- Cover
- Half Title
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- Acknowledgments
- Editors
- Contributors
- Section I
- Chapter 1: Secure Fog-Cloud of Things: Architectures, Opportunities and Challenges
- 1.1 Introduction
- 1.1.1 Chapter Road Map
- 1.2 Secure Fog-Cloud of Things
- 1.2.1 Environment
- 1.2.2 Architecture
- 1.3 Threats, Vulnerabilities and Exploits in Fog-Cloud of Things Ecosystems
- 1.4 Key Machine Learning Kits for Secure Fog-Cloud of Things Architecture
- 1.5 Applications
- 1.6 Opportunities and Challenges in Improving Security in Fog-Cloud of Things
- 1.6.1 Opportunities
- 1.6.2 Challenges
- 1.7 Future Trends
- 1.8 Conclusion
- References
- Chapter 2: Collaborative and Integrated Edge Security Architecture
- 2.1 Background
- 2.2 Edge Security Challenges
- 2.3 Perspectives of Edge Security Architecture
- 2.4 Emerging Trends and Enablers for Edge Security Architecture
- 2.4.1 The Edge Computing Architecture
- 2.4.2 Leveraging Fog-Based Security Architecture for Edge Networks
- 2.5 Collaborative and Integrated Security Architecture for Edge Computing
- 2.5.1 Overview
- 2.5.2 Distributed Virtual Firewall (DFWs)
- 2.5.3 Distributed Intrusion Detection Systems (IDSs)
- 2.6 Conclusion and Future Research
- References
- Chapter 3: A Systemic IoT–Fog–Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing
- 3.1 Introduction
- 3.2 Fog Computing Systems
- 3.2.1 Description of Fog
- 3.2.2 Characteristics of Fog
- 3.2.3 Systemic Architecture of IoT–Fog–Cloud
- 3.2.4 Applications of IoT, Fog and Cloud Systems
- 3.3 Cyber Security Challenges
- 3.4 Security Solutions and Future Directions
- 3.5 Conclusion
- References
- Chapter 4: Security and Organizational Strategy: A Cloud and Edge Computing Perspective
- 4.1 Introduction
- 4.2 Cloud Computing and Cloud-based Computing
- 4.3 Business Operations and Management
- 4.3.1 Business Process
- 4.3.2 Business Continuity
- 4.3.3 Risk Management and Disaster Recovery
- 4.4 Human and Technological Factors
- 4.4.1 Human Factors
- 4.4.2 Technological Factors
- 4.4.3 Copyright and SLAs
- 4.5 Trust
- 4.5.1 Intra-organizational Trust
- 4.5.2 Inter-organizational Trust
- 4.6 Geographic Location
- 4.6.1 Regulations and Jurisdictions
- 4.6.2 Compliance and Governance
- 4.7 Conclusions
- References
- Chapter 5: An Overview of Cognitive Internet of Things: Cloud and Fog Computing
- 5.1 Introduction
- 5.2 Background of Fog, Cloud and Edge Computing
- 5.2.1 Fog Computing
- 5.2.1.1 Benefits of Fog Computing
- 5.2.1.2 Disadvantages of Fog Computing
- 5.2.2 Cloud Computing
- 5.2.2.1 Benefits of Cloud Computing
- 5.2.2.2 Disadvantages of Cloud Computing
- 5.2.3 Edge Computing
- 5.2.3.1 Benefits of Edge Computing
- 5.2.3.2 Disadvantages of Edge Computing
- 5.3 Literature Review of Existing Works
- 5.3.1 Review of Fog Computing
- 5.3.2 Review of Cloud Computing
- 5.3.3 Review of Edge Computing
- 5.4 Network Architecture
- 5.4.1 Computation Between Fog and Cloud
- 5.4.2 Computation Between Fog and Fog
- 5.5 Numerical Results
- 5.6 Conclusion
- References
- Chapter 6: Privacy of Edge Computing and IoT
- 6.1 Introduction
- 6.2 IoT Ecosystem
- 6.3 Privacy Spaces
- 6.4 The Technology of Privacy Spaces
- 6.4.1 Apple HomeKit
- 6.4.2 Google Home
- 6.5 Privacy Space Data Flows
- 6.6 Remote Access
- 6.7 Personal Data Store
- 6.8 Privacy-Preserving Techniques
- 6.8.1 Anonymization
- 6.8.2 k-anonymization
- 6.8.3 Unicity
- 6.8.4 Differential Privacy
- 6.8.5 Privacy-Preserving Data Queries
- 6.9 Case Study: Contact Tracking Mobile Applications
- 6.10 Conclusions
- Notes
- References
- Section II
- Chapter 7: Reducing the Attack Surface of Edge Computing IoT Networks via Hybrid Routing Using Dedicated Nodes
- 7.1 Introduction
- 7.2 Related Works
- 7.3 The Solution
- 7.3.1 Inference System of Trusted Time Server
- 7.3.2 Security Features
- 7.3.3 Synchronization with a Trusted Time Server
- 7.3.4 Transit Addresses
- 7.4 Test Methodology and Environment
- 7.4.1 TTS Server and Data Collection for Inference
- 7.4.2 Heterogeneous Network Environment
- Simulation Case 1:
- Simulation Case 2:
- 7.4.3 Graph-based Representation
- 7.5 Case Study
- 7.6 Conclusion
- Notes
- References
- Chapter 8: Early Identification of Mental Health Disorder Employing Machine Learning-based Secure Edge Analytics: A Real-time Monitoring System
- 8.1 Introduction
- 8.2 Traditional Methods Implemented in Edge Computing
- 8.3 Secure Analytics of Smart Healthcare at the Edge
- 8.4 Related Work: Overview of Mobile Applications for Mental Health
- 8.4.1 Anxiety Reliever
- 8.4.2 Anxiety Coach
- 8.4.3 Breath2Relax
- 8.4.4 Happify
- 8.4.5 Head Space
- 8.4.6 Mindshift
- 8.4.7 MoodKit
- 8.4.8 Panic Relief
- 8.4.9 PTSD Coach
- 8.5 Methodologies for Automated Real-Time Mood Detection for Assessing Anxiety and Depression Levels in the Edge with Privacy-Preservation Capability
- 8.5.1 Data Preparation and Pre-processing
- 8.5.1.1 Identifying Optic Flow in Facial Regions
- 8.5.2 Pre-processing and Noise Elimination of the Image Data
- 8.5.3 Questionnaire Data Description
- 8.5.4 Proposed Architecture
- 8.5.5 Data Analysis Using AI Techniques
- 8.5.6 Privacy Preservation of the Model
- 8.5.6.1 Federated Learning
- 8.5.7 Model Deployment on Edge Devices
- 8.6 Experimental Results
- 8.6.1 SqlLite Analysis
- 8.6.2 Machine Learning Algorithm Analysis
- 8.6.3 Federated Learning Analysis
- 8.6.4 Comparative Analysis
- 8.7 Conclusion
- References
- Chapter 9: Harnessing Artificial Intelligence for Secure ECG Analytics at the Edge for Cardiac Arrhythmia Classification
- 9.1 Introduction
- 9.2 Literature Review
- 9.3 Dataset Preparation
- 9.4 Methodology
- 9.4.1 ECG Pre-processing Phase
- 9.4.2 Heartbeat Segmentation Phase
- 9.4.3 Feature Extraction Phase
- 9.4.4 Learning/Classification Phase
- 9.5 Experimental Setups, Results and Discussion
- 9.5.1 Performance Indicators
- 9.5.2 Results for Experimental Setup 1
- 9.5.3 Results for Experimental Setup 2
- 9.6 Conclusion
- References
- Chapter 10: On Securing Electronic Healthcare Records Using Hyperledger Fabric Across the Network Edge
- 10.1 Introduction
- 10.2 Existing Decentralized Security Methods: Can Blockchain Be Used At the Edge?
- 10.2.1 Current EHR System in Canada
- 10.2.2 Challenges with the Traditional EHR Systems
- 10.2.3 Security Measures for Health Records
- 10.3 Current Challenges Faced by the Healthcare Workers in Covid-19 Pandemic
- 10.3.1 Importance and Role of Medical Records During Pandemic
- 10.3.2 Challenges Faced by Doctors
- 10.3.3 Understanding the Proposed Architecture Using COVID-19 Example
- 10.4 Scalable Secure Management and Access Control of Electronic Health Records at the Edge
- 10.4.1 The Importance of Integrating Blockchain and Edge Computing?
- 10.4.2 Challenges
- 10.5 Overview of Blockchain and Hyper Ledger Methodologies
- 10.5.1 Blockchain
- 10.5.2 Electronic Health Records (EHRs)
- 10.5.3 Smart Contract
- 10.5.4 Access Control in Medical Domain
- 10.5.5 Hyperledger
- 10.5.6 Composer Tools
- 10.5.7 Playground
- 10.5.8 Off-chain Storage
- 10.5.9 User Experience From Patient’s Side
- 10.6 Hyper Ledger-Based Proposed Architecture for Protecting Electronic Health Records
- 10.6.1 Proposed Architecture of the Blockchain System
- 10.6.2 Data Flow Diagrams
- 10.6.2.1 Doctors
- 10.6.2.2 Patient
- 10.6.2.3 Transaction Flow
- 10.7 Performance Evaluation
- 10.7.1 Performance of the Proposed Model
- 10.7.2 Performance Comparison
- 10.8 Conclusions and Future Caveats
- References
- Chapter 11: AI-Aided Secured ECG Live Edge Monitoring System with a Practical Use-Case
- 11.1 Introduction
- 11.1.1 Background
- 11.1.2 Problem Statement
- 11.1.3 Objective and Scope
- 11.2 Related Work
- 11.3 Proposed AI-Based System Architecture
- 11.3.1 Block Diagram
- 11.3.2 Data Collection and Pre-Processing Steps
- 11.3.3 Detecting Heart Abnormalities Using AI-Aided Techniques
- 11.4 Considered Smart ECG Monitoring System
- 11.4.1 Edge Hardware Components
- 11.4.1.1 System-on-a-Chip (SoC) Model
- 11.4.1.2 IoT Sensor for Heart Rate Data Acquisition
- 11.4.1.3 Microprocessor and Analog to Digital Converter
- 11.4.2 AI-Logic Component
- 11.4.2.1 Decision Tree
- 11.4.2.2 Random Forest
- 11.4.2.3 ANN
- 11.4.2.4 CNN
- 11.5 Bio-Authentication Application of the Considered ECG Monitoring System for Specific Use-Cases
- 11.6 Performance Evaluation
- 11.6.1 Supraventricular Arrhythmia Classification
- 11.6.2 Authorized User Classification for Bio-Authentication System
- 11.7 Challenges Involved with the Proposed System
- 11.8 Conclusion and Future Scope
- References
- Section III
- Chapter 12: Application of Unmanned Aerial Vehicles in Wireless Networks: Mobile Edge Computing and Caching
- 12.1 Introduction
- 12.1.1 Chapter Roadmap
- 12.2 Literature Review
- 12.3 Description of Caching and Mobile Edge Computing
- 12.3.1 Overview of Caching
- 12.3.1.1 Advantages
- 12.3.1.2 Disadvantages
- 12.3.2 Overview of Mobile Edge Computing
- 12.3.2.1 Advantages
- 12.3.2.2 Disadvantages
- 12.4 Layering of UAV-Based MEC Architecture
- 12.4.1 Explanation of the Layers
- 12.5 System Model
- 12.5.1 Mathematical Model of NOMA
- 12.5.2 Path Loss Model
- 12.5.3 Transmission Delay
- 12.5.4 Computing Model
- 12.5.4.1 Edge Computing Model
- 12.5.4.2 Local Computing Model
- 12.5.5 Time Consumption Model
- 12.5.6 Energy Consumption Model
- 12.6 Simulation Results
- 12.7 Conclusion
- References
- Chapter 13: Vehicular Edge Computing Security
- 13.1 Introduction
- 13.1.1 Chapter Roadmap
- 13.2 Vehicular Edge Computing Overview
- 13.2.1 VEC Architecture and Provided Services
- 13.2.2 Enabling Technologies: 5G, SDN, NFV, AI, Blockchain
- 13.2.3 Overview of Challenges
- 13.2.3.1 Task Offloading
- 13.2.3.2 Network Management
- 13.2.3.3 Caching
- 13.2.3.4 Data Management
- 13.2.3.5 Security and Privacy
- 13.3 Security Threats and Analysis of Potential Security Challenges
- 13.3.1 Access Control and Trust Management
- 13.3.2 Data Management
- 13.3.3 Decentralized Computation
- 13.3.4 Intrusion and Anomaly Detection
- 13.4 State-of-the-Art Solutions for Security Issues in VEC
- 13.4.1 Identity Preservation, Trust Management and Authentication
- 13.4.1.1 Pseudonym Management Scheme for Identity Preservation
- 13.4.1.2 Ensuring Trust with Distributed Reputation Management
- 13.4.1.3 Blockchain-Aided Cooperative Authentication
- 13.4.2 Blockchain-Based Secure Data Management
- 13.4.3 Secure Distributed Computation Techniques
- 13.4.4 RSU Misbehavior and Vehicle Anomaly Detection
- 13.5 Discussion
- 13.6 Conclusion
- References
- Section IV
- Chapter 14: On Exploiting Blockchain Technology to Approach toward Secured, Sliced, and Edge Deployed Virtual Network Functions for Improvised IoT Services
- 14.1 Introduction
- 14.2 Literature Review
- 14.3 Blockchain-Powered Secured Slicing
- 14.4 The Blockchain-Inspired Architecture for Network Slicing
- 14.5 The Hyperledger Fabric-Driven Prototype
- 14.6 Conclusion
- References
- Chapter 15: Usage of Blockchain for Edge Computing
- 15.1 Introduction
- 15.2 Applications and Benefits of Edge Computing
- 15.2.1 Identify the Benefits of Using Edge Computing from Different Perspectives
- 15.2.2 Identify the Applications of Edge Computing in Different Fields
- 15.3 Issues in Edge Computing
- 15.3.1 Issues in Security and Privacy
- 15.3.2 Issues in Decentralized Architecture
- 15.4 Integrating Blockchain in Edge Computing: The Missing Piece of the Puzzle?
- 15.4.1 Blockchain: Beyond Cryptocurrency
- 15.4.2 Advantages of Blockchain
- 15.4.3 How Blockchain Will Complement Edge Computing
- 15.4.4 How Blockchain Can be Integrated with Edge Computing
- 15.4.4.1 Requirements: Integrated Blockchain and Edge Computing
- 15.4.4.2 Overview on Existing Frameworks
- 15.5 Challenges and Future Scope for Incorporating Blockchain to Edge Computing
- 15.6 Conclusion
- References
- Index
..................Content has been hidden....................
You can't read the all page of ebook, please click here login for view all page.