Edge Intelligent Computing Systems in Different Domains (SpringerBriefs in Computer Science)

✍ Scribed by Benedetta Picano, Romano Fantacci

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 98
• Category: Library

✦ Synopsis

This book provides a comprehensive and systematic exploration of next-generation Edge Intelligence (EI) Networks. It delves deep into the critical design considerations within this context, emphasizing the necessity for functional and dependable interactions between networking strategies and the diverse application scenarios. This should help assist to encompass a wide range of environments.

This book also discusses topics such as resource optimization, incentive mechanisms, channel prediction and cutting-edge technologies, which includes digital twins and advanced machine learning techniques. It underscores the importance of functional integration to facilitate meaningful collaborations between networks and systems, while operating across heterogeneous environments aiming support novel and disruptive human-oriented services and applications. Valuable insights into the stringent requirements for intelligence capabilities, communication latency and real-time response are discussed. This characterizes the new EI era, driving the creation of comprehensive cross-domain architectural ecosystems that infuse human-like intelligence into every aspect of emerging EI systems.

This book primarily targets advanced-level students as well as postdoctoral researchers, who are new to this field and are searching for a comprehensive understanding of emerging EI systems. Practitioners seeking guidance in the development and implementation of EI systems in practical contexts will also benefit from this book.

✦ Table of Contents

Preface
Contents
List of Figures
1 Emerging Technologies for Edge Intelligent Computing Systems
1.1 Introduction
1.2 Edge Intelligence Paradigm
1.3 Federated Learning Framework
1.4 Semantic Communications
2 Offloading Methodologies for Air-Ground Edge Intelligent Computing Systems
2.1 Introduction
2.2 Problem Statement
2.2.1 System Scenario
2.2.2 Problem Formulation
2.3 Stochastic Network Calculus Principles
2.4 End-to-End Stochastic Bound
2.5 Tasks Offloading Scheme
2.5.1 Flows Preference List
2.5.2 Computational Nodes Preference List
2.5.3 Stability Analysis
2.6 Performance Analysis
2.7 Summary
Appendix
Appendix
3 Edge Intelligent Computing Enabled Federated Learning in 6G Wireless Systems
3.1 Introduction
3.2 Federated Learning with an Ideal Channel
3.3 Federation Learning in Actual Scenarios
3.3.1 Users' Revenue Model
3.3.2 Problem Formulation
3.4 Channel Performance Prediction Empowered by Artificial Intelligence
3.5 Matching Theory for Devices Selection
3.6 Performance Analysis
3.7 Summary
4 Edge Intelligent Computing in Aqua Environments
4.1 Introduction
4.2 Intelligent Data Communication Framework
4.3 Federated Learning for Ground–Aqua Environments
4.3.1 Channels and Computation Modeling
4.3.2 Problem Formulation
4.4 Underwater Semantic Communications
4.5 Performance Analysis
4.6 Summary
5 Application of the Digital Twin Technology in Novel Edge Intelligent Computing Systems
5.1 Introduction
5.2 Problem Outline
5.2.1 Physical System Model
5.2.2 Digital Layer Integration
5.2.3 Problem Formulation
5.3 DT UAV-MEC Offloading Framework
5.3.1 AI-Empowered DTs Congestion Monitoring
5.3.2 Three-Dimensional Matching Game for Tasks Offloading
5.4 Performance Analysis
5.5 Democratized Digital Twin Technology for Industrial Edge Intelligent Computing Systems
5.5.1 Motivation
5.5.2 The Dem-AI Framework
5.5.3 DT Architecture and Functional View
5.5.4 Experimental Results
5.6 Summary
Bibliography