Deep Learning in ad-hoc Wireless Networks

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)

Inhalt

Recent Deep Learning based Trust Solutions.- Smart Mobility Solutions: The Role of Deep Learning in Traffic Management.- A Survey of Routing Protocols for Low Power and Lossy IoT Network.- Generative Artificial Intelligence Using Deep Learning on Wireless Ad-Hoc Networks.