Applied Edge AI: Concepts, Platforms, and Industry Use Cases
- Length: 318 pages
- Edition: 1
- Language: English
- Publisher: Auerbach Pub
- Publication Date: 2022-04-06
- ISBN-10: 0367702363
- ISBN-13: 9780367702366
- Sales Rank: #0 (See Top 100 Books)
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
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
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