3.6 Conclusion
References
4 IoT and FOG Space-Time Particulate Matter (PM_2.5) Concentration Forecasting for IoT-Based Air Pollution Monitoring Systems
4.1 Introduction
4.2 IoT- Based Air Quality Monitoring
4.3 Related Works
4.4 Overview
4.4.1 Data Description
4.4.2 Problem Formulation
4.5 Proposed Methodology
4.5.1 Proposed CNN-Based Encoder
4.5.2 Proposed LSTM for Learning Patterns in the Series Represented in Latent Space
4.5.3 Modelling
4.6 Evaluation Metrics
4.7 Experiments and Results
4.7.1 Performance Comparison With Baseline Models
4.7.2 Effectiveness of 1D-CNN in the Proposed CNN-LSTM Architecture
4.8 Conclusion
References
5 Model Predictive-Based Control for Supplies of Medicines During COVID-19
5.1 Introduction
5.2 System Model
5.3 Problem Formulation
5.4 System Dynamics
5.4.1 Simulation Results
5.5 Demand Forecasting
5.6 Conclusion
References
6 Role of Swarm Intelligence for Health Monitoring and Related Actions
6.1 Introduction
6.2 Proposed Technology
6.2.1 Sensors
6.2.2 Humidity and Temperature Sensors
6.2.3 Heart Rate Sensor
6.2.4 Accelerometer
6.2.5 Oximeter
6.2.6 Microcontroller/control Unit
6.2.7 Wireless Communication
6.2.8 Bluetooth
6.2.9 Wireless Fidelity (Wi-Fi)
6.2.10 IoT Server
6.2.11 Notifications of an Emergency
6.3 Obstacles and Constraints
6.3.1 Expenses for Servicing and Upkeep
6.3.2 Consumption of Energy
6.3.3 Confidentiality and Security of Data
6.3.4 The Administration of Data
6.3.5 Excavation of New Diseases
6.4 Potential Future Applications
6.4.1 Health-Related Internet of Things Services
6.4.2 Ambient Assisted Living (ALL)
6.4.3 The Importance of Mobile Connection in the IoT
6.4.4 Computed Intelligence
6.4.5 Healthcare Services in the Community
6.4.6 Information On Children’s Health (ICH)
6.4.7 Drug Adverse Response (DAR)
6.5 Real-Life Challenges and Their Solutions
6.5.1 Scheduling/load Balancing
6.5.2 Clustering
6.5.3 Optimization
6.5.4 Routing
6.6 Conclusion
Acknowledgments
References
7 Real-Time Implementation of an Implantable Antenna Using Chicken Swarm Optimization for IoT-Based Wearable Healthcare …
7.1 Introduction
7.2 Literature Review
7.3 Proposed Methodology
7.4 Results and Performance Analysis
7.4.1 Low Complex Channel Estimation Model
7.4.2 Data Preprocessing
7.4.3 Chicken Swarm Optimization (CSO)
7.4.4 Algorithm for CSO
7.4.5 Cooperative Antenna Selection – Neural Network Classifier
7.4.6 Gain
7.4.7 Mutual Coupling
7.4.8 Specific Absorption Rate (SAR)
7.4.9 Bandwidth
7.4.10 Efficiency
7.5 Conclusion and Future Discussion
References
8 Intelligent Traffic Light Systems for the Smart Cities
8.1 Introduction
8.2 Traffic Light Control System
8.2.1 Conventional Traffic Light System
8.2.2 Intelligent Traffic Light System
8.3 Basic Electronic Circuit of Intelligent Traffic Light System
8.4 Wireless Communication Network-Based Centralized Traffic Management System
8.5 Fog Computing-Based Smart Traffic Control Using Phase Optimization [13]
8.6 Conclusions
References
9 Case Study On Fog Computing With the Integration of Internet of Things: Applications, Challenges, and Future Directions
9.1 Introduction
9.1.1 Elements of IoT
9.2 Technologies Involved in IoT and Fog Computing
9.2.1 Wireless Sensor Network
9.3 Characteristics of Fog Computing
9.4 Fog Computing Principles
9.5 Fog Computing Architecture
9.6 Application of IoT and Fog Computing
9.6.1 Energy Efficient Datacenter
9.6.2 Home Automation
9.6.3 IoT in Agriculture
9.6.4 Reducing Pollution
9.6.5 Smart Transport System
9.6.6 Traffic Surveillance
9.6.7 Intelligent Parking Monitoring System
9.6.8 Safety Management
9.6.9 Traffic Lights Monitoring
9.6.10 Smart Water Management
9.6.11 Health Care Management System
9.6.12 Forest Fire Detection
9.6.13 IoT in Data Analytics
9.6.14 IoT-Based Real-Time Analytics in Fog Computing
9.6.15 IoT in Business Process
9.6.16 IoT Security
9.6.17 Identity Management and Privacy Using IoT
9.6.18 IoT in Education Sector
9.6.19 IoT Role in Oil and Gas Industry
9.7 Advantages of Using Fog Computing for IoT
9.8 Challenges Faced By Fog Architecture
9.9 Conclusion
References
10 Virtual Health Management Through IoT
10.1 Introduction
10.2 IoT and Its Background for COVID-19 Pandemic
10.3 Cloud Computing Assistance in COVID-19
10.4 Benefits of Cloud Computing
10.4.1 Diversity
10.4.2 Processing Power
10.4.3 Data Sharing
10.4.4 Expenses
10.5 Processes Involved in IoT for COVID-19
10 5.1 Convenience
10.5.2 Timeless
10.5.3 Accurateness
10.5.4 Safety and Insurance
10.5.5 Mobility
10.6 IOT for Verification and Tracking
10.7 Problems of Handling in Tracking for COVID-19
10.8 Next-Generation Proposed Tools and Discussions
10.8.1 Deep Machine Learning
10.8.2 New Generation Challenges
10.8.3 Advantages
10.8.4 Disadvantages
10.9 Key IoT Tools to Fight COVID-19 Pandemic
10.10 Wearable IOT Devices
10.11 Drones to Combat COVID-19
10.11.1 Uses of Drones
10.11.2 Transportation
10.11.3 Aerial Spraying
10.11.4 Surveillance of the Public Spaces
10.12 Robots During COVID-19 Situation
10.13 Internet of Things Buttons
10.13.1 Advantages
10.14 Conclusion
References
Index