- 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
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Acknowledgments
It is another incredible book editing experience and our sincere gratitude goes to the publisher for facilitating the process. This book editing journey enhanced our patience, communication and tenacity. We are thankful to all the contributors, reviewers and publishing team. Last but not least, our thanks to our family members whose support and encouragement contributed significantly to completing this book.
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