Название: Deep Learning in Ad-Hoc Wireless Networks
Автор: Gokhan Altan, Ipek Abasikeleş Turgut
Издательство: Springer
Год: 2025
Страниц: 128
Язык: английский
Формат: pdf (true), epub
Размер: 23.9 MB

This book presents innovative applications of Deep Learning techniques in wireless ad-hoc networks, addressing critical challenges such as trust, routing, traffic management, and intrusion detection. By combining advanced AI models with real-world network scenarios, the chapters explore novel solutions for improving network reliability, security, and efficiency. Readers benefit from a multidisciplinary approach that bridges Deep Learning and wireless communication, offering both theoretical insights and practical frameworks. Targeting researchers, engineers, and graduate students, this work serves as a valuable resource for understanding and implementing deep learning strategies to optimize modern wireless systems. Whether improving IoT networks, securing controller area networks, or enabling smart mobility, the book provides actionable knowledge on Deep Learning applications for solving current and future challenges in ad-hoc wireless networks.

- Is a reference for researchers and data engineers developing enhanced management, security, and privacy features
- Proposes deep learning-based approaches using recent algorithms to present applications
- Addresses the application of recent Deep Learning algorithms on Wireless Ad-Hoc Networks (WANET)

The vulnerability of wireless communication to attacks, combined with the highly dynamic nature of VANETs and the stringent requirements for accuracy and speed, necessitates the development of specialized solutions for these networks. Trust systems have emerged as an important area of interest in both academic and industrial contexts, especially considering the limitations of cryptographic methods that primarily address external threats. While trust systems utilizing Machine Learning have been used for some time, the application of Deep Learning in this field is relatively new. This study presents an analysis of the algorithms used and their application areas, focusing on recent developments in the integration of deep learning approaches within trust systems. It is anticipated that the findings of this study will contribute to the future development of Deep Learning-based trust systems.

Contents:







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