The strategically sound combination of edge computing and artificial intelligence (AI) results in a series of distinct innovations and disruptions enabling worldwide enterprises to visualize and realize next-generation software products, solutions and services. Businesses, individuals, and innovators are all set to embrace and experience the sophisticated capabilities of Edge AI. With the faster maturity and stability of Edge AI technologies and tools, the world is destined to have a dazzling array of edge-native, people-centric, event-driven, real-time, service-oriented, process-aware, and insights-filled services. Further on, business workloads and IT services will become competent and cognitive with state-of-the-art Edge AI infrastructure modules, AI algorithms and models, enabling frameworks, integrated platforms, accelerators, high-performance processors, etc. The Edge AI paradigm will help enterprises evolve into real-time and intelligent digital organizations.
Applied Edge AI: Concepts, Platforms, and Industry Use Cases focuses on the technologies, processes, systems, and applications that are driving this evolution. It examines the implementation technologies; the products, processes, platforms, patterns, and practices; and use cases. AI-enabled chips are exclusively used in edge devices to accelerate intelligent processing at the edge. This book examines AI toolkits and platforms for facilitating edge intelligence. It also covers chips, algorithms, and tools to implement Edge AI, as well as use cases.
FEATURES
- The opportunities and benefits of intelligent edge computing
- Edge architecture and infrastructure
- AI-enhanced analytics in an edge environment
- Encryption for securing information
- An Edge AI system programmed with Tiny Machine learning algorithms for decision making
- An improved edge paradigm for addressing the big data movement in IoT implementations by integrating AI and caching to the edge
- Ambient intelligence in healthcare services and in development of consumer electronic systems
- Smart manufacturing of unmanned aerial vehicles (UAVs)
- AI, edge computing, and blockchain in systems for environmental protection
- Case studies presenting the potential of leveraging AI in 5G wireless communication
Author(s): R. I. Minu, G. Nagarajan, Pethuru Raj Chelliah
Publisher: CRC Press
Year: 2022
Language: English
Pages: 318
City: Boca Raton
Cover
Half Title
Title Page
Copyright Page
Contents
Contributors
Chapter 1. Edge Computing: Opportunities and Challenges
Introduction
Background
Artificial Intelligence
Edge AI
Advantages of Edge AI
How Edge AI Helps
Reduced Latency
Scalability
Real-Time Analytics
Reduced Cost
Privacy and Security
Edge AI and the Internet of Things
Smart Applications
Manufacturing
Transportation and Traffic
Health Care
Energy and Retail
Edge Chip
What Do Edge AI Chip Functions Do?
Chip Innovation to Meet the Edge's Needs
Discussion on Edge AI
Apart from the Data Center
Smart Devices
Rethinking and Reconnecting AI
On the Top Ledge
Conclusion
References
Chapter 2. Demystifying the Edge AI Paradigm
Introduction
Edge or Fog Devices and Their Roles
About Edge Computing
Edge Computing Architecture
Edge Cloud Infrastructures
Edge Analytics
Tending towards Edge AI
Artificial Intelligence (AI) Chips for Edge Devices
The Noteworthy Trends towards Edge AI
Why Edge Site Processing?
How Are Edge-Based AI Solutions Produced?
Applications of Edge Devices
Computer Vision on the Edge
Machine Learning (ML) on the Edge
Approaches for Analytics in Edge Devices
Microservices
Microservices Pattern Language
5G Technology at the Edge
Network Function Virtualization (NFV)
Network Slicing in 5G Core (5GC)
ML Models for Edge Devices
Deep Learning at the Edge
Edge-Based Inferencing
Natural Language Processing (NLP) at the Edge
5G for Edge Computing
Edge AI Use Cases
Ambient Intelligence (AmI)
Conclusion
References
Chapter 3. Big Data Driven Edge-Cloud Collaboration for Cloud Manufacturing with SDN Technologies
Introduction
Classifications of Edge Computing
Fog Computing
Real-Time Application Execution
Resource Management
Edge Computing in Big Data
Big Data Analytics
Artificial Intelligent in Edge Computing
Benefits of Big Data Analytics in Fog
SDN Perspective of Edge Computing
Software-Defined Networking
Advantages and Disadvantages of SDN Model
Data-Intensive Applications for the Workload Slicing Approach
SDN Controller
Evaluation
Practical Applications of SDN
Enhanced Safety
Compact Functioning Charges
Better User Experience
Role of Big Data in Decision Making
Earliest DSS
References
Chapter 4. Artificial Intelligence in 5G and Beyond Networks
Introduction
Applying AI in 5G Network Functions
Fundamentals on ML
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Types of AI-Related Problems
Optimization
Detection
Estimation
AI-Enabled 5G Network Management
State of Play on AI-Enabled 5G Functionality
The AI Ambition for 5G Technologies
Network Slicing beyond MANO
Ambition
Radio Technologies and Spectrum
Ambition
Core and Edge Computing and Networking
Ambition
The AI Ambition for 5G Vertical Sectors
Innovations for the Media and Entertainment Sector
Ambition
Innovations for the PPDR Sector
Ambition
Innovations for the Automotive Sector
Ambition
Innovations for the E-Health Sector
Ambition
Creating Innovation Potential for AI Applications
Benefits and Impact of 5G/B5G in AI-Enabled Vertical Industries
Strategic and Operational Benefits
Direct User Benefits
New Business Models and Opportunities for Revenue
Societal Benefits
Future Perspectives
Conclusions
Acknowledgment
References
Chapter 5. An Application-Oriented Study of Security Threats and Countermeasures in Edge Computing-Assisted Internet of Things
Introduction
Edge Paradigms
Mobile Cloud Computing
Fog Computing
Mobile Edge Computing
Applications of Edge Paradigms
Smart City
General Data Protection Regulations inside the Smart City Environment
Ensuring Information Security
Preserving Privacy
Programming of IoT Services
Trust-Oriented Service Placements
Trust Management Mechanism
Industrial IOT
Architecture of Edge Computing in IIoT
Control Systems
Data Security
Data Storage and Searching
Network Management
Resource Management
Security Requirements
Trust Management Mechanism
Use Case: Builder Company
Vehicular Networks
Intrusion Detection Mechanism
Authentication Mechanism
Trust-Based Clustering Approach
Healthcare Monitoring
Time-Critical Applications
Disaster Management
Manhole Cover Management Systems
Live Data Analytics
Societal Applications
Security Threats and Countermeasures
Side Channel Attack
Cyber Threats
Distributed Denial of Service (DDoS) Attacks
Hardware Trojan Attacks
Attacks on HTTP
Impersonation Attacks
Malware Attacks
Poisoning Attacks
Discussion
Analysis of Existing Defense Mechanisms
Open Research Challenges
Conclusion and Future Works
References
Chapter 6. Edge AI for Industrial IoT Applications
General Overview
Edge Nodes
Edge AI in Industries
Smart Agriculture
Agribots
Farm Automation
Disaster Protection
Autonomous Vehicles
AI in Self-Driving Cars
Edge AI in Industries
Drawbacks of Edge AI
Less Computational Power
Machine Variations
Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications
General Overview
Data Privacy
Data Security
Business Continuity
Next-Generation Analytics
Increased Innovation
Customer Experience
Influencing Factors
Edge AI-Enabled IoT Devices
Benefits of Edge AI in IoT Devices
Edge Computing Downsides for IoT
Edge AI-Enabled IoT Devices
Case Study: Edge Deduction
Ethereum Blockchain with Edge AI
Edge Computing Downsides for IoT
Data Trade through Ethereum
Case Study: Blockchain for Transaction
Conclusion
References
Chapter 7. Edge AI: From the Perspective of Predictive Maintenance
Industry 4.0: Country's Vision to Become a Superpower
Smart Manufacturing
Pursuit of Edge AI in Smart Manufacturing
Predictive Maintenance: The Future Era of Maintenance
Genesis of Predictive Maintenance and Its Future
Reactive Maintenance
Preventive Maintenance
Predictive Maintenance
Prescriptive Maintenance
A Brief Overview of Predictive Maintenance
Niche of Edge and Fog Computing in Predictive Maintenance
Limitations of Implementing Cloud-Based Predictive Maintenance
Types of Edge AI in Industries
Stature of Edge AI in Predictive Maintenance
Edge AI Framework for Predictive Maintenance in Industries
Design Requirements of the Edge AI-Based Predictive Analytic Framework
Challenges in the Edge AI-Based Predictive Analytic Framework
Conclusion and Future Work
References
Chapter 8. Unlocking the Potential of (AI-Powered) Blockchain Technology in Environment Sustainability and Social Good
Introduction
COVID-19 Tracking Blockchain Powered by AI Platform and Global Environmental Sustainability
Blockchain Powered by AI and Its Application in Environmental Sustainability and Social Good
Application of Blockchain Technology in Climate Change
Blockchain and Biodiversity Protection
How Are Biodiversity and Ecosystems Protected?
Blockchain and Marine/Ocean Conservation
Blockchain Applications towards a Sustainable Ocean
Blockchain-Enabled Technological Projects
Techniques and Practices to Protect the Environment and Social Good through the AI-Powered Blockchain Models
Challenges Facing the Blockchain Model in Environment Safety and Social Good
Future Potentials of AI-Powered Blockchain and Social Good
Conclusion
References
Chapter 9. UAV-Based Smart Wing Inspection System
Introduction
Aircraft Wing Structure
Types of Aircraft Wing Structures
UAV and Its Specifications
Edge AI
Causes for Wing Failure
Various Modes of Wing Failure
Fatigue Cracks
Corrosion
Lightning Strikes
Ice Formation
Design and Manufacturing Errors
Hydrogen Embrittlement
NDT Inspection Methods
Ultrasonic Testing (UT)
Infrared Thermography (IRT)
Acoustic Emission (AE)
Eddy Current Testing (ECT)
Laser Shearography (LS)
Deep Learning and Deep Neural Networks
Architectural Design of Edge AI-Based Wing Inspection System
Motivation - A Need for Edge AI
Edge Networking Data Generating Using AI
Implementation Algorithm
Software Requirement
Hardware Selection
Scope for Future Development
Conclusion
References
Chapter 10. Edge AI-Based Aerial Monitoring
Introduction
Solar Cell
Drones and Their Specifications
Specifications of an Inspection Drone
Ways Drones Deliver Value to Solar Industry
Edge AI
Analysis of Various Modes of Solar Cell Failure (FMEA)
Solar Cell Failure
Encapsulant Tarnish Failure
Voltage Failure
Corrosion Failure
Failure in Junction Bay Box
Failure Due to Delamination
Bubble Formation Failure
Failure Due to Cracks
Existing Inspection and Rectification Methods
Manual Ground-Level Inspection
One-Camera Tripod System
Multi-Camera Tripod System
AI-Based Methods in Solar Cell Failure Analysis
Artificial Intelligence (AI)
Recognition Technology
Machine Learning
Architectural Design of Edge AI-Based Solar Cell Inspection System
Motivation: Need of Edge AI-Based System
Implementation of Algorithm
Suggested Algorithm-Design Framework
Software Requirement
Flight Application Software
Data Software (Drone)
Hardware Requirement
Challenges in Edge AI-Based Solar Cell Inspection System
Scope for Future Deployment
Conclusion
References
Chapter 11. Object Detection in Edge Environment: A Comparative Study of Algorithms and Use Cases
Introduction to Object Detection
Object Detection Algorithms
Traditional/Non-Neural Methods
Neural Network-Based Algorithms
Two-Stage Detectors
Single-Stage Detectors
Comparison between Traditional Algorithms and Neural Networks
Object Detection Environments
Image Source Environment
Object Detection Extraction Environment
Edge Computing
Object Detection Algorithms for Edge Computing Environment
SqueezeNet
MobileNet
ShuffleNet
NASNet
EdgeAI
Applications of Object Detection
Object Tracking
Robotic Vision
Self-Driving Cars
Surveillance
Smart City
Health Care
Object Detection Metrics
Challenges of Object Detection in an Edge Computing Environment
Research Directions
Conclusion
References
Chapter 12. Ambient Intelligence: An Emerging Innovation of Sensing and Service Systems
Introduction
Pervasive Computing - Backbone of Ambient Intelligence
Pervasiveness
Pervasive Computing in AmI
Ambient Intelligence in Health Care and Consumer Electronics
Human Psychology - Behaviorism - Futuristic Ambient Intelligent Systems
Behaviorism
Balanced Approach Model
AmI - Technology Dimension
AmI - Psychology Dimension
Ambient Intelligence - Healthcare Industry Transition
Technology Dimension
Psychology Dimension
AmI - Consumer Electronics
Technology Dimension
Psychology Dimension
Other Possible Applications of AmI
Social Issues and Research Prospects of AmI
Technology versus Ethics
Research Prospects
Conclusion
Acknowledgments
References
Index
