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by Satish Narayana Srirama, Rajkumar Buyya
Fog and Edge Computing
Cover
List of Contributors
Preface
Organization of the Book
Acknowledgments
Part I: Foundations
1 Internet of Things (IoT) and New Computing Paradigms
1.1 Introduction
1.2 Relevant Technologies
1.3 Fog and Edge Computing Completing the Cloud
1.4 Hierarchy of Fog and Edge Computing
1.5 Business Models
1.6 Opportunities and Challenges
1.7 Conclusions
References
2 Addressing the Challenges in Federating Edge Resources
2.1 Introduction
2.2 The Networking Challenge
2.3 The Management Challenge
2.4 Miscellaneous Challenges
2.5 Conclusions
References
3 Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges
3.1 Introduction
3.2 Methodology
3.3 Integrated C2F2T Literature by Modeling Technique
3.4 Integrated C2F2T Literature by Use‐Case Scenarios
3.5 Integrated C2F2T Literature by Metrics
3.6 Future Research Directions
3.7 Conclusions
Acknowledgments
References
4 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds
4.1 Introduction
4.2 Background
4.3 Network Slicing in 5G
4.4 Network Slicing in Software‐Defined Clouds
4.5 Network Slicing Management in Edge and Fog
4.6 Future Research Directions
4.7 Conclusions
Acknowledgments
References
5 Optimization Problems in Fog and Edge Computing
5.1 Introduction
5.2 Background / Related Work
5.3 Preliminaries
5.4 The Case for Optimization in Fog Computing
5.5 Formal Modeling Framework for Fog Computing
5.6 Metrics
5.7 Optimization Opportunities along the Fog Architecture
5.8 Optimization Opportunities along the Service Life Cycle
5.9 Toward a Taxonomy of Optimization Problems in Fog Computing
5.10 Optimization Techniques
5.11 Future Research Directions
5.12 Conclusions
Acknowledgments
References
Part II: Middlewares
6 Middleware for Fog and Edge Computing: Design Issues
6.1 Introduction
6.2 Need for Fog and Edge Computing Middleware
6.3 Design Goals
6.4 State‐of‐the‐Art Middleware Infrastructures
6.5 System Model
6.6 Proposed Architecture
6.7 Case Study Example
6.8 Future Research Directions
6.9 Conclusions
References
7 A Lightweight Container Middleware for Edge Cloud Architectures
7.1 Introduction
7.2 Background/Related Work
7.3 Clusters for Lightweight Edge Clouds
7.4 Architecture Management – Storage and Orchestration
7.5 IoT Integration
7.6 Security Management for Edge Cloud Architectures
7.7 Future Research Directions
7.8 Conclusions
References
8 Data Management in Fog Computing
8.1 Introduction
8.2 Background
8.3 Fog Data Management
8.4 Future Research and Direction
8.5 Conclusions
References
9 Predictive Analysis to Support Fog Application Deployment
9.1 Introduction
9.2 Motivating Example: Smart Building
9.3 Predictive Analysis with FogTorchΠ
9.4 Motivating Example (continued)
9.5 Related Work
9.6 Future Research Directions
9.7 Conclusions
References
10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems
10.1 Introduction
10.2 Background
10.3 Survey of ML Techniques for Defending IoT Devices
10.4 Machine Learning in Fog Computing
10.5 Future Research Directions
10.6 Conclusions
References
Part III: Applications and Issues
11 Fog Computing Realization for Big Data Analytics
11.1 Introduction
11.2 Big Data Analytics
11.3 Data Analytics in the Fog
11.4 Prototypes and Evaluation
11.5 Case Studies
11.6 Related Work
11.7 Future Research Directions
11.8 Conclusions
References
12 Exploiting Fog Computing in Health Monitoring
12.1 Introduction
12.2 An Architecture of a Health Monitoring IoT‐Based System with Fog Computing
12.3 Fog Computing Services in Smart E‐Health Gateways
12.4 System Implementation
12.5 Case Studies, Experimental Results, and Evaluation
12.6 Discussion of Connected Components
12.7 Related Applications in Fog Computing
12.8 Future Research Directions
12.9 Conclusions
References
13 Smart Surveillance Video Stream Processing at the Edge for Real‐Time Human Objects Tracking
13.1 Introduction
13.2 Human Object Detection
13.3 Object Tracking
13.4 Lightweight Human Detection
13.5 Case Study
13.6 Future Research Directions
13.7 Conclusions
References
14 Fog Computing Model for Evolving Smart Transportation Applications
14.1 Introduction
14.2 Data‐Driven Intelligent Transportation Systems
14.3 Mission‐Critical Computing Requirements of Smart Transportation Applications
14.4 Fog Computing for Smart Transportation Applications
14.5 Case Study: Intelligent Traffic Lights Management (ITLM) System
14.6 Fog Orchestration Challenges and Future Directions
14.7 Future Research Directions
14.8 Conclusions
References
15 Testing Perspectives of Fog‐Based IoT Applications
15.1 Introduction
15.2 Background
15.3 Testing Perspectives
15.4 Future Research Directions
15.5 Conclusions
References
16 Legal Aspects of Operating IoT Applications in the Fog
16.1 Introduction
16.2 Related Work
16.3 Classification of Fog/Edge/IoT Applications
16.4 Restrictions of the GDPR Affecting Cloud, Fog, and IoT Applications
16.5 Data Protection by Design Principles
16.6 Future Research Directions
16.7 Conclusions
Acknowledgment
References
17 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit
17.1 Introduction
17.2 iFogSim Simulator and Its Components
17.3 Installation of iFogSim
17.4 Building Simulation with iFogSim
17.5 Example Scenarios
17.6 Simulation of a Placement Policy
17.7 A Case Study in Smart Healthcare
17.8 Conclusions
References
Index
End User License Agreement
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Wiley Series
Table of Contents
Cover
List of Contributors
Preface
Organization of the Book
Acknowledgments
Part I: Foundations
1 Internet of Things (IoT) and New Computing Paradigms
1.1 Introduction
1.2 Relevant Technologies
1.3 Fog and Edge Computing Completing the Cloud
1.4 Hierarchy of Fog and Edge Computing
1.5 Business Models
1.6 Opportunities and Challenges
1.7 Conclusions
References
2 Addressing the Challenges in Federating Edge Resources
2.1 Introduction
2.2 The Networking Challenge
2.3 The Management Challenge
2.4 Miscellaneous Challenges
2.5 Conclusions
References
3 Integrating IoT + Fog + Cloud Infrastructures: System Modeling and Research Challenges
3.1 Introduction
3.2 Methodology
3.3 Integrated C2F2T Literature by Modeling Technique
3.4 Integrated C2F2T Literature by Use‐Case Scenarios
3.5 Integrated C2F2T Literature by Metrics
3.6 Future Research Directions
3.7 Conclusions
Acknowledgments
References
4 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds
4.1 Introduction
4.2 Background
4.3 Network Slicing in 5G
4.4 Network Slicing in Software‐Defined Clouds
4.5 Network Slicing Management in Edge and Fog
4.6 Future Research Directions
4.7 Conclusions
Acknowledgments
References
5 Optimization Problems in Fog and Edge Computing
5.1 Introduction
5.2 Background / Related Work
5.3 Preliminaries
5.4 The Case for Optimization in Fog Computing
5.5 Formal Modeling Framework for Fog Computing
5.6 Metrics
5.7 Optimization Opportunities along the Fog Architecture
5.8 Optimization Opportunities along the Service Life Cycle
5.9 Toward a Taxonomy of Optimization Problems in Fog Computing
5.10 Optimization Techniques
5.11 Future Research Directions
5.12 Conclusions
Acknowledgments
References
Part II: Middlewares
6 Middleware for Fog and Edge Computing: Design Issues
6.1 Introduction
6.2 Need for Fog and Edge Computing Middleware
6.3 Design Goals
6.4 State‐of‐the‐Art Middleware Infrastructures
6.5 System Model
6.6 Proposed Architecture
6.7 Case Study Example
6.8 Future Research Directions
6.9 Conclusions
References
7 A Lightweight Container Middleware for Edge Cloud Architectures
7.1 Introduction
7.2 Background/Related Work
7.3 Clusters for Lightweight Edge Clouds
7.4 Architecture Management – Storage and Orchestration
7.5 IoT Integration
7.6 Security Management for Edge Cloud Architectures
7.7 Future Research Directions
7.8 Conclusions
References
8 Data Management in Fog Computing
8.1 Introduction
8.2 Background
8.3 Fog Data Management
8.4 Future Research and Direction
8.5 Conclusions
References
9 Predictive Analysis to Support Fog Application Deployment
9.1 Introduction
9.2 Motivating Example: Smart Building
9.3 Predictive Analysis with FogTorchΠ
9.4 Motivating Example (continued)
9.5 Related Work
9.6 Future Research Directions
9.7 Conclusions
References
10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems
10.1 Introduction
10.2 Background
10.3 Survey of ML Techniques for Defending IoT Devices
10.4 Machine Learning in Fog Computing
10.5 Future Research Directions
10.6 Conclusions
References
Part III: Applications and Issues
11 Fog Computing Realization for Big Data Analytics
11.1 Introduction
11.2 Big Data Analytics
11.3 Data Analytics in the Fog
11.4 Prototypes and Evaluation
11.5 Case Studies
11.6 Related Work
11.7 Future Research Directions
11.8 Conclusions
References
12 Exploiting Fog Computing in Health Monitoring
12.1 Introduction
12.2 An Architecture of a Health Monitoring IoT‐Based System with Fog Computing
12.3 Fog Computing Services in Smart E‐Health Gateways
12.4 System Implementation
12.5 Case Studies, Experimental Results, and Evaluation
12.6 Discussion of Connected Components
12.7 Related Applications in Fog Computing
12.8 Future Research Directions
12.9 Conclusions
References
13 Smart Surveillance Video Stream Processing at the Edge for Real‐Time Human Objects Tracking
13.1 Introduction
13.2 Human Object Detection
13.3 Object Tracking
13.4 Lightweight Human Detection
13.5 Case Study
13.6 Future Research Directions
13.7 Conclusions
References
14 Fog Computing Model for Evolving Smart Transportation Applications
14.1 Introduction
14.2 Data‐Driven Intelligent Transportation Systems
14.3 Mission‐Critical Computing Requirements of Smart Transportation Applications
14.4 Fog Computing for Smart Transportation Applications
14.5 Case Study: Intelligent Traffic Lights Management (ITLM) System
14.6 Fog Orchestration Challenges and Future Directions
14.7 Future Research Directions
14.8 Conclusions
References
15 Testing Perspectives of Fog‐Based IoT Applications
15.1 Introduction
15.2 Background
15.3 Testing Perspectives
15.4 Future Research Directions
15.5 Conclusions
References
16 Legal Aspects of Operating IoT Applications in the Fog
16.1 Introduction
16.2 Related Work
16.3 Classification of Fog/Edge/IoT Applications
16.4 Restrictions of the GDPR Affecting Cloud, Fog, and IoT Applications
16.5 Data Protection by Design Principles
16.6 Future Research Directions
16.7 Conclusions
Acknowledgment
References
17 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit
17.1 Introduction
17.2 iFogSim Simulator and Its Components
17.3 Installation of iFogSim
17.4 Building Simulation with iFogSim
17.5 Example Scenarios
17.6 Simulation of a Placement Policy
17.7 A Case Study in Smart Healthcare
17.8 Conclusions
References
Index
End User License Agreement
List of Tables
Chapter 2
Table 2.1 Network challenges, their causes and potential solutions in federating...
Table 2.2 Management challenges, the need for addressing them, and potential sol...
Chapter 3
Table 3.1 Summary of systematic search results.
Table 3.2 Articles about modelling of IoT, fog, and cloud integration.
Table 3.3 Scenarios presented in articles.
Table 3.4 Metrics observed in articles.
Chapter 4
Table 4.1 Acronyms and abbreviations.
Table 4.2 Network‐aware virtual machines management.
Table 4.3 Virtual machine migration planning.
Table 4.4 Virtual network functions management projects.
Chapter 5
Table 5.1 Notation overview.
Table 5.2 Classification of the work of Do et al. [21] according to the presente...
Table 5.3 Classification of the work of Sardellitti et al. [22] according to the...
Table 5.4 Classification of the work of Mushunuri et al. [23] according to the p...
Chapter 6
Table 6.1 Middleware features in fog and edge architectures.
Chapter 7
Table 7.1 Speed and power consumption of the Raspberry Pi cluster. Adapted from ...
Table 7.2 Approximate costs of the Raspberry Pi cluster.
Table 7.3 Time comparison – listing the overall, the mean, and the maximal time ...
Table 7.4 Comparison of the power consumption while idling and under load.
Table 7.5 Power consumption of the Raspberry Pi cluster while idling and under l...
Chapter 9
Table 9.1 Hardware specification for different VM types.
Table 9.2 QoS profiles associated to the communication links.
Table 9.3 Eligible deployments generated by FogTorchΠ for Q1 and Q2.
14
Table 9.4 Result of FogTorchΠ for the VR Game.
Chapter 10
Table 10.1 OWASP IoT attack surface areas.
Table 10.2 Categorization of ML solutions for IOT security.
Table 10.3 Categorization of ML solutions for outlier detection.
Table 10.4 Where data should be processed.
Table 10.5 ML Use cases for fog computing.
Table 10.6 Machine‐learning algorithms at different fog layers.
Chapter 11
Table 11.1 Data analytics using a fog‐engine and the cloud.
Table 11.2 Comparison of various schemes with and without a fog‐engine, where ba...
Table 11.3 List of IOT solutions from five major cloud providers.
Chapter 12
Table 12.1 Energy consumption of the sensor node with and without running AES.
Chapter 14
Table 14.1 Application use cases for data‐driven intelligent transportation appl...
Table 14.2 Performance comparison of cloud and fog computing models in smart tra...
Table 14.3 Computing requirements of intelligent traffic light management (ITLM)...
Chapter 15
Table 15.1 Outline of the work done to test smart homes.
Table 15.2 Outline of the work done to test smart health.
Table 15.3 Outline of the work done to test smart transport.
Table 15.4 Summary of limitations and research directions for smart home.
Table 15.5 Summary of limitations and research directions for smart health.
Table 15.6 Summary of limitations and research directions for smart transport.
List of Illustrations
Chapter 1
Figure 1.1 IoT applications and environments with supporting computing p...
Figure 1.2 FEC nodes supports five basic mechanisms—storage, compute, ac...
Figure 1.3 Hierarchy of fog and edge computing.
Chapter 2
Figure 2.1 Networking and management challenges in federating edge resou...
Figure 2.2 Fog computing with SDN as the network orchestrator.
Figure 2.3 Resource and modeling challenges in federating edge resources...
Chapter 3
Figure 3.1 Integration of IoT devices with fog and cloud computing.
Figure 3.2 Systematic review steps. Adapted from [8].
Figure 3.3 The most approaches used to model the integration among cloud...
Equation 3.1 Scheme proposed in [10].
Equation 3.2 Energy consumption model presented in [17].
Equation 3.3 Billing model presented in [15].
Equation 3.4 Calculate of energy consumption between IoT and c...
Equation 3.5 Calculate of energy consumption between IoT and f...
Equation 3.6 Reliability equation presented in [16].
Figure 3.4 Petri Net model proposed in [26]. © Elsevier. Reproduced with...
Petri Net model presented in [29]. © Elsevier. Reproduced with the pe...
Equation 3.7 Objective function used in ILP model presented in [28].
Figure 3.6 Markov model presented in [21]. Adapted from [21].
Figure 3.7Figure 3.7 Fuzz‐ontology model proposed in [13]. © Elsevier. Rep...
Chapter 4
Figure 4.1 Generic 5G slicing framework.
Figure 4.2 Taxonomy of network‐aware VM/VNF Management in software‐defin...
Chapter 5
Figure 5.1 Number of (a) papers and (b) citations in fog computing.
Figure 5.2 Number of (a) papers and (b) citations about optimization in ...
Figure 5.3 Three‐layer model of fog computing.
Figure 5.4 Total execution time of an example computation offloading sce...
Chapter 6
Figure 6.1 Fog and edge computing devices.
Figure 6.2 Fog and edge computing architecture.
Chapter 7
Figure 7.1 Edge cloud architecture.
Figure 7.2 Simplified container orchestration plan for the ski resort ca...
Figure 7.3 Overall orchestration flow.
Figure 7.4 Blockchain‐based IoT orchestration and security management.
Figure 7.5
Provenance model
. Adapted from W3C. “PROV Model Primer,” Ap...
Figure 7.6 Blockchain‐based tracking of an orchestration plan.
Figure 7.7 Architecture of the blockchain integration.
Chapter 8
Figure 8.1 Structure of data management in fog computing.
Figure 8.2 Basic data management diagram in fog computing.
Figure 8.3 Data life cycle in fog computing.
Figure 8.4 A simple sequence diagram of an e‐health application.
Figure 8.5 Proposed architecture.
Figure 8.6 Interaction of the main process in proposed architecture.
Chapter 9
Figure 9.1 Fog application of the motivating example.
1
Figure 9.2 Fog infrastructure of the motivating example.
4
Figure 9.3 Bird's‐eye view of FogTorchΠ.
Figure 9.4 Pseudocode of the exhaustive search algorithm.
Figure 9.5 Search space to find eligible deployments of
A
to
I
.
Figure 9.6 Pseudocode for the backtracking search.
Figure 9.7 Pseudocode of the Monte Carlo simulation in FogTorchΠ.
Figure 9.8 Bernoulli sampling function example.
Figure 9.9 Results for Q1(a) and Q1(b).
15
Figure 9.10 Results
16
for Q2.
Figure 9.11 VR Game application.
Figure 9.12 VR Game infrastructure.
18
Chapter 10
Figure 10.1 IoT system with threats and protections annotated.
Figure 10.2 Privacy vulnerabilities in IoT
Figure 10.3 Privacy threats with entities and information flows in IoT. ...
Figure 10.4 DDoS attack.
Figure 10.5 Ensemble machine learning.
Figure 10.6 Fog computing security at multiple layers.
Chapter 11
Figure 11.1 Typical data analytics flow.
Figure 11.2 Deployment of FE in a typical cloud‐based computing system. ...
Figure 11.3 Data analytics using a fog‐engine before offloading to the c...
Figure 11.4 (a) General architecture of fog‐engine; (b) A detailed archi...
Figure 11.5 Various configurations of fog‐engine: (a) as a broker; (b) a...
Figure 11.6 Fog‐engine collects data and communicates with the cloud.
Figure 11.7 Data transmission time between FEs and cloud for various pac...
Figure 11.8 The deployment of fog‐engines in the system pipeline.
Figure 11.9 Architecture of the smart nutrition monitoring system.
Figure 11.10 Prototype of the smart nutrition monitoring system.
Chapter 12
Figure 12.1 Architecture of remote real‐time health‐monitoring IoT syste...
Figure 12.2 Fog services in a smart gateway.
Figure 12.3 Acceleration and angular velocity changes during a fall.
Figure 12.4 Multilevel threshold based fall detection algorithm.
Figure 12.5 SVM of 3‐D acceleration and SVM of 3‐D angular velocity.
Figure 12.6 Real‐time ECG monitoring and preprocess ECG data at fog.
Figure 12.7 RMS of HRV features in different window lengths and differen...
Figure 12.8 ROC curves of
No pain
and
Pain
classification.
Chapter 13
Figure 13.1 Edge‐fog‐cloud‐based hierarchy smart surveillance architectu...
Figure 13.2 Haar‐like features: (a) two rectangular features; (b) three ...
Figure 13.3 (a) Histogram of oriented gradients; (b) representation of H...
Figure 13.4 An example of multi‐detection for a single object.
Figure 13.5 Object tracking methods.
Figure 13.6 Different motion constrains.
Figure 13.7 KCF tracking process.
Figure 13.8 Convolutional filter vs. separable depthwise convolutional f...
Figure 13.9 Results of Haar cascaded human detection.
Figure 13.10 Performance of HOG+SVM algorithm.
Figure 13.11 Example of light version CNN for human object detection.
Figure 13.12 An example of multi‐object tracking.
Figure 13.13 An example of object tracker phase in and out.
Figure 13.14 An example of re‐tracking after target lost.
Chapter 14
Figure 14.1 Key components of a data‐driven ITS [12].
Figure 14.2 Topology of FOG computing paradigm for smart transportation ...
Figure 14.3 Data/Control Flow among FCNs in Layer 2
Figure 14.4 An orchestration scenario for intelligent traffic management...
Figure 14.5 Functional elements of a typical fog orchestrator showing th...
Figure 14.6 The conceptual framework for BD
2
A and optimization of ITLM b...
Chapter 16
Figure 16.1 Data management in fog environments.
Chapter 17
Figure 17.1 Interactions among IoT‐enabled systems, fog and cloud compu...
Figure 17.2 High‐level view of interactions among iFogSim components.
Figure 17.3 Master–worker application model.
Figure 17.4 Sequential unidirectional dataflow application model.
Figure 17.5 Network topology for the placement policy.
Figure 17.6 Application model for the placement policy.
Figure 17.7 Flowchart of the application placement policy.
Figure 17.8 Fog environment for IoT‐enabled healthcare case study.
Figure 17.9 Application model for IoT‐enabled healthcare case study.
Guide
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