Edge Artificial Intelligence

Name: Edge Artificial Intelligence | Foundations, Techniques, and Applications
Brand: Wiley
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Foundations, Techniques, and Applications

Preeti Agarwal Anchit Bijalwan(Herausgeber*in)

Wiley (Verlag)

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Erschienen am 23. Oktober 2025

647 Seiten

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Beschreibung

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries.

Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency.

Readers will find this volume:

Dives deep into the world of edge AI with a comprehensive exploration covering foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, governance frameworks, optimization techniques, and real-world applications;
Offers practical guidance on implementing edge AI solutions effectively in various domains, including architecture design, development frameworks, deployment strategies, and optimization techniques;
Explores concrete examples of edge AI applications across diverse domains such as healthcare, industrial IoT, smart cities, and autonomous systems, providing insights into how edge AI is revolutionizing industries and everyday life;
Provides insights into emerging trends and technologies in the field of edge AI, including convergence with blockchain, augmented reality, virtual reality, autonomous systems, personalized experiences, and cybersecurity.

Audience

Researchers, AI experts, and industry professionals in the field of computer science, IT, and business management.

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Inhalt

Cover
Series Page
Title Page
Copyright Page
Contents
Preface
Part 1: Foundations and Core Concepts of Edge AI
Chapter 1 Edge AI Demystified: From Its Origins to Future Frontiers
1.1 Introduction
1.1.1 Edge AI Evolution
1.1.2 What is Edge AI, and How Does It Work?
1.1.3 Difference Between Edge AI and Cloud AI
1.2 Edge AI Ecosystem
1.2.1 Hardware Architecture
1.2.2 Operating Systems
1.2.3 Communication Protocols
1.2.4 Computational Paradigms
1.2.5 Mechanisms for Security and Data Protection
1.2.6 Applications
1.3 Benefits of Edge AI
1.3.1 Lower Latency and Real-Time Processing
1.3.2 Better Privacy and Security of Data
1.3.3 Scalability and Resource Efficiency
1.3.4 Energy Efficiency
1.3.5 Increased Reliability
1.3.6 Context-Aware Intelligence
1.3.7 Optimization of Bandwidth
1.3.8 Versatility Across Industries
1.3.9 Democratizing AI
1.3.10 Environmental Benefits
1.4 Different Deployment Architectures
1.4.1 Architecture Types
1.5 Hardware and Software Requirements for Edge AI
1.5.1 Hardware Requirements for Edge AI
1.5.2 Edge AI Software Requirements
1.6 Applications of Edge AI
1.7 Challenges in Edge AI
1.8 Open Research Issues
1.9 Future Research Directions for Edge-Driven IoT
1.10 Conclusion
References
Chapter 2 Optimizing Deep Learning Models for Edge Devices
2.1 Introduction
2.2 Background Review
2.3 Techniques, Algorithms and Frameworks
2.3.1 Quantization
2.3.2 Pruning
2.3.3 Model Optimization for Edge Devices
2.3.4 Knowledge Distillation
2.3.5 Layer Partitioning
2.3.6 DNN Partitioning and Right Sizing
2.3.7 Hashing
2.3.8 Hybrid Compression Techniques
2.3.9 SVD (Singular Value Decomposition)
2.3.10 CPU Optimization
2.4 Deploying Technology
2.5 Experimental Evaluation
2.6 Case Study
2.6.1 Smart Cities
2.6.2 Health Care and Remote Monitoring
2.6.3 Autonomous Driving
2.7 Challenges and Future Direction
2.8 Conclusion
References
Chapter 3 Role of Multi Objective Evolutionary Algorithms in Edge AI System Optimization
3.1 Introduction
3.2 Multi Objective Optimization in Edge AI
3.3 Challenges and Limitations
3.4 Future Directions
3.5 Conclusion
References
Chapter 4 The Convergence of Edge Computing, AI, and Blockchain: Challenges, Opportunities, and Future Prospects
4.1 Introduction
4.2 Understanding Edge Computing
4.3 Understanding Edge AI
4.4 Understanding Blockchain Technology
4.4.1 Key Features of Blockchain
4.4.2 Consensus Mechanisms
4.4.3 Blockchain Applications
4.4.4 Challenges of Blockchain
4.4.5 Future of Blockchain
4.5 Convergence of Edge AI and Blockchain
4.5.1 Data Privacy and Security
4.5.2 Decentralized Intelligence
4.5.3 Efficiency and Scalability
4.5.4 Transparency
4.6 Challenges in Convergence
4.7 Applications (IoT, Edge AI, and Blockchain for Sustainable Cities)
4.8 Future Prospects
4.9 Conclusion
References
Chapter 5 Network Optimization in Edge Computing
5.1 Introduction
5.2 Overview - What is Network Optimization?
5.3 Role of Edge Computing in Network Optimization/ Network Fundamentals
5.3.1 Reduced Latency
5.3.2 Bandwidth Optimization
5.3.3 Scalability
5.3.4 Enhanced Security and Privacy
5.3.5 Cost Efficiency
5.4 Challenges
5.4.1 Programmability
5.4.2 Data Abstraction
5.4.3 Service Management
5.4.4 Privacy & Security
5.4.5 Application Distribution
5.5 Strategies for Network Optimization
5.5.1 Edge Caching
5.5.2 Data Locality
5.5.3 Advanced Routing Techniques
5.6 Software-Defined Networking: Principles and Edge Environment Applications
5.6.1 Principles of SDN
5.6.2 SDN in Edge Environments
5.7 Network Function Virtualization (NFV)
5.7.1 Concepts and Applications in Edge Networks
5.7.2 Foundation and Core Principles of NFV
5.7.3 Components of NFV Architecture
5.7.4 Benefits of NFV
5.7.5 Satisfying Network Service Requirements on the Edge in Nearly Real-Time
5.7.6 Edge Oriented Network Protocols
5.7.7 Enhancements in Edge-Oriented Network Protocols
5.8 Application of Edge Computing in Network Optimization: A Comprehensive Overview
5.9 Case Study: Edge Computing in Network Optimization for Autonomous Vehicles
5.9.1 Edge Computing Deployment Architecture
5.9.2 Key Benefits and Outcomes
5.9.3 Challenges Encountered
5.10 Future Trends in Edge Computing for Network Optimization
5.10.1 Integration with 5G Networks
5.10.2 Artificial Intelligence at the Edge (Edge AI)
5.10.3 Federated Learning and Distributed Computing
5.10.4 Edge-to-Cloud Orchestration
5.10.5 The Assembled Zone of the Edge with IoT for Smart Cities
5.10.6 Energy Efficiency and Sustainability in Edge Computing
5.10.7 Security and Privacy on the Edge
5.10.8 Network Slicing and Customization with Edge Computing
5.11 Conclusion
References
Chapter 6 Comparative Analysis of Pruning Conventional Machine Learning or Deep Learning Frameworks Utilizing Discrete Wavelet Transform for Iris Biometrics
6.1 Introduction
6.2 Literature Review
6.3 Background Theories
6.3.1 Biometrics and Iris Recognition
6.3.2 Deep Learning in Biometrics
6.3.3 Discrete Wavelet Transform (DWT)
6.3.4 Principal Component Analysis (PCA)
6.3.5 Support Vector Machines (SVM)
6.4 Methodology
6.4.1 Datasets
6.4.1.1 IIT Delhi Iris Dataset (IITD)
6.4.1.2 MMU Iris Dataset
6.4.2 Preprocessing
6.4.3 Feature Extraction: CNN and ResNet
6.4.4 Classification
6.4.5 Model Evaluation
6.4.6 Implementation Details
6.5 Results
6.5.1 Performance Comparison
6.5.2 Classification Report Analysis
6.5.3 Confusion Matrix
6.5.4 ROC Curve
6.6 Problems Faced
6.7 Conclusion
6.8 Future Scope
Bibliography
Chapter 7 Convergence of K8s and Istio Deployment on Mission- Critical Environments
7.1 Introduction
7.2 Related Work
7.3 Requirement Analysis and Proposed HYINKUBIS Architecture Design
7.4 Experimental Evaluations
7.5 Conclusion
References
Part 2: Security, Ethics, and Trust in Edge AI
Chapter 8 Edge AI Security and Privacy: Threats, Solutions, and Best Practices
8.1 Introduction
8.1.1 Importance of Security and Privacy in Edge AI
8.1.2 Overview of Security and Privacy Issue in Edge AI
8.1.3 Types of Security Concerns in Edge Devices
8.2 Threats Landscape
8.2.1 Physical Tampering
8.2.2 Malware and Remote Attack
8.2.3 Adversial Attack
8.3 Vulnerabilities in Edge Devices
8.3.1 Device Level Vulnerabilities
8.3.2 Software Level Vulnerabilities
8.3.3 Network Level Vulnerabilities
8.4 Regulatory and Compliance Issues
8.4.1 Overview of Relevant Regulations
8.4.2 Compliance Challenges for Edge Devices
8.4.3 Best Practices for Compliance
8.5 Best Practices for Securing Edge Devices
8.5.1 Regular Monitoring and Threat Detection
8.5.2 Secure Firmware and Software Updates
8.5.2.1 Organizational Protection Schemes
8.5.2.2 OTA Updates with Security Layers
8.5.3 Role of Cryptographic Methods
8.5.4 Minimal Data Exposure: Cleaning and Masking
8.5.5 Trusted Hardware: TPMs and Secure Enclaves
8.5.6 THASSA
8.6 Emerging Trends and Technologies in Edge AI Security
8.6.1 Emerging Technologies
8.6.2 Anticipated Security Challenges
8.6.3 Recommendation for Enhancing Security
8.7 Conclusion
References
Chapter 9 Edge AI in Cybersecurity
9.1 Introduction to Edge AI in Cybersecurity
9.1.1 The Evolution of Cybersecurity Needs
9.1.2 Fundamentals of Edge AI
9.1.3 Key Components of Edge AI
9.1.3.1 Key Features of Hardware Accelerators
9.1.3.2 Types of Hardware Accelerators
9.1.4 Role of Edge AI in Cybersecurity
9.1.5 Cybersecurity Challenges in a Connected World
9.1.6 Applications of Edge AI in Cybersecurity
9.1.7 Future Directions
9.2 Conclusion
References
Chapter 10 Database-Guard Artificial Intelligence: Revolutionizing Database Security with Artificial Intelligence
10.1 Introduction
10.1.1 Problem Statement
10.1.2 Objectives
10.1.3 Significance of the Study
10.2 Background and Literature Review
10.2.1 Evolution of Database Security
10.2.2 Traditional Database Security Mechanisms and their Limitations
10.2.3 Emerging Threats to Database Security
10.2.4 AI in Database Security: A Paradigm Shift
10.3 DB-GuardIA Architecture and Components
10.3.1 Adaptive Threat Detection Layer (ATDL)
10.3.1.1 Overview of ATDL
10.3.1.2 Key Features of ATDL
10.3.1.3 Workflow of ATDL
10.3.2 Graph Neural Network-Based Anomaly Detection (GNN-AD)
10.3.2.1 Overview of GNN-AD
10.3.2.2 Key Features of GNN-AD
10.3.2.3 Workflow of GNN-AD
10.3.3 AI-Driven Dynamic Encryption Layer (AIDEL)
10.3.3.1 Overview of AIDEL
10.3.3.2 Key Features of AIDEL
10.3.3.3 Workflow of AIDEL
10.3.4 Self-Healing Database Layer (SHDL)
10.3.4.1 Overview of SHDL
10.3.4.2 Key Features of SHDL
10.3.4.3 Workflow of SHDL
10.3.5 Insider Threat Management Module (ITMM)
10.3.5.1 Overview of ITMM
10.3.5.2 Key Features of ITMM
10.3.5.3 Workflow of ITMM
10.3.6 Explainable AI (XAI) Module
10.3.6.1 Overview of XAI Module
10.3.6.2 Key Features of XAI Module
10.3.7 Behavioral DNA Profiling (BDP)
10.3.7.1 Overview of BDP
10.3.7.2 Key Features of BDP
10.3.7.3 Workflow of BDP
10.3.8 Integration with Cloud Services
10.3.8.1 Overview of Integration with Cloud Services
10.3.8.2 Workflow of Integration with Cloud Services
10.3.9 Blockchain Audit Trail (BBAT)
10.3.9.1 Overview of Blockchain Audit Trail (BBAT)
10.3.9.2 List of Features of Blockchain Audit Trail (BBAT)
10.3.9.3 Workflow of Blockchain Audit Trail (BBAT)
10.4 Workflow of DB-GuardIA NGDB in Action (Layer by Layer)
10.5 Comparative Analysis with Existing Database Security Frameworks
10.6 Conclusion
Bibliography
Chapter 11 Edge-AI Ledger Docs: Revolutionizing Document Storage and Verification with Blockchain
11.1 Introduction
11.2 Literature Review
11.3 Blockchain Basics
11.3.1 Types of Blockchain
11.3.2 Key Features
11.3.3 Document Verification
11.4 Proposed System
11.5 Challenges and Limitations
11.6 Use of AI in Document Verification
11.6.1 Blockchain-AI Benefits
11.7 Case Studies
11.8 Future Directions
11.9 Conclusion
References
Chapter 12 A Lightweight Lie Detector for Resource Constraint Devices
12.1 Introduction
12.2 Literature Survey
12.3 Proposed Methodology
12.3.1 Setup
12.3.2 Dataset
12.3.3 Feature Extraction
12.3.3.1 Facial Features and Iris Detection
12.3.3.2 Posture Detection
12.3.3.3 Feature Coordinate Normalization
12.3.4 Deep Learning
12.4 Conclusion
12.5 Future Scope
Acknowledgement
References
Chapter 13 AIPendant: An Efficient and Lightweight Threat Detection System for Real-Time Personal Safety
13.1 Introduction
13.1.1 Background
13.1.2 Related Works
13.2 Methodology
13.2.1 Model Selection: Mobile NetV2
13.2.2 Dataset: HOD Benchmark Dataset
13.2.3 System Integration with Telegram
13.2.4 Model Training
13.3 Results and Discussion
13.3.1 Model Performance
13.3.2 Comparison with Existing Models
13.3.3 SOS Alert System Efficiency
13.3.4 Visualization of Model Predictions
13.4 Conclusion
References
Part 3: Edge AI in Industry and Domain-Specific Applications
Chapter 14 Edge AI for Connected & Automated Vehicles: Opportunities & Challenges
14.1 Introduction
14.2 Applications of Edge AI in CAV
14.2.1 Environmental Perception
14.2.2 Path Planning and Autonomous Navigation
14.2.3 Vehicular Edge Computing and Intelligence
14.2.4 Vehicle-to-Everything (V2X) Communication
14.3 Hardware and Tool Chains for Edge AI Innovation
14.3.1 Tool Chains
14.3.1.1 TensorFlow Lite Micro (TFLM)
14.3.1.2 µTensor
14.3.1.3 µTVM
14.3.1.4 CMSIS-NN
14.3.1.5 Edge Impulse EON Compiler
14.3.1.6 STM32Cube.AI and Nano Edge AI Studio
14.3.1.7 Tiny Engine
14.3.1.8 Other Available Platforms
14.4 Edge AI Led Architecture in CAV
14.4.1 Vehicular Edge Layer
14.4.2 Distributed Edge Computing Layer
14.4.3 Cloud Computing Layer
14.5 Future Trends & Open Challenges
14.5.1 Fusion of Physical and Digital Vehicular Environments
14.5.2 Integration of Generative AI in CAV
14.5.3 Hardware-Software Co-Design
14.5.4 Infrastructure Advancements: CAV-Road- Hybrid Cloud Integration
14.5.5 Edge AI Models for CAVs
14.5.6 Data Privacy & Security
14.6 Conclusion
References
Chapter 15 The Role of Edge Computing in Enhancing Autonomous Vehicle Performance
15.1 Introduction: The Convergence of Autonomous Vehicle Technology and Edge Computing
15.1.1 Historical Evolution and Current Trends in Autonomous Vehicles
15.1.2 Evolution from Levels 0 to 5 of Automation
15.1.3 Current Global Trends in AV Technology
15.1.4 Challenges in Autonomous Vehicle Deployment
15.2 Technological Framework for Autonomous Vehicles: The Role of Edge Computing and AI
15.2.1 Interaction Between AVs and Edge Computing Infrastructure
15.2.2 Artificial Intelligence and Machine Learning in AVs
15.2.3 Software-Defined Networking (SDN) and Its Role in AV Networks
15.3 Real-Time Data Processing and Edge Computing: Handling the Data Deluge
15.3.1 Sensor Data Fusion and Real-Time Decision-Making
15.3.2 Challenges in Real-Time Data Processing
15.4 Algorithms in Autonomous Vehicle Decision-Making
15.4.1 Reinforcement Learning in AV Navigation
15.4.2 Deep Learning Models for Object Detection and Navigation
15.5 Security, Privacy, and Trust in Vehicular Edge Computing
15.5.1 Security Challenges in AV Networks
15.6 Conclusion and Future Research Directions
15.6.1 Visionary Outlook on Future Technologies
15.6.2 Future Research Topics
References
Chapter 16 Applying Edge AI in the Environment of Industrial Internet of Things (IIoT)
16.1 Internet of Things in Industries
16.1.1 Comparing IoT with IIoT
16.1.2 Industrial Internet of Things (IIoT)
16.2 Edge AI
16.2.1 Why to Opt for Edge AI
16.3 Edge AI vs Distributed AI vs Cloud AI
16.3.1 Edge AI vs Distributed AI
16.3.2 Edge AI Versus Cloud AI
16.4 Benefits of Edge AI
16.5 Edge AI and IIoT - Use Cases and Applications
16.6 Sample Datasets to Experiment Applying Edge AI in IIoT Domain
References
Chapter 17 Healthcare Applications of Edge AI
17.1 Introduction
17.2 Literature Survey
17.3 Methodology
17.4 Result and Discussion
17.5 Use Cases of Edge AI in Healthcare
17.6 Conclusion and Future Scope
Bibliography
Chapter 18 Design and Enhance of Data Analysis in Healthcare System with AutoML Business Intelligence Technology
18.1 Introduction
18.2 Related Work
18.3 Methodology
18.4 Results and Discussion
18.5 Conclusion and Future Scope
References
Chapter 19 A Case Study from Internet of Things to Edge AI with Challenges in Industry 4.0
19.1 Introduction
19.2 Edge AI: An IoT Method for Industry 4.0
19.3 Edge AI Deployments
19.4 Simulation Results
19.5 Conclusions and Future Scope
References
Chapter 20 Edge AI and Blockchain: A Synergy for Water Body Preservation
20.1 Introduction
20.2 Background
20.2.1 Blockchain's Introduction
20.2.2 Blockchain's Characteristics
20.2.3 Consensus Mechanism
20.2.4 Blockchain's Platforms
20.3 Ethereum
20.3.1 Smart Contract
20.3.2 Vulnerability Detection Tools to Check the Performance of SCs
20.4 Water Quality Metrics
20.4.1 Sensors
20.5 IBM Watson AI Tool
20.5.1 ML Algorithms
20.5.2 Performance Metrics
20.6 Proposed Model
20.7 Conclusion
References
Chapter 21 Enhancing Stadium Safety Through AI-Based Detection of Violent Incidents with 360-Degree Cameras
21.1 Introduction
21.2 Related Works
21.2.1 Object Detection and Real-Time Processing Systems
21.2.2 Deep Learning Architectures for Violence Detection
21.2.3 Multi-Modal Surveillance Integration
21.2.4 Real-Time Analysis in Stadium Environments
21.2.5 Audiovisual Integration and Feature Extraction
21.3 Methodology
21.3.1 System Overview
21.3.2 Video Preprocessing Module
21.3.2.1 Video Preprocessing
21.3.2.2 Modified YOLOv8 Detection
21.3.3 Violence Classification Module
21.4 Experiment
21.4.1 Project Experiments
21.4.2 Results
21.5 Conclusion
Bibliography
Part 4: Optimization and Intelligent Decision- Making with Edge AI
Chapter 22 Design of a Deep Reinforcement Learning Approach for Optimization of Task Offloading and Resource Allocation for Edge Computing Networks
22.1 Introduction
22.2 Proposed System
22.2.1 Task Model
22.2.2 Computational Model
22.2.3 Cloud Computing
22.2.4 Overview of DDQN-Based Task Offloading Framework
22.2.5 Task Offloading-Resource Allocation Using a DDQN-Based Approach
22.2.6 Action
22.2.7 DDQN Framework for Task Offloading
22.2.8 Performance Evaluation
22.3 Results and Discussions
22.4 Conclusion and Future Work
Bibliography
Chapter 23 Optimizing Evacuations Using Agent-Based Modeling
23.1 Introduction
23.2 Literature Review
23.3 Methodology
23.3.1 Design Criteria
23.3.2 Data Collection and Sampling Technique
23.3.3 Data Processing and Analysis
23.3.4 Validation and Sensitivity Analysis
23.3.5 Simulation and Experimentation
23.4 Results and Discussion
23.5 Conclusion
Acknowledgements
References
Chapter 24 CDLPP: Optimized Multi-Drone Path Planning in Evolving Environments Using Machine Learning for Object Detection
24.1 Introduction
24.2 Methodology
24.3 Related Work
24.4 Results
24.5 Conclusion
References
Chapter 25 Development of Low-Cost Road Surface Classification System Using Acceleration Sensors on Motorcycles
25.1 Introduction
25.2 The Proposed System
25.2.1 The Block Diagram and Operating Principle
25.2.2 Classification Model
25.2.3 Classification Algorithm
25.3 Experiment Setup and Results
25.3.1 Experiment Setup
25.3.2 Results
25.4 Result Evaluation
25.5 Conclusions
Acknowledgements
References
Chapter 26 Revolutionizing Education with Edge AI: Exploring Its Role, Impact, and Future Potential
26.1 Introduction
26.1.1 Evolution of Technology in Education
26.1.2 Significance of Localized, Real-Time Data Processing in Education
26.2 Current Role of Technology in Education
26.2.1 Role of AI and Computing in Education
26.2.2 Role of Cloud Computing in Education
26.2.2.1 Limitations of Cloud-Based Systems
26.2.2.2 Problems in Under-Resourced Regions
26.2.3 The Shift towards Edge Computing in Education
26.3 Role of Edge AI in Enhancing Learning Experiences
26.3.1 Personalized and Adaptive Learning
26.3.1.1 How Edge AI Can Deliver Customized Content and Real-Time Feedback
26.3.1.2 Case Studies of Adaptive Learning Platforms Leveraging Edge Computing
26.3.2 Real-Time Data Processing for Immersive Learning
26.3.2.1 Use of Edge Computing in AR/VR to Enable Real-Time Feedback
26.3.2.2 Enhancing STEM Education through Real-World Simulations Using Edge AI
26.3.3 Enhancing Collaboration in Remote Learning
26.3.3.1 Peer-to-Peer Networking and Decentralized Communication through Edge Devices
26.3.3.2 Impact on Group Projects, Collaborative Assignments, and Remote Labs
26.3.4 Supporting Special Education Needs
26.3.4.1 How Edge AI Enhances Accessibility (Speech-to-Text, Real-Time Translation, Behavior Tracking)
26.3.4.2 Assistive Technology Supported by Edge Computing for Students with Disabilities
26.4 Infrastructure and Implementation of Edge Computing in Education
26.4.1 Edge Networks in Educational Institutions
26.4.1.1 Architecture of Edge Networks in Schools and Universities
26.4.2 Deployment Strategies for Edge Devices
26.4.2.1 Use of Mobile and Wearable Devices for Educational Purposes
26.4.2.2 Smart Classrooms and IoT-Based Learning Environments
26.4.3 Challenges of Integrating Edge AI with Existing IT Infrastructure
26.5 Case Studies and Real-World Applications
26.5.1 Case Study 1: Edge AI in STEM Education
26.5.2 Case Study 2: Edge Computing for Remote and Rural Education
26.5.3 Case Study 3: AI-Driven Tutoring Systems
26.6 The Future of Edge AI and Edge Computing in Education
26.6.1 Predictions and Trends
26.6.2 The Role of 5G in Enhancing Edge Computing for Education
26.6.3 Emerging Technologies Complementing Edge Computing
26.6.4 Long-Term Impact on Educational Pedagogy
26.6.4.1 Transforming the Role of Educators and Redefining Traditional Teaching Methods
26.6.4.2 New Forms of Assessment and Student- Teacher Interaction Enabled by Real- Time AI Systems
26.7 Conclusion
References
Index
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