Fog Computing for Intelligent Cloud IoT Systems

Fog Computing for Intelligent Cloud IoT Systems

FOG COMPUTING FOR INTELLIGENT CLOUD IOT SYSTEMS This book is a comprehensive guide on fog computing and how it facilitates computing, storage, and networking services Fog computing is a decentralized computing structure that connects data, devices, and the cloud. It is an extension of cloud computin...

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Detalles Bibliográficos
Autor principal: Banerjee, Chandan (-)
Otros Autores: Ghosh, Anupam, Chakraborty, Rajdeep, Elngar, Ahmed A.
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2024.
Edición:1st ed
Colección:Advances in Learning Analytics for Intelligent Cloud-IoT Systems Series
Materias:
Internet of things.
Cloud computing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009828037706719
Tabla de Contenidos:
  • Cover
  • Series Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Part I: Study of Fog Computing and Machine Learning
  • Chapter 1 Fog Computing: Architecture and Application
  • 1.1 Introduction
  • 1.2 Fog Computing: An Overview
  • 1.3 Fog Computing for Intelligent Cloud-IoT System
  • 1.4 Fog Computing Architecture
  • 1.5 Basic Modules of Fog Computing
  • 1.6 Cloud Computing vs. Fog Computing
  • 1.7 Fog Computing vs. IoT
  • 1.8 Applications of Fog Computing
  • 1.9 Will the Fog Be Taken Over by the Cloud?
  • 1.10 Challenges in Fog Computing
  • 1.11 Future of Fog Computing
  • 1.12 Conclusion
  • References
  • Chapter 2 A Comparative Review on Different Techniques of Computation Offloading in Mobile Cloud Computing
  • 2.1 Introduction
  • 2.2 Related Works
  • 2.3 Computation Offloading Techniques
  • 2.3.1 MAUI Architecture
  • 2.3.2 Clone-Cloud Based Model
  • 2.3.3 Cuckoo Design
  • 2.3.4 MACS Architecture
  • 2.3.5 AHP and TOPSIS Design Technique
  • 2.3.6 Energy Aware Design for Workflows
  • 2.3.7 MCSOS Architecture
  • 2.3.8 Cloudlet
  • 2.3.9 Jade
  • 2.3.10 Phone2Cloud
  • 2.4 Conclusion
  • 2.5 Future Scope
  • 2.6 Acknowledgement
  • References
  • Chapter 3 Fog Computing for Intelligent Cloud-IoT System: Optimization of Fog Computing in Industry 4.0
  • 3.1 Introduction
  • 3.1.1 Industry 4.0
  • 3.1.2 Fog Computing
  • 3.1.3 Fog Nodes
  • 3.2 How Fog Computing with IIoT Brings Revolution
  • 3.2.1 Hierarchical Fog Computing Architecture
  • 3.2.2 Layered Fog Computing Architecture
  • 3.3 Applications of Fog Computing on Which Industries Rely
  • 3.3.1 In the Field of Agriculture
  • 3.3.2 In Healthcare Industry
  • 3.3.3 In Smart Cities
  • 3.3.4 In Education
  • 3.3.5 In Entertainment
  • 3.4 Data Analysis
  • 3.5 Illustration of Fog Computing and Application
  • 3.5.1 Figures
  • 3.6 Conclusion
  • 3.7 Future Scope/Acknowledgement
  • References.
  • Chapter 4 Machine Learning Integration in Agriculture Domain: Concepts and Applications
  • 4.1 Introduction
  • 4.2 Fog Computing in Agriculture
  • 4.2.1 Smart Farming
  • 4.3 Methodology
  • 4.3.1 Data Source
  • 4.3.2 Data Analysis and Pre-Processing
  • 4.3.3 Feature Extraction
  • 4.3.4 Model Selection
  • 4.3.5 Hyper-Parameter Tuning
  • 4.3.6 Train-Test Split
  • 4.4 Results and Discussion
  • 4.4.1 Modeling Algorithms
  • 4.5 Conclusion
  • 4.6 Future Scope
  • References
  • Chapter 5 Role of Intelligent IoT Applications in Fog Computing
  • 5.1 Introduction
  • 5.1.1 PaaS/SaaS Platforms Have Various Benefits That are Crucial to the Success of Many Small IoT Startup Businesses
  • 5.2 Cloud Service Model's Drawbacks
  • 5.3 Fog Computation
  • 5.3.1 Standardization
  • 5.3.2 Growing Use Cases for Fog Computing
  • 5.3.3 IoT Applications with Intelligence
  • 5.3.4 Graphics Processing Units
  • 5.4 Recompenses of FoG
  • 5.5 Limitation of Fog Computing
  • 5.6 Fog Computing with IoT
  • 5.6.1 Benefits of Fog Computing with IoT
  • 5.6.2 Challenges of Fog Computing with IoT
  • 5.7 Edge AI Embedded
  • 5.7.1 Key Software Characteristics in Fog Computing
  • 5.7.2 Fog Cluster Management
  • 5.7.3 Technology for Computing in the Fog
  • 5.7.4 Concentrating Intelligence
  • 5.7.5 Device-Driven Intelligence
  • 5.8 Network Intelligence Objectives
  • 5.9 Farming with Fog Computation (Case Study)
  • 5.10 Conclusion
  • References
  • Chapter 6 SaaS-Based Data Visualization Platform-A Study in COVID-19 Perspective
  • 6.1 Introduction
  • 6.1.1 Motivation and the Problem of Interest
  • 6.2 Summary of Objectives
  • 6.3 What is a Pandemic?
  • 6.4 COVID-19 and Information Gap
  • 6.5 Data Visualization and its Importance
  • 6.6 Data Management with Data Visualization
  • 6.7 What is Power BI?
  • 6.7.1 Data Collection &amp
  • Wrangling
  • 6.7.2 Data Description &amp
  • Source.
  • 6.7.3 Data Transformation
  • 6.8 Output Data
  • 6.9 Design &amp
  • Implementation
  • 6.9.1 Integration Design
  • 6.9.2 High-Level Process Flow
  • 6.9.3 Solution Flow
  • 6.10 Dashboard Development
  • 6.10.1 Landing Page
  • 6.10.2 Approach and Design
  • 6.10.3 Helpline Information
  • 6.10.3.1 Approach and Design
  • 6.10.4 Symptom Detection
  • 6.10.4.1 Approach and Design
  • 6.10.5 Testing Lab Information
  • 6.10.5.1 Approach and Design
  • 6.10.6 Hospital Information
  • 6.10.6.1 Approach and Design
  • 6.10.7 Oxygen Suppliers Information
  • 6.10.7.1 Approach and Design
  • 6.10.8 COVID Cases Information
  • 6.10.8.1 Approach and Design
  • 6.10.9 Vaccination Information
  • 6.10.9.1 Approach and Design
  • 6.10.10 Patients' Information
  • 6.10.10.1 Approach and Design
  • 6.11 Advantages and its Impact
  • 6.12 Conclusion and Future Scope
  • References
  • Chapter 7 A Complete Study on Machine Learning Algorithms for Medical Data Analysis
  • 7.1 Introduction
  • 7.1.1 Importance of Machine Learning Algorithms in Medical Data Analysis
  • 7.2 Pre-Processing Medical Data for Machine Learning
  • 7.3 Supervised Learning Algorithms for Medical Data Analysis
  • 7.3.1 Linear Regression Algorithm
  • 7.3.2 Logistic Regression Algorithm
  • 7.3.3 Decision Trees Algorithm
  • 7.3.3.1 Advantages of Decision Tree Algorithm
  • 7.3.3.2 Limitations of Decision Tree Algorithm
  • 7.3.4 Random Forest Algorithm
  • 7.3.4.1 Advantages of Random Forest Algorithm
  • 7.3.4.2 Limitations of Random Forest Algorithm
  • 7.3.4.3 Applications of Random Forest Algorithm in Medical Data Analysis
  • 7.3.5 Support Vector Machine Algorithm
  • 7.3.5.1 Advantages of SVM Algorithm
  • 7.3.5.2 Limitations of SVM Algorithm
  • 7.3.5.3 Applications of SVM Algorithm in Medical Data Analysis
  • 7.3.6 Naive Bayes Algorithm
  • 7.3.7 KNN (K-Nearest Neighbor Algorithm)
  • 7.3.7.1 Applications of K-NN Algorithm.
  • 7.3.8 Deep Learning Algorithm
  • 7.3.9 Deep Learning Application
  • 7.4 Unsupervised Learning Algorithms for Medical Data Analysis
  • 7.4.1 Clustering Algorithm
  • 7.4.2 Principal Component Analysis Algorithm
  • 7.4.3 Independent Component Analysis Algorithm
  • 7.4.4 Association Rule Mining Algorithm
  • 7.5 Applications of Machine-Learning Algorithms in Medical Data Analysis
  • 7.6 Limitations and Challenges of Machine Learning Algorithms in Medical Data Analysis
  • 7.7 Future Research Directions and Machine Learning Developments in the Realm of Medical Data Analysis
  • 7.8 Conclusion
  • References
  • Part II: Applications and Analytics
  • Chapter 8 Fog Computing in Healthcare: Application Taxonomy, Challenges and Opportunities
  • 8.1 Introduction
  • 8.2 Research Methodology
  • 8.3 Application Taxonomy in FC-Based Healthcare
  • 8.3.1 Diagnosis
  • 8.3.2 Monitoring
  • 8.3.3 Notification
  • 8.3.4 Zest of Applications of FC in Healthcare
  • 8.4 Challenges in FC-Based Healthcare
  • 8.4.1 QoS Optimization
  • 8.4.2 Patient Authentication and Access Control
  • 8.4.3 Data Processing
  • 8.4.4 Data Privacy Preservation
  • 8.4.5 Energy Efficiency
  • 8.5 Research Opportunities
  • 8.5.1 Research Opportunity in Computing
  • 8.5.2 Research Opportunity in Security
  • 8.5.3 Research Opportunity in Services
  • 8.5.4 Research Opportunity in Implementation
  • 8.6 Conclusion
  • References
  • Chapter 9 IoT-Driven Predictive Maintenance Approach in Industry 4.0: A Fiber Bragg Grating (FBG) Sensor Application
  • 9.1 Introduction
  • 9.2 Review of Related Research Articles
  • 9.2.1 Studies on FBG Sensors and Their Role in Industry 4.0
  • 9.2.1.1 Magnetostrictive Material
  • 9.2.1.2 Magneto-Optical (MO) Materials
  • 9.2.1.3 Magnetic Fluid (MF) Materials
  • 9.2.1.4 Magnetically Sensitive Materials and Their Application
  • 9.2.1.5 Optical Fiber Current Sensors.
  • 9.3 Research Gaps
  • 9.4 Emerging Research Directions
  • 9.5 The Broad Concept of FBG Sensor Applications in Industry 4.0
  • 9.6 Conclusion
  • References
  • Chapter 10 Fog Computing-Enabled Cancer Cell Detection System Using Convolution Neural Network in Internet of Medical Things
  • 10.1 Introduction
  • 10.2 Fog Computing: Approach of IoMT
  • 10.3 Relationship Between IoMT and Deep Neural Network
  • 10.4 Fog Computing Enabled CNN for Medical Imaging
  • 10.5 Algorithm Approach of Proposed Model
  • 10.6 Result and Analysis
  • 10.7 Conclusion
  • References
  • Chapter 11 Application of IoT in Smart Farming and Precision Farming: A Review
  • 11.1 Introduction
  • 11.2 Methodologies Used in Precision Agriculture
  • 11.3 Contribution of IoT in Agriculture
  • 11.4 IoT Enabled Smart Farming
  • 11.5 IoT Enabled Precision Farming
  • 11.6 Machine Learning Enable Precision Farming
  • 11.7 Application of Operational Research Method in Farming System
  • 11.8 Conclusion
  • 11.9 Future Scope
  • References
  • Chapter 12 Big IoT Data Analytics in Fog Computing
  • 12.1 Introduction
  • 12.2 Literature Review
  • 12.3 Motivation
  • 12.4 Fog Computing
  • 12.4.1 Fog Node
  • 12.4.2 Characteristics of Fog Computing
  • 12.4.3 Attributes of Fog Node
  • 12.4.4 Fog Computing Service Model
  • 12.4.5 Fog Computing Architecture
  • 12.4.6 Data Flow and Control Flow in Fog Architecture
  • 12.4.7 Fog Deployment Models
  • 12.5 Big Data
  • 12.5.1 What is Big Data?
  • 12.5.2 Source of Big Data
  • 12.5.3 Characteristic of Big Data
  • 12.6 Big Data Analytics Using Fog Computing
  • 12.7 Conclusion
  • References
  • Chapter 13 IOT-Based Patient Monitoring System in Real Time
  • 13.1 Introduction
  • 13.2 Components Used
  • 13.2.1 Node MCU
  • 13.2.2 Heart Rate/Pulse Sensor
  • 13.2.3 Temperature Sensor (LM35)
  • 13.3 IoT Platform
  • 13.3.1 ThingSpeak-IoT Platform Used in This Work.
  • 13.4 Proposed Method.

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