Fog and Edge Computing

Principles and Paradigms
 
 
Standards Information Network (Verlag)
  • 1. Auflage
  • |
  • erschienen am 31. Dezember 2018
  • |
  • 512 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-52501-1 (ISBN)
 
A comprehensive guide to Fog and Edge applications, architectures, and technologies Recent years have seen the explosive growth of the Internet of Things (IoT): the internet-connected network of devices that includes everything from personal electronics and home appliances to automobiles and industrial machinery. Responding to the ever-increasing bandwidth demands of the IoT, Fog and Edge computing concepts have developed to collect, analyze, and process data more efficiently than traditional cloud architecture. Fog and Edge Computing: Principles and Paradigms provides a comprehensive overview of the state-of-the-art applications and architectures driving this dynamic field of computing while highlighting potential research directions and emerging technologies. Exploring topics such as developing scalable architectures, moving from closed systems to open systems, and ethical issues rising from data sensing, this timely book addresses both the challenges and opportunities that Fog and Edge computing presents. Contributions from leading IoT experts discuss federating Edge resources, middleware design issues, data management and predictive analysis, smart transportation and surveillance applications, and more. A coordinated and integrated presentation of topics helps readers gain thorough knowledge of the foundations, applications, and issues that are central to Fog and Edge computing. This valuable resource: * Provides insights on transitioning from current Cloud-centric and 4G/5G wireless environments to Fog Computing * Examines methods to optimize virtualized, pooled, and shared resources * Identifies potential technical challenges and offers suggestions for possible solutions * Discusses major components of Fog and Edge computing architectures such as middleware, interaction protocols, and autonomic management * Includes access to a website portal for advanced online resources Fog and Edge Computing: Principles and Paradigms is an essential source of up-to-date information for systems architects, developers, researchers, and advanced undergraduate and graduate students in fields of computer science and engineering.
weitere Ausgaben werden ermittelt
Rajkumar Buyya, PhD, is Redmond Barry Distinguished Professor and Director of the Cloud Computing and Distributed Systems Laboratory, University of Melbourne, Australia and founding CEO of Manjrasoft. Dr. Buyya is author of several works including Mastering Cloud Computing and Editor-in-Chief of Wiley Software: Practice and Experience Journal.

Satish Narayana Srirama, PhD, is a Research Professor and head of the Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Estonia. He is editor of Wiley Software: Practice and Experience Journal and has co-authored over 120 scientific publications.
  • Cover
  • Title Page
  • Copyright
  • Contents
  • List of Contributors
  • Preface
  • Acknowledgments
  • Part I Foundations
  • Chapter 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
  • 1.3.1 Advantages of FEC: SCALE
  • 1.3.1.1 Security
  • 1.3.1.2 Cognition
  • 1.3.1.3 Agility
  • 1.3.1.4 Latency
  • 1.3.1.5 Efficiency
  • 1.3.2 How FEC Achieves These Advantages: SCANC
  • 1.3.2.1 Storage
  • 1.3.2.2 Compute
  • 1.3.2.3 Acceleration
  • 1.3.2.4 Networking
  • 1.3.2.5 Control
  • 1.4 Hierarchy of Fog and Edge Computing
  • 1.4.1 Inner-Edge
  • 1.4.2 Middle-Edge
  • 1.4.2.1 Local Area Network
  • 1.4.2.2 Cellular Network
  • 1.4.3 Outer-Edge
  • 1.4.3.1 Constraint Devices
  • 1.4.3.2 Integrated Devices
  • 1.4.3.3 IP Gateway Devices
  • 1.5 Business Models
  • 1.5.1 X as a Service
  • 1.5.2 Support Service
  • 1.5.3 Application Service
  • 1.6 Opportunities and Challenges
  • 1.6.1 Out-of-Box Experience
  • 1.6.1.1 OOBE-Based Equipment
  • 1.6.1.2 OOBE-Based Software
  • 1.6.2 Open Platforms
  • 1.6.2.1 OpenStack++
  • 1.6.2.2 WSO2-IoT Server
  • 1.6.2.3 Apache Edgent
  • 1.6.3 System Management
  • 1.6.3.1 Design
  • 1.6.3.2 Implementation
  • 1.6.3.3 Adjustment
  • 1.7 Conclusions
  • References
  • Chapter 2 Addressing the Challenges in Federating Edge Resources
  • 2.1 Introduction
  • 2.2 The Networking Challenge
  • 2.2.1 Networking Challenges in a Federated Edge Environment
  • 2.2.1.1 A Service-Centric Model
  • 2.2.1.2 Reliability and Service Mobility
  • 2.2.1.3 Multiple Administrative Domains
  • 2.2.2 Addressing the Networking Challenge
  • 2.2.3 Future Research Directions
  • 2.3 The Management Challenge
  • 2.3.1 Management Challenges in a Federated Edge Environment
  • 2.3.1.1 Discovering Edge Resources
  • 2.3.1.2 Deploying Services and Applications
  • 2.3.1.3 Migrating Services across the Edge
  • 2.3.1.4 Load Balancing
  • 2.3.2 Current Research
  • 2.3.3 Addressing the Management Challenges
  • 2.3.3.1 Edge-as-a-Service (EaaS) Platform
  • 2.3.3.2 Edge Node Resource Management (ENORM) Framework
  • 2.3.4 Future Research Directions
  • 2.4 Miscellaneous Challenges
  • 2.4.1 The Research Challenge
  • 2.4.1.1 Defined Edge Nodes
  • 2.4.1.2 Unified Architectures to Account for Heterogeneity
  • 2.4.1.3 Public Usability of Edge Nodes
  • 2.4.1.4 Interoperability with Communication Networks
  • 2.4.1.5 Network Slices for Edge Systems
  • 2.4.2 The Modeling Challenge
  • 2.4.2.1 Computational Resource Modeling
  • 2.4.2.2 Demand Modeling
  • 2.4.2.3 Mobility Modeling
  • 2.4.2.4 Network Modeling
  • 2.4.2.5 Simulator Efficiency
  • 2.5 Conclusions
  • References
  • Chapter 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
  • 3.3.1 Analytical Models
  • 3.3.2 Petri Net Models
  • 3.3.3 Integer Linear Programming
  • 3.3.4 Other Approaches
  • 3.4 Integrated C2F2T Literature by Use-Case Scenarios
  • 3.5 Integrated C2F2T Literature by Metrics
  • 3.5.1 Energy Consumption
  • 3.5.2 Performance
  • 3.5.3 Resource Consumption
  • 3.5.4 Cost
  • 3.5.5 Quality of Service
  • 3.5.6 Security
  • 3.6 Future Research Directions
  • 3.7 Conclusions
  • Acknowledgments
  • References
  • Chapter 4 Management and Orchestration of Network Slices in 5G, Fog, Edge, and Clouds
  • 4.1 Introduction
  • 4.2 Background
  • 4.2.1 5G
  • 4.2.2 Cloud Computing
  • 4.2.3 Mobile Edge Computing (MEC)
  • 4.2.4 Edge and Fog Computing
  • 4.3 Network Slicing in 5G
  • 4.3.1 Infrastructure Layer
  • 4.3.2 Network Function and Virtualization Layer
  • 4.3.3 Service and Application Layer
  • 4.3.4 Slicing Management and Orchestration (MANO)
  • 4.4 Network Slicing in Software-Defined Clouds
  • 4.4.1 Network-Aware Virtual Machines Management
  • 4.4.2 Network-Aware Virtual Machine Migration Planning
  • 4.4.3 Virtual Network Functions Management
  • 4.5 Network Slicing Management in Edge and Fog
  • 4.6 Future Research Directions
  • 4.6.1 Software-Defined Clouds
  • 4.6.2 Edge and Fog Computing
  • 4.7 Conclusions
  • Acknowledgments
  • References
  • Chapter 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
  • 5.6.1 Performance
  • 5.6.2 Resource Usage
  • 5.6.3 Energy Consumption
  • 5.6.4 Financial Costs
  • 5.6.5 Further Quality Attributes
  • 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
  • Chapter 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
  • 6.3.1 Ad-Hoc Device Discovery
  • 6.3.2 Run-Time Execution Environment
  • 6.3.3 Minimal Task Disruption
  • 6.3.4 Overhead of Operational Parameters
  • 6.3.5 Context-Aware Adaptive Design
  • 6.3.6 Quality of Service
  • 6.4 State-of-the-Art Middleware Infrastructures
  • 6.5 System Model
  • 6.5.1 Embedded Sensors or Actuators
  • 6.5.2 Personal Devices
  • 6.5.3 Fog Servers
  • 6.5.4 Cloudlets
  • 6.5.5 Cloud Servers
  • 6.6 Proposed Architecture
  • 6.6.1 API Code
  • 6.6.2 Security
  • 6.6.2.1 Authentication
  • 6.6.2.2 Privacy
  • 6.6.2.3 Encryption
  • 6.6.3 Device Discovery
  • 6.6.4 Middleware
  • 6.6.4.1 Context Monitoring and Prediction
  • 6.6.4.2 Selection of Participating Devices
  • 6.6.4.3 Data Analytics
  • 6.6.4.4 Scheduling and Resource Management
  • 6.6.4.5 Network Management
  • 6.6.4.6 Execution Management
  • 6.6.4.7 Mobility Management
  • 6.6.5 Sensor/Actuators
  • 6.7 Case Study Example
  • 6.8 Future Research Directions
  • 6.8.1 Human Involvement and Context Awareness
  • 6.8.2 Mobility
  • 6.8.3 Secure and Reliable Execution
  • 6.8.4 Management and Scheduling of Tasks
  • 6.8.5 Modularity for Distributed Execution
  • 6.8.6 Billing and Service-Level Agreement (SLA)
  • 6.8.7 Scalability
  • 6.9 Conclusions
  • References
  • Chapter 7 A Lightweight Container Middleware for Edge Cloud Architectures
  • 7.1 Introduction
  • 7.2 Background/Related Work
  • 7.2.1 Edge Cloud Architectures
  • 7.2.2 A Use Case
  • 7.2.3 Related Work
  • 7.3 Clusters for Lightweight Edge Clouds
  • 7.3.1 Lightweight Software - Containerization
  • 7.3.2 Lightweight Hardware - Raspberry Pi Clusters
  • 7.4 Architecture Management - Storage and Orchestration
  • 7.4.1 Own-Build Cluster Storage and Orchestration
  • 7.4.1.1 Own-Build Cluster Storage and Orchestration Architecture
  • 7.4.1.2 Use Case and Experimentation
  • 7.4.2 OpenStack Storage
  • 7.4.2.1 Storage Management Architecture
  • 7.4.2.2 Use Case and Experimentation
  • 7.4.3 Docker Orchestration
  • 7.4.3.1 Docker Orchestration Architecture
  • 7.4.3.2 Docker Evaluation - Installation, Performance, Power
  • 7.5 IoT Integration
  • 7.6 Security Management for Edge Cloud Architectures
  • 7.6.1 Security Requirements and Blockchain Principles
  • 7.6.2 A Blockchain-Based Security Architecture
  • 7.6.3 Integrated Blockchain-Based Orchestration
  • 7.7 Future Research Directions
  • 7.8 Conclusions
  • References
  • Chapter 8 Data Management in Fog Computing
  • 8.1 Introduction
  • 8.2 Background
  • 8.3 Fog Data Management
  • 8.3.1 Fog Data Life Cycle
  • 8.3.1.1 Data Acquisition
  • 8.3.1.2 Lightweight Processing
  • 8.3.1.3 Processing and Analysis
  • 8.3.1.4 Sending Feedback
  • 8.3.1.5 Command Execution
  • 8.3.2 Data Characteristics
  • 8.3.3 Data Pre-Processing and Analytics
  • 8.3.3.1 Data Cleaning
  • 8.3.3.2 Data Fusion
  • 8.3.3.3 Edge Mining
  • 8.3.4 Data Privacy
  • 8.3.5 Data Storage and Data Placement
  • 8.3.6 e-Health Case Study
  • 8.3.7 Proposed Architecture
  • 8.3.7.1 Device Layer
  • 8.3.7.2 Fog Layer
  • 8.3.7.3 Cloud Layer
  • 8.4 Future Research and Direction
  • 8.4.1 Security
  • 8.4.2 Defining the Level of Data Computation and Storage
  • 8.5 Conclusions
  • References
  • Chapter 9 Predictive Analysis to Support Fog Application Deployment
  • 9.1 Introduction
  • 9.2 Motivating Example: Smart Building
  • 9.3 Predictive Analysis with FogTorch?
  • 9.3.1 Modeling Applications and Infrastructures
  • 9.3.2 Searching for Eligible Deployments
  • 9.3.3 Estimating Resource Consumption and Cost
  • 9.3.4 Estimating QoS-Assurance
  • 9.4 Motivating Example (continued)
  • 9.5 Related Work
  • 9.5.1 Cloud Application Deployment Support
  • 9.5.2 Fog Application Deployment Support
  • 9.5.3 Cost Models
  • 9.5.4 Comparing iFogSim and FogTorch?
  • 9.6 Future Research Directions
  • 9.7 Conclusions
  • References
  • Chapter 10 Using Machine Learning for Protecting the Security and Privacy of Internet of Things (IoT) Systems
  • 10.1 Introduction
  • 10.1.1 Examples of Security and Privacy Issues in IoT
  • 10.1.2 Security Concerns at Different Layers in IoT
  • 10.1.2.1 Sensing Layer
  • 10.1.2.2 Network Layer
  • 10.1.2.3 Service Layer
  • 10.1.2.4 Interface Layer
  • 10.1.3 Privacy Concerns in IoT Devices
  • 10.1.3.1 Information Privacy
  • 10.1.3.2 Categorization of IoT Privacy Issues
  • 10.1.4 IoT Security Breach Deep-Dive: Distributed Denial of Service (DDoS) Attacks on IoT Devices
  • 10.1.4.1 Introduction to DDoS
  • 10.1.4.2 Timeline of Notable DoS Events
  • 10.1.4.3 Reason for the Recent Success of the DDoS Attacks
  • 10.1.4.4 Directions for Prevention of Specific Attacks on IoT Devices
  • 10.1.4.5 Steps to Prevent Attacks on IoT Devices
  • 10.2 Background
  • 10.2.1 Brief Overview of Machine Learning
  • 10.2.2 Frequently Used Machine-Learning Algorithms
  • 10.2.2.1 Classification
  • 10.2.2.2 Regression
  • 10.2.2.3 Clustering
  • 10.2.2.4 Dimensionality Reduction
  • 10.2.2.5 Combining Models (Ensemble ML)
  • 10.2.2.6 Artificial Neural Networks
  • 10.2.3 Examples of Machine-Learning Algorithms in IoT
  • 10.2.3.1 Overview
  • 10.2.3.2 Examples
  • 10.2.4 Machine-Learning Algorithms by IoT Domains
  • 10.2.4.1 Healthcare
  • 10.2.4.2 Utilities - Energy/Water/Gas
  • 10.2.4.3 Manufacturing
  • 10.2.4.4 Insurance
  • 10.2.4.5 Traffic
  • 10.2.4.6 Smart City - Citizens and Public Places
  • 10.2.4.7 Smart Homes
  • 10.2.4.8 Agriculture
  • 10.3 Survey of ML Techniques for Defending IoT Devices
  • 10.3.1 Systematic Categorization of ML Solutions for IoT Security
  • 10.3.2 Examples of ML Algorithms for IoT Security
  • 10.3.2.1 Malware Detection Using SVM
  • 10.3.2.2 Malware Detection Using a Random Forest
  • 10.3.2.3 Intrusion Detection Using PCA, Naïve Bayes, and KNN
  • 10.3.2.4 Anomaly Detection Using Classification
  • 10.3.3 Use of Artificial Neural Networks (ANN) to Forecast and Secure IoT Systems
  • 10.3.4 New Flavors of Attacks on IoT Devices
  • 10.3.4.1 Mirai
  • 10.3.4.2 Brickerbot
  • 10.3.4.3 FLocker
  • 10.3.4.4 Summary
  • 10.3.5 Proposal for Effective ML Techniques to Achieve IoT Security
  • 10.3.5.1 Insights from the Research
  • 10.3.5.2 Proposals
  • 10.4 Machine Learning in Fog Computing
  • 10.4.1 Introduction
  • 10.4.2 Machine Learning for Fog Computing and Security
  • 10.4.3 Examples of Machine Learning in Fog Computing
  • 10.4.3.1 ML in Fog Computing in Industry
  • 10.4.3.2 ML in Fog Computing in Retail
  • 10.4.3.3 Fog Computing for Self-Driving Cars
  • 10.4.4 Machine Learning in Fog Computing Security
  • 10.4.5 Other Machine-Learning Algorithms for Fog Computing
  • 10.5 Future Research Directions
  • 10.6 Conclusions
  • References
  • Part III Applications and Issues
  • Chapter 11 Fog Computing Realization for Big Data Analytics
  • 11.1 Introduction
  • 11.2 Big Data Analytics
  • 11.2.1 Benefits
  • 11.2.2 A Typical Big Data Analytics Infrastructure
  • 11.2.2.1 Big Data Platform
  • 11.2.2.2 Data Management
  • 11.2.2.3 Storage
  • 11.2.2.4 Analytics Core and Functions
  • 11.2.2.5 Adaptors
  • 11.2.2.6 Presentation
  • 11.2.3 Technologies
  • 11.2.4 Big Data Analytics in the Cloud
  • 11.2.5 In-Memory Analytics
  • 11.2.6 Big Data Analytics Flow
  • 11.3 Data Analytics in the Fog
  • 11.3.1 Fog Analytics
  • 11.3.2 Fog-Engines
  • 11.3.3 Data Analytics Using Fog-Engines
  • 11.4 Prototypes and Evaluation
  • 11.4.1 Architecture
  • 11.4.2 Configurations
  • 11.4.2.1 Fog-Engine as a Broker
  • 11.4.2.2 Fog-Engine as a Data Analytics Engine
  • 11.4.2.3 Fog-Engine as a Server
  • 11.4.2.4 Communication with Fog-Engine versus the Cloud
  • 11.5 Case Studies
  • 11.5.1 Smart Home
  • 11.5.1.1 Fog-Engine as a Broker
  • 11.5.1.2 Fog-Engine as a Data Analytic Engine
  • 11.5.1.3 Fog-Engine as a Server
  • 11.5.2 Smart Nutrition Monitoring System
  • 11.6 Related Work
  • 11.7 Future Research Directions
  • 11.8 Conclusions
  • References
  • Chapter 12 Exploiting Fog Computing in Health Monitoring
  • 12.1 Introduction
  • 12.2 An Architecture of a Health Monitoring IoT-Based System with Fog Computing
  • 12.2.1 Device (Sensor) Layer
  • 12.2.2 Smart Gateways with Fog Computing
  • 12.2.3 Cloud Servers and End-User Terminals
  • 12.3 Fog Computing Services in Smart E-Health Gateways
  • 12.3.1 Local Database (Storage)
  • 12.3.2 Push Notification
  • 12.3.3 Categorization
  • 12.3.4 Local Host with User Interface
  • 12.3.5 Interoperability
  • 12.3.6 Security
  • 12.3.7 Human Fall Detection
  • 12.3.8 Fault Detection
  • 12.3.9 Data Analysis
  • 12.4 System Implementation
  • 12.4.1 Sensor Node Implementation
  • 12.4.2 Smart Gateways with Fog Implementation
  • 12.4.3 Cloud Servers and Terminals
  • 12.5 Case Studies, Experimental Results, and Evaluation
  • 12.5.1 A Case Study of Human Fall Detection
  • 12.5.2 A Case Study of Heart Rate Variability
  • 12.6 Discussion of Connected Components
  • 12.7 Related Applications in Fog Computing
  • 12.8 Future Research Directions
  • 12.9 Conclusions
  • References
  • Chapter 13 Smart Surveillance Video Stream Processing at the Edge for Real-Time Human Objects Tracking
  • 13.1 Introduction
  • 13.2 Human Object Detection
  • 13.2.1 Haar Cascaded-Feature Extraction
  • 13.2.2 HOG+SVM
  • 13.2.3 Convolutional Neural Networks (CNNs)
  • 13.3 Object Tracking
  • 13.3.1 Feature Representation
  • 13.3.2 Categories of Object Tracking Technologies
  • 13.3.3 Point-Based Tracking
  • 13.3.3.1 Deterministic Methods
  • 13.3.3.2 Kalman Filters
  • 13.3.3.3 Particle Filters
  • 13.3.3.4 Multiple Hypothesis Tracking (MHT)
  • 13.3.4 Kernel-Based Tracking
  • 13.3.5 Silhouette-Based Tracking
  • 13.3.6 Kernelized Correlation Filters (KCF)
  • 13.4 Lightweight Human Detection
  • 13.5 Case Study
  • 13.5.1 Human Object Detection
  • 13.5.2 Object Tracking
  • 13.5.2.1 Multi-Object Tracking
  • 13.5.2.2 Object Tracking Phase In and Out
  • 13.5.2.3 Tracking Object Lost
  • 13.6 Future Research Directions
  • 13.7 Conclusions
  • References
  • Chapter 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
  • 14.3.1 Modularity
  • 14.3.2 Scalability
  • 14.3.3 Context-Awareness and Abstraction Support
  • 14.3.4 Decentralization
  • 14.3.5 Energy Consumption of Cloud Data Centers
  • 14.4 Fog Computing for Smart Transportation Applications
  • 14.4.1 Cognition
  • 14.4.2 Efficiency
  • 14.4.3 Agility
  • 14.4.4 Latency
  • 14.5 Case Study: Intelligent Traffic Lights Management (ITLM) System
  • 14.6 Fog Orchestration Challenges and Future Directions
  • 14.6.1 Fog Orchestration Challenges for Intelligent Transportation Applications in IoT Space
  • 14.6.1.1 Scalability
  • 14.6.1.2 Privacy and Security
  • 14.6.1.3 Dynamic Workflows
  • 14.6.1.4 Tolerance
  • 14.7 Future Research Directions
  • 14.7.1 Opportunities in the Deployment Phase
  • 14.7.1.1 Optimal Node Selection and Routing
  • 14.7.1.2 Parallelization Approaches to Manage Scale and Complexity
  • 14.7.1.3 Heuristics and Late Calibration
  • 14.7.2 Opportunities in Runtime Phase
  • 14.7.2.1 Dynamic Orchestration of Fog Resources
  • 14.7.2.2 Incremental Computation Strategies
  • 14.7.2.3 QoS-Aware Control and Monitoring Protocols
  • 14.7.2.4 Proactive Decision-Making
  • 14.7.3 Opportunities in Evaluation Phase: Big-Data-Driven Analytics (BD2A) and Optimization
  • 14.8 Conclusions
  • References
  • Chapter 15 Testing Perspectives of Fog-Based IoT Applications
  • 15.1 Introduction
  • 15.2 Background
  • 15.3 Testing Perspectives
  • 15.3.1 Smart Homes
  • 15.3.2 Smart Health
  • 15.3.3 Smart Transport
  • 15.4 Future Research Directions
  • 15.4.1 Smart Homes
  • 15.4.2 Smart Health
  • 15.4.3 Smart Transport
  • 15.5 Conclusions
  • References
  • Chapter 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
  • 16.4.1 Definitions and Terms in the GDPR
  • 16.4.1.1 Personal Data
  • 16.4.1.2 Data Subject
  • 16.4.1.3 Controller
  • 16.4.1.4 Processor
  • 16.4.1.5 Pseudonymization
  • 16.4.1.6 Limitation
  • 16.4.1.7 Consent
  • 16.4.1.8 Right to Be Forgotten
  • 16.4.1.9 Data Portability
  • 16.4.2 Obligations Defined by the GDPR
  • 16.4.2.1 Obligations of the Controller
  • 16.4.2.2 Obligations of the Processor
  • 16.4.3 Data Transfers Outside the EU
  • 16.4.3.1 Data Transfers to Third Countries
  • 16.4.3.2 Remedies, Liabilities, and Sanctions
  • 16.4.4 Summary
  • 16.5 Data Protection by Design Principles
  • 16.5.1 Reasons for Adopting Data Protection Principles
  • 16.5.2 Privacy Protection in the GDPR
  • 16.5.3 Data Protection by Default
  • 16.6 Future Research Directions
  • 16.7 Conclusions
  • Acknowledgment
  • References
  • Chapter 17 Modeling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit
  • 17.1 Introduction
  • 17.2 iFogSim Simulator and Its Components
  • 17.2.1 Physical Components
  • 17.2.2 Logical Components
  • 17.2.3 Management Components
  • 17.3 Installation of iFogSim
  • 17.4 Building Simulation with iFogSim
  • 17.5 Example Scenarios
  • 17.5.1 Create Fog Nodes with Heterogeneous Configurations
  • 17.5.2 Create Different Application Models
  • 17.5.2.1 Master-Worker Application Models
  • 17.5.2.2 Sequential Unidirectional Dataflow Application Model
  • 17.5.3 Application Modules with Different Configuration
  • 17.5.4 Sensors with Different Tuple Emission Rate
  • 17.5.5 Send Specific Number of Tuples from a Sensor
  • 17.5.6 Mobility of a Fog Device
  • 17.5.7 Connect Lower-Level Fog Devices with Nearby Gateways
  • 17.5.8 Make Cluster of Fog Devices
  • 17.6 Simulation of a Placement Policy
  • 17.6.1 Structure of Physical Environment
  • 17.6.2 Assumptions for Logical Components
  • 17.6.3 Management (Application Placement) Policy
  • 17.7 A Case Study in Smart Healthcare
  • 17.8 Conclusions
  • References
  • Index
  • Wiley Series on Parallel and Distributed Computing
  • EULA

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Bitte beachten Sie bei der Verwendung der Lese-Software Adobe Digital Editions: wir empfehlen Ihnen unbedingt nach Installation der Lese-Software diese mit Ihrer persönlichen Adobe-ID zu autorisieren!

Weitere Informationen finden Sie in unserer E-Book Hilfe.


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103,99 €
inkl. 7% MwSt.
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PDF mit Adobe-DRM
siehe Systemvoraussetzungen
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