AI-Based Advanced Optimization Techniques for Edge Computing.

Format:

Book

Author/Creator:

Mohit, Kumar.

Contributor:

Srivastava, Gautam.

Singh, Ashutosh Kumar.

Dubey, Kalka.

Series:

Advances in learning analytics for intelligenct cloud-IOT systems

Language:

English

Physical Description:

1 online resource (481 pages)

Edition:

1st ed.

Place of Publication:

Wiley 2025

Summary:

The book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field. This book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime. This volume aims to encourage industry professionals to initiate a set of architectural strategies to solve open research computation challenges. The authors achieve this by defining and exploring emerging trends in advanced optimization algorithms, AI techniques, and fog and Edge technologies for IoT applications. Solutions are also proposed to reduce the latency of real-time applications and improve other quality of service parameters using adaptive optimization algorithms in fog and Edge paradigms. The book provides information on the full potential of IoT-based intelligent computing paradigms for the development of suitable conceptual and technological solutions using adaptive optimization techniques when faced with challenges. Additionally, it presents in-depth discussions in emerging interdisciplinary themes and applications reflecting the advancements in optimization algorithms and their usage in computing paradigms. Audience Researchers, industrial engineers, and graduate/post-graduate students in software engineering, computer science, electronic and electrical engineering, data analysts, and security professionals working in the fields of intelligent computing paradigms and similar areas.

Contents:

Series Page 1 Navigating Next-Generation Network Architecture: Unleashing the Power of SDN, NFV, NS, and AI Convergence 2 OctoEdge: An Octopus-Inspired Adaptive Edge Computing Architecture 3 Development of Optimized Machine Learning Oriented Models 4 Leveraging Multimodal Data and Deep Learning for Enhanced Stock Market Prediction 5 Context Dependent Sentiments Analysis Using Machine Learning 6 Thyroid Cancer Prediction Using Optimizations 7 An LSTM-Oriented Approach for Next Word Prediction Using Deep Learning 8 Churn Prediction in Social Networks Using Modified BiLSTM-CNN Model 9 Fog Computing Security Concerns in Healthcare Using IoT and Blockchain 10 Smart Agriculture Revolution: Cloud and IoT-Based Solutions for Sustainable Crop Management and Precision Farming 11 Greedy Particle Swarm Optimization Approach Using Leaky ReLU Function for Minimum Spanning Tree Problem 12 SDN Deployed Secure Application Design Framework for IoT Using Game Theory 13 Framework for PLM in Industry 4.0 Based on Industrial Blockchain 14 Machine Learning Enabled Smart Agriculture Classification Technique for Edge Devices Using Remote Sensing Platform 15 A Lightweight Intelligent Detection Approach for Interest Flooding Attack 16 An Internet of Vehicles Model Architecture with Seven Layers End User License Agreement

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9781394287048

1394287046

9781394287062

1394287062

9781394287055

1394287054

1-394-28705-4

OCLC:

1509787695