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

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