A new era of complexity science is emerging, in which nature- and bio-inspired principles are being applied to provide solutions. At the same time, the complexity of systems is increasing due to such models like the Internet of Things (IoT) and fog computing. Will complexity science, applying the principles of nature, be able to tackle the challenges posed by highly complex networked systems?
Bio-Inspired Optimization in Fog and Edge Computing: Principles, Algorithms, and Systems is an attempt to answer this question. It presents innovative, bio-inspired solutions for fog and edge computing and highlights the role of machine learning and informatics. Nature- or biological-inspired techniques are successful tools to understand and analyze a collective behavior. As this book demonstrates, algorithms, and mechanisms of self-organization of complex natural systems have been used to solve optimization problems, particularly in complex systems that are adaptive, ever-evolving, and distributed in nature.
The chapters look at ways of enhancingto enhance the performance of fog networks in real-world applications using nature-based optimization techniques. They discuss challenges and provide solutions to the concerns of security, privacy, and power consumption in cloud data center nodes and fog computing networks. The book also examines how:
- The existing fog and edge architecture is used to provide solutions to future challenges.
- A geographical information system (GIS) can be used with fog computing to help users in an urban region access prime healthcare.
- An optimization framework helps in cloud resource management.
- Fog computing can improve the quality, quantity, long-term viability, and cost-effectiveness in agricultural production.
- Virtualization can support fog computing, increase resources to be allocated, and be applied to different network layers.
- The combination of fog computing and IoT or cloud computing can help healthcare workers predict and analyze diseases in patients.
Author(s): Punit Gupta, Dinesh Kumar Saini, Pradeep Rawat, Kashif Zia
Publisher: CRC Press/Auerbach
Year: 2023
Language: English
Pages: 568
City: Boca Raton
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
