Edge Computing: Fundamentals, Advances and Applications
- Length: 164 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-11-22
- ISBN-10: 1032126086
- ISBN-13: 9781032126081
- Sales Rank: #0 (See Top 100 Books)
This reference text presents the state-of-the-art in edge computing, its primitives, devices and simulators, applications, and healthcare-based case studies.
The text provides integration of blockchain with edge computing systems and integration of edge with Internet of Things (IoT) and cloud computing. It will facilitate the readers to setup edge-based environment and work with edge analytics. It covers important topics including cluster computing, fog computing, networking architecture, edge computing simulators, edge analytics, privacy-preserving schemes, edge computing with blockchain, autonomous vehicles, and cross-domain authentication.
Aimed at senior undergraduate, graduate students and professionals in the fields of electrical engineering, electronics engineering, computer science, and information technology, this text:
- Discusses edge data storage security with case studies and blockchain integration with edge computing system.
- Covers theoretical methods with the help of applications, use cases, case studies, and examples.
- Provides healthcare real-time case studies are elaborated in detailed by utilizing the virtues of homomorphic encryption.
- Discusses real-time interfaces, devices, and simulators in detail.
Cover Half Title Series Page Title Page Copyright Page Dedication Contents Preface About the Book Acknowledgments Author Biographies 1. Computing Paradigms 1.1 Introduction to Computing 1.2 The Major Impacts of Computing 1.3 Parallel Computing 1.3.1 Shared Memory Systems 1.3.2 Distributed Memory Systems 1.3.3 Hybrid Model 1.4 Distributed Computing 1.5 Cluster Computing 1.5.1 High-Performance Clusters 1.5.2 Load Balancing Clusters 1.5.3 High-Availability Clusters 1.6 Utility Computing 1.7 Grid Computing Grid Architecture 1.8 Cloud Computing 1.8.1 Characteristics of Cloud Environments 1.8.2 Cloud Models 1.8.2.1 Cloud Services Models 1.8.2.2 Cloud Deployment Models 1.9 Other Computing Paradigms 1.9.1 Ubiquitous Computing 1.9.2 Jungle Computing 1.9.3 Fog Computing 1.9.4 Osmotic Computing 1.10 Research Directions 1.11 Conclusion References 2. Edge Computing and Its Essentials 2.1 Introduction 2.2 Edge Computing Architecture 2.2.1 Edge Devices 2.2.2 Edge Server Cluster 2.2.3 Cloud Server 2.3 Background Essentials: IoT Devices 2.3.1 Mobile Phone-Based Sensors 2.3.2 Medical Sensors 2.3.3 Neural Sensors 2.3.4 Environmental and Chemical Sensors 2.3.5 Radio Frequency Identification 2.3.6 Actuators 2.4 Networking Architecture 2.5 Network Management and Control 2.5.1 Orchestration 2.6 Edge Computing State-of-the-Art Interfaces and Devices 2.6.1 Middleware 2.6.1.1 Hydra 2.6.1.2 Aura 2.6.1.3 TinyDB 2.6.1.4 FiWare 2.6.2 Application Interfaces 2.7 Edge Computing Simulators 2.7.1 PureEdgeSim 2.7.2 IoTSim-Edge 2.7.3 iFogSim 2.7.3.1 Creating Topology and Executing an Application in iFogSim Simulator 2.7.4 EdgeCloudSim 2.8 Research Directions 2.9 Summary References 3. Edge Analytics 3.1 Types of Data 3.2 Data Analytics 3.3 Goals of Data Analytics 3.4 Domains Benefiting from Big Data Analytics 3.5 Real-Time Applications of Data Analytics 3.6 Phases of Data Analytics 3.6.1 Data Collection and Pre-Processing 3.6.2 Machine Learning-Model Building Supervised Learning Some Popular Supervised Learning Algorithms Unsupervised Learning Some Popular Unsupervised Learning Algorithms Applications Semi-Supervised Learning Applications Reinforcement Learning Some Popular Reinforcement Learning Algorithms Applications 3.6.3 Performance Evaluation Confusion Matrix Accuracy Precision Recall F1 Score Specificity Negative Predictive Value PR Curve Receiver Operating Characteristics (ROC) curve 3.7 Types of Data Analytics 3.7.1 Descriptive Analytics 3.7.2 Diagnostic Analytics 3.7.3 Predictive Analytics 3.7.4 Prescriptive Analytics 3.8 Edge Data Analytics 3.9 Potential of Edge Analytics 3.10 Architecture of Edge Analytics 3.11 Machine Learning for Edge Devices 3.12 Edge Analytics: Case Study 3.13 Research Challenges and Future Research Directions 3.14 Summary References 4. Edge Data Storage Security 4.1 Data Security 4.2 Data Confidentiality 4.2.1 Identity-Based Encryption 4.2.2 Attribute-Based Encryption 4.2.3 Proxy Re-Encryption 4.2.4 Functional Encryption 4.2.5 Honey Encryption 4.2.6 Searchable Encryption 4.2.7 Homomorphic Encryption 4.2.7.1 Types of Homomorphic Encryption Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SHE) Fully Homomorphic Encryption (FHE) 4.2.7.2 Basic Functions of Homomorphic Encryption 4.3 Authentication 4.3.1 Single-Domain Authentication 4.3.2 Cross-Domain Authentication 4.3.3 Handover Authentication 4.4 Privacy-Preserving Schemes 4.4.1 Data Privacy 4.4.2 Location Privacy 4.4.3 Identity Privacy 4.5 Edge-Based Attack Detection and Prevention 4.6 Conclusion and Future Research Directions References 5. Blockchain and Edge Computing Systems 5.1 History of Blockchain 5.2 Distributed Ledger Technology 5.3 Role of P2P Architecture in Blockchain 5.4 Blockchain Cryptography 5.5 Characteristics of Blockchain 5.6 Benefits and Limitations of Blockchain Benefits Limitations 5.7 Types of Blockchain 5.8 Blockchain Architecture and Fundamentals 5.8.1 Blockchain behind Bitcoin Network 5.8.2 Transaction Validation 5.8.3 Mining and Block Structure 5.8.4 Consensus Mechanisms 5.8.5 Smart Contracts 5.9 Blockchain Platforms 5.9.1 Ethereum Bitcoin Vs. Ethereum Framework Overview 5.9.2 Hyperledger 5.9.3 Polkadot Network 5.10 Edge Computing with Blockchain 5.10.1 Internet of Things and Blockchain Advantages of Combining IoT and Blockchain Applications of Blockchain Integrated IoT Systems 5.10.2 System Design 5.10.3 Case Studies Case Study I: Blockchain and IoT-Based Crowd Funding System Smart Contracts Using Solidity Language Funding Contract Case Study II: Biometric System with Blockchain Technology Issues with Existing System Design Considerations System Design System Implementation Smart Contract Case Study III: Weapon Tracking Using Blockchain Introduction Functionalities System Design Implementation 5.11 Research Challenges and Future Research Directions Expert Notes By Dr. Bithin Alangot, Research Assistant, Singapore University of Technology and Design 5.12 Summary References 6. Edge Computing Use Cases and Case Studies 6.1 Use Cases 6.2 Edge Computing High-Potential Use Cases 6.2.1 Autonomous Vehicles 6.2.2 Smart Cities 6.2.3 Industrial Automation 6.2.4 Network Functions 6.2.5 Gaming 6.2.6 Content Delivery 6.2.7 Financial Sector 6.2.8 Augmented Reality 6.2.9 Healthcare Sector 6.3 Realization of Edge Computing in Healthcare Ensuring Storage Security 6.3.1 Devices and Setup 6.3.2 Case Study I: Pulse Oximeter to Detect ARDS in Edge Server 6.3.2.1 Pulse Oximetry 6.3.2.2 Oxygen Delivery (DO2) 6.3.2.3 Oxygen Consumption (VO2) 6.3.2.4 Acute Respiratory Distress Syndrome 6.3.2.5 Analysis in Edge Server 6.3.3 Case Study II: Blood Pressure Monitor to Predict Hypotension in Edge Server 6.3.3.1 Mean Arterial Pressure 6.3.3.2 Edge Server Analysis on MAP 6.3.4 Case Study III: Body Composition Scale to Detect Heat Index in Edge Server 6.3.4.1 Heat Index 6.3.4.2 Heat Index Analysis in Edge Server 6.3.5 Use Case - Edge Computing/Analytics in Industrial IOT 6.4 Conclusion and Open Research Challenges References Index
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