SDN-Supported Edge-Cloud Interplay for Next Generation Internet of Things

Cover
Half Title
Series Page
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Chapter 1: SDN-Based IoT with Edge Computing
1.1 Introduction
Organization of Chapter
1.2 Edge Computing, SDN and IoT Devices
1.2.1 Edge Computing
1.2.2 Software-defined Networks
1.2.3 IoT Devices
1.3 Integration of SDN with Edge Computing
1.3.1 Edge Computing Level
1.3.1.1 Why Does Edge Computing Matter in SDN Networks?
1.3.2 Fog Computing Level
1.3.3 Cloud Computing Level
1.3.4 Interaction between SDN and Edge–Cloud
1.4 Integration of SDN with IoT Devices
1.4.1 Role of SDN In Control of IoT Devices
1.4.2 Role of SDN In IOT Resource Management
1.4.3 Role of SDN In IoT Security and Privacy
1.5 Security Issues with Edge Computing
1.5.1 Eavesdropping Attacks
1.5.2 Denial-of-Service Attacks
1.5.2.1 Implementation of DDOS Attacks
1.5.2.2 Identifying DDOS Attacks
1.5.2.3 Types of DDOS Attacks
1.5.2.4 Mitigating the Process of DDOS Attacks
1.6 Enhancing SDN for Future Challenges of Edge Computing
1.6.1 Protocols and Standardization
1.6.1.1 Enhancing OpenFlow
1.6.1.2 Northbound Interface Standardization
1.6.1.3 Debugging Capabilities
1.6.1.4 East–west Interface and Geographical Scalability
1.7 Scalability
1.7.1 Administrative Scalability
1.7.2 Geographic Scalability
1.7.3 Scalability and Reliability
1.7.4 Finer Granularity of Control and Abstraction
1.8 Networking
1.8.1 Enhancing Network Virtualization
1.8.2 Enhancing the Forward Plane
1.9 Conclusion
Notes
Bibliography
Chapter 2: Towards Cloud-Edge-of-Things: State of the Art, Challenges and Future Trends
2.1 Introduction
2.2 Background to Cloud-Edge Computing
2.2.1 Similarities of Edge Computing and Data Center Computing
2.2.2 Dissimilarities of Edge Computing and Large Data Centers
2.2.3 Capabilities of Edge Computing
2.3 Overview of Cloud and IoT
2.4 Overview of Edge and IoT
2.5 Challenges Faced by Edge Computing
2.6 Potential Use Cases
2.6.1 Data Gathering and Analytics
2.6.2 Security
2.6.3 Compliance
2.6.4 Network Function Virtualization (NFV)
2.6.5 Immersive Applications
2.6.6 Efficiency
2.6.7 Autonomous Operations
2.6.8 Privacy
2.6.9 Real Time
2.7 Real-Time Cloud-Edge Computing Applications
2.7.1 Autonomous Vehicles or Vehicular Communication
2.7.2 Oil and Gas Industry
2.7.3 Smart Grid
2.7.4 Healthcare
2.7.5 Surveillance
2.7.6 Military
2.7.7 Virtualization
2.7.8 Content Caching and Delivery
2.7.9 Smart Homes, Offices and Buildings
2.8 Open Issues and Future Trends
2.9 Conclusion and Future Scope
References
Chapter 3: SDN-Aided Edge Computing-Enabled AI for IoT and Smart Cities
3.1 Introduction
Organization of the Chapter
3.2 Internet of Things (IoT)
3.2.1 Background to IoT
3.2.2 Working Model of IoT
3.2.3 IoT Architecture
3.2.4 IoT Technology Stack
3.3 Software-Defined Network (SDN)
3.4 Rise of Artificial Intelligence in Technology Enlargement
3.4.1 Technologies and Techniques Provided by Artificial Intelligence for Edge Computing
3.4.2 Edge Computing Gives AI Scenarios and Platforms to Work With
3.5 AI-Enabled IoT Computing
3.5.1 Edge Computing
3.5.2 Fog Computing
3.5.3 Cloud Computing
3.6 SDN-Aided Edge-Enabled AI for IoT
3.7 Recent Trends in Edge Computing-Based IoT Applications
3.7.1 Smart Cities
3.7.2 Business
3.7.3 Medical and Health Care
3.7.4 Education
3.7.5 Agriculture
3.7.6 Smart Home
3.7.7 Automotive Industry
3.8 Edge Computing: Architecture and Security
3.8.1 Architecture and Tasks
3.8.2 Privacy and Security
3.8.3 Measures and Risk Reduction
3.9 Challenges and Opportunities
3.10 Case Study of SDN-Aided Edge Computing
3.11 Conclusion and Future Scope
References
Chapter 4: An Overview of Software-Defined Internet of Vehicles
4.1 Introduction
4.2 Background
4.2.1 Software-Defined Networks (SDN)
4.2.1.1 Infrastructure Layer
4.2.1.2 Control Layer
4.2.1.3 Application Layer
4.2.2 OpenFlow
4.2.3 Network Function Virtualization (NFV)
4.3 Internet of Vehicles (IoV)
4.3.1 Software-Defined Internet of Vehicles (SD-IoV)
4.4 Software-Defined IoV: Architecture
4.5 Software-Defined IoV: Open Issues
4.6 Conclusion and Future Scope
Acknowledgment
References
Chapter 5: IoT Architecture and Research Issues in the Smart City Environment
5.1 Introduction
Organization of the Chapter
5.2 Related Work
5.3 Analysis of Current Technology Available for Smart Cities
5.4 Overview of Power Consumption Models in IoT for Smart Cities
5.5 Smart City Scenario
5.6 Smart City Aspects
5.6.1 Smart Citizen
5.6.2 Smart Home
5.6.3 Smart Health Care
5.6.4 Smart Education
5.6.5 Smart Transportation
5.6.6 Smart Government
5.6.7 Smart Grid
5.6.8 Smart Business
5.7 Issues Related to Smart Cities
5.7.1 Human Well-being
5.7.2 Communication
5.7.3 Transportation
5.7.4 Power Efficiency
5.7.5 Preservation of Critical Resources
5.7.6 Efficient Healthcare System
5.7.7 Security and Trust
5.8 Usefulness and Challenges of the Smart City in India
5.9 Conclusion
References
Chapter 6: Performance Evaluation Methods for SDN Controllers: A Comparative Analysis
6.1 Introduction
Organization of the Chapter
6.2 SDN Architecture
6.2.1 Data Plane
6.2.1.1 Southbound (SB) APIs
6.2.1.2 Northbound (NB) API
6.2.2 Management Plane
6.2.3 Control Plane
6.3 Categorization of SDN Controller Selection Approaches
6.3.1 Performance Analysis Controller Selection
6.3.2 Feature-Based Controller Selection
6.3.3 Hybrid Methods of Controller Selection
6.3.4 Multi-Criteria Decision-Making (MCDM) Methods for Controller Selection in SDN
6.4 Analytical Network Process-Based Controller Selection
6.4.1 Pairwise Comparison Matrix for Criteria and Alternatives
6.4.2 Pairwise Comparison Matrix for Criteria and Alternatives
6.4.3 Pairwise Comparison for Criteria with Respect to Controllers
6.4.4 Weighted Super-Matrix
6.4.5 Super-Matrix (Limit) Computation
6.5 Results and Discussion
6.5.1 Throughput Evaluation
6.5.2 Utilization of CPU
6.6 Conclusion and Future Scope
References
Chapter 7: A Scalable Software-Defined Edge Computing Model for Sustainable Smart City Internet of Things (IoT) Application
7.1 Background and Motivation
7.2 Introduction
Objectives of Chapter
Organization of Chapter
7.3 Literature Survey
7.4 Evolution of Edge Computing
7.4.1 Cloud Computing
7.4.2 Cloudlet Computing
7.4.3 Mobile Edge Computing (MEC)
7.4.4 Edge Computing
7.4.5 Fog Computing
7.5 Internet of Things (IoT) Network
7.5.1 Perception Layer
7.5.2 Network Layer
7.5.3 Application Layer
7.6 Characteristics of IoT Network
7.6.1 Internet of Things (IoT) Applications
7.6.2 Challenges of IoT Applications
7.6.3 Classification of IoT Applications
7.7 Proposed Edge-SDN Architecture
7.7.1 Different Components of Fog-SDN Architecture
7.8 Mathematical Models
7.8.1 IoT Application Model
7.8.2 FCM Queueing Model
7.9 Research Challenges
7.10 Conclusion and Future Scope
References
Chapter 8: Deep Learning Models with SDN and IoT for Intelligent Healthcare
8.1 Introduction
Organization of the Chapter
8.2 Related Work
8.3 Methodology
8.3.1 SDN Overview
8.3.2 SDN for IoT
8.3.3 Approaches Used in Smart Healthcare
8.3.3.1 Machine Learning-Based Approach
8.3.3.2 Deep Learning-Based Approach
8.3.3.3 Hyper-parameters
8.3.3.4 Algorithm Principles
8.3.4 Smart Healthcare Using Machine Learning
8.4 Case Studies
8.4.1 Case I: Study of Skin Cancer Using Deep Learning (CNN)
8.4.1.1 Dataset
8.4.1.2 CNN Model and Architecture
8.4.2 Case II: How Deep Learning and IoT Help Make Healthcare Effective
8.5 Simulation Results and Discussion
8.5.1 Augmented IoT
8.5.2 Applications of Deep Learning and IoT
8.5.2.1 Drug Research and Production
8.5.2.2 Diagnosis and Identification of Diseases
8.5.2.3 Personalized Treatment
8.5.2.4 Clinical Trials
8.5.2.5 Enhanced Radiotherapy
8.5.2.6 Maintenance of Health Records
8.5.2.7 Data Collection
8.5.2.8 Epidemic Outbreak Prediction
8.5.2.9 Analytics for Pattern Imaging
8.5.3 Challenges
8.5.3.1 Data Volume
8.5.3.2 Data Quality
8.5.3.3 Temporality
8.5.3.4 Complexity of the Domain
8.5.3.5 Interpretability
8.6 Conclusion and Future Scope
References
Chapter 9: Applications of Machine Learning Techniques in SD-IoT Traffic Management
9.1 Overview
9.1.1 Introduction
9.1.2 DDoS Detection
9.1.3 Software-Defined Networking (SDN)
9.1.4 Edge Computing
9.1.5 Fog Computing
9.1.6 A Generic Framework for SD-IoT
9.1.7 Traffic Management System Based on Software-Defined IoT
Organization of Chapter
9.2 IoT
9.2.1 Cybersecurity and Internet of Things (IoT)
9.2.2 IoT Terms and Acronyms
9.2.3 Features of IoT
9.3 IoT-Based Traffic Management
9.3.1 Growth of IoT Devices for Traffic Management System
9.3.2 Existing Traffic Signals
9.3.3 Algorithm for Calculating Green Light Phase Time
9.3.4 Wireless Traffic Controller during Rush Hour
9.3.5 Traffic Light System Security
9.4 Machine Learning
9.4.1 Machine Learning in IoT
9.5 Smart Transportation
9.5.1 ML and IoT in Smart Transportation
9.5.2 IoT and ML Applications in Smart Transportation
9.6 Conclusion
References
Index