دانلود کتاب Bio-Inspired Optimization in Fog and Edge Computing Environments: Principles, Algorithms, and Systems

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Cover Half Title Title Page Copyright Page Table of Contents Preface Editors Contributors Chapter 1 Introduction to Optimization in Fog Computing 1.1 Introduction 1.2 Fog Computing Versus Cloud Computing 1.2.1 Benefits of Fog Computing 1.2.2 Edge Computing 1.2.3 Fog Computing Over 5G 1.3 Fog Computing System and Examples of Use 1.4 Optimization in Fog 1.5 Conclusions References Chapter 2 Open Issues and Challenges in Fog and Edge 2.1 Introduction 2.2 Issues With Fog 2.2.1 Open Issues 2.2.2 Optimization Issues 2.3 Optimization in Various Layers of Fog Architecture 2.4 Security Issues in Fog References Chapter 3 Future Challenges in Fog and Edge Computing Applications 3.1 Introduction 3.2 Related Works 3.3 CC, Fog and Edge Computing 3.3.1 Fog Computing and Related Computing Paradigms 3.3.1.1 Differences Between Fog Computing and CC 3.3.1.2 Differences Between Fog and Edge Computing 3.4 System Architecture 3.5 Challenges and Solutions for Edge and Fog Computing 3.5.1 Scale 3.5.2 Control and Management 3.5.3 Data Accumulation 3.5.4 Backup 3.5.5 Security and Accessibility 3.5.6 Latency 3.5.7 Distributed Computing 3.5.8 Network Bandwidth 3.6 Conclusions References Chapter 4 Geographic Information Systems-Based Modeling of Health Care Data and Its Optimization Using Various Approaches 4.1 Introduction 4.2 Various Material and Their Methodologies 4.2.1 Benefits of GIS 4.2.2 Various Classification of Health Data in GIS 4.2.3 Visualization 4.3 Overlay and Analysis in GIS 4.3.1 Buffer Zone Analysis in GIS 4.3.2 GIS-Based Network Analysis 4.3.3 Statistical Analysis in GIS 4.3.4 Query in GIS 4.3.5 Web-Based GIS 4.4 GIS and Its Applications in Health Sciences 4.4.1 GIS and Epidemiology 4.4.2 Routes to Provide Services 4.4.3 Health Systems in Hospitals 4.4.4 Social Services 4.4.5 Customer Service 4.4.6 Site Selection 4.4.7 Managed Health Care 4.4.8 Resource Management 4.5 Requirements for Health Care Services 4.5.1 Analysis of Health Care Facilities 4.5.2 Access Measurement 4.5.3 Geographic Variations in Health Care 4.5.4 Health Care Delivery and GIS 4.5.5 Health Services Locations 4.6 Spatial Decision Support System 4.6.1 GIS in Homeland Security 4.6.2 GIS in Indian Health Care 4.7 Conclusions 4.8 Future Directions References Chapter 5 Application of Optimization Techniques in Cloud Resource Management 5.1 Introduction 5.2 Related Works 5.3 Motivations for the Work 5.4 Optimization Techniques 5.4.1 Classifications of Resource Management Techniques in Cloud Computing 5.5 Resource Management Using Optimization Techniques 5.5.1 Resource Management Techniques Taxonomy Using Performance Metrics 5.5.1.1 Energy Aware Resource Management 5.5.1.2 SLA-Based Resource Management 5.5.1.3 Fitness Value Aware Resource Management 5.5.1.4 Time Aware Resource Management 5.5.2 Network Parameters Aware Resource Management 5.5.3 Integration of Cloud Deployment Model With Service Model Using Optimization Mechanism 5.5.3.1 Layer 1: Deployment Model 5.5.3.2 Layer 2: Service Model 5.5.3.3 Layer 3: SLA Management Policy Implementation 5.5.3.4 Cloud Computing Model for Resource Management 5.5.3.5 Implementation Procedure of Resource Management Policy Using Simulation Process 5.6 Performance Evaluation and Analysis 5.7 Conclusions 5.7.1 Future Directions References Chapter 6 Use of Fog Computing in Health Care 6.1 Introduction 6.2 Evolution of the Industry to Healthcare 4.0 6.3 Fog Computing in Healthcare 4.0 6.4 Benefits of Fog Computing in Health Care 6.5 Challenges in Fog Computing 6.6 Differences Between Cloud, Fog, and Edge Computing 6.7 Applications of Fog Computing 6.7.1 Fog Computing-Based IoT for Health Monitoring Systems 6.7.1.1 Experimental Analysis 6.7.2 Data Processing and Analytics in Fog Computing for Healthcare 4.0 6.7.2.1 Need for Data Processing and Analysis 6.7.2.2 Case Study 6.7.3 Fog-IoT Environment in Smart Health Care: A Case Study for Student Stress Monitoring 6.7.3.1 Proposed Methodology: A Case Study of Fog Computing in Student Stress Monitoring 6.8 Future of Fog Computing in the Health Care Sector 6.9 Conclusions References Chapter 7 Fog Computing for Agriculture Applications and Its Issues 7.1 Introduction 7.2 Literature Review 7.3 Smart Agriculture 7.4 Cloud Computing (CC) 7.5 Fog Computing 7.5.1 Features of Fog Computing 7.5.2 Architecture of Fog Computing 7.5.2.1 IoT Devices 7.5.2.2 Fog Layer 7.5.2.3 Cloud Layer 7.5.3 Layers of Fog Computing Architecture 7.5.3.1 Physical and Virtualization Layer 7.5.3.2 Monitoring Layer 7.5.3.3 Preprocessing Layer 7.5.3.4 Temporary Storage 7.5.3.5 Security Layer 7.5.3.6 Transport Layer 7.5.4 Fog Data Flow 7.6 Fog Computing With the IoT 7.7 Fog–IoT Based Agricultural Applications 7.7.1 PA 7.7.2 Smart Crop Disease Prediction 7.7.3 Fog Computing in Large Farms 7.8 Issues in Applications of Fog 7.8.1 Challenges in the Device and Network 7.8.2 Computing Difficulties 7.8.3 Privacy Issues 7.8.4 Administrative Difficulties 7.9 Connectivity of Fog Elements to Cloud 7.10 Conclusions References Chapter 8 Fog Computing and Vehicular Networks for Smart Traffic Control: Fog Computing-Based Cognitive Analytics Model ... 8.1 Introduction 8.2 Related Work 8.2.1 Intelligent Transport System 8.3 Proposed Cognitive Model for Smart Traffic Control 8.3.1 Phase 1: Deployment of Static Sensors at Highest Traffic Density Locations (IoT Layer) 8.3.2 Phase 2: ETL Process for Sensed Attributes of Sensors in the Fog Layer 8.3.3 Phase 3: Regional Traffic Geometric Constructs, Inference Rule and Knowledge Base Management in the Cloud Layer 8.4 Conclusions References Chapter 9 Virtualization Concepts and Industry Standards in Cloud Computing 9.1 Introduction 9.2 How Does Virtualization Work? 9.3 Virtualization Helps Applications: Hardware Independence 9.3.1 Compute Virtualization 9.3.2 Storage Virtualization 9.3.3 Network Virtualization 9.3.4 Desktop Virtualization 9.3.5 Application Virtualization 9.4 VMware 9.5 VSphere 9.6 VMotion 9.7 VCenter 9.8 Hardware and Software Separation Using Virtualization 9.9 Comparison of Before and After Virtualization 9.10 Virtualizing X86 Hardware 9.11 Techniques to Virtualize X86 Hardware 9.11.1 Full Virtualization 9.11.2 Paravirtualization 9.11.3 Hardware-Assisted Virtualization Conclusions References Chapter 10 Optimized Cloud Storage Data Analysis Using the Machine Learning Model 10.1 Introduction 10.2 Motivation for This Work 10.3 Related Work 10.4 Proposed Framework 10.4.1 Cloud Storage and Data 10.4.2 ML Model and Analysis 10.4.3 Optimization and ML 10.5 Performance Evaluation and Analysis 10.5.1 Scenario 1: Without Nature-Inspired Optimization 10.5.2 Scenario 2: With Nature-Inspired Optimization 10.6 Conclusion and Future Works References Chapter 11 Resource Management in Fog Computing Environment Using Optimal Fog Network Topology 11.1 Introduction 11.2 Background and Related Work 11.2.1 Simulation Setup 11.2.2 High-Level Architecture of Resource Management System Using Optimal Fog Network 11.3 Resource Management in Fog Computing Environment 11.3.1 Fog Computing Topology Nodes and Configuration Parameters 11.3.1.1 Level 0 Fog Node 11.3.1.2 Level 1 Fog Node 11.3.1.3 Level 2 Fog Node 11.3.1.4 Level 3 Fog Node 11.3.1.5 Level 4 Fog Node 11.4 Simulation of Fog Computing Environment 11.5 Results 11.5.1 Fog Network Physical Topology of the Simulation 11.6 Performance Evaluation and Analysis 11.7 Conclusions 11.7.1 Future Directions References Chapter 12 Applications of Fog in Healthcare Services 12.1 Introduction to Fog Computing 12.2 Characteristics of Fog Computing 12.3 Fog Computing Architecture 12.4 Working of Fog Computing 12.5 Literature Review 12.5.1 Literature Review On Fog Computing in Healthcare Systems 12.5.2 Literature Review Related to Frameworks and Models in Healthcare Systems Using Fog Computing 12.6 Edge and Fog Computing Comparisons 12.7 Limitations and Challenges in Fog Computing 12.7.1 Control of Access 12.7.2 Authentication 12.7.3 Security and Privacy Issues 12.7.4 Fault Tolerance 12.8 Conclusion and Future Works References Chapter 13 Roles and Future of the Internet of Things-Based Smart Health Care Models 13.1 Introduction 13.2 Digital Health Care: Use of ML and Cloud Computing Technologies 13.2.1 ML 13.2.2 Cloud in Health Care 13.2.3 Usage of ML and Cloud Computing Technologies in Smart Health Care 13.3 Health Care and IoT Technologies 13.3.1 Identification of IoT Devices 13.3.2 Communication Technology 13.3.2.1 Radio Frequency Identification 13.3.2.2 Near-Field Communication 13.3.2.3 Bluetooth 13.3.2.4 Wi-Fi 13.3.2.5 Zigbee 13.4 Services and Applications of HIoT 13.4.1 Services 13.4.1.1 Mobile IoT 13.4.1.2 Wearable Devices 13.4.1.3 Community-Based Health Care Services 13.4.1.4 Blockchain 13.4.1.5 Adverse Drug Reaction 13.4.1.6 Child Health Information 13.4.1.7 Cognitive Computing 13.4.2 Applications of HIoT 13.4.2.1 ECG Monitoring 13.4.2.2 BP Monitoring 13.4.2.3 Glucose Level Monitoring 13.4.2.4 Mood Monitoring 13.4.2.5 Oxygen Saturation Monitoring 13.4.2.6 Asthma Monitoring 13.4.2.7 Medication Management 13.5 Challenges 13.6 Conclusion References Index