Secure Edge Computing: Applications, Techniques and Challenges

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
				Face-tracking
				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
			Limitations
		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