DL-AWN 2024: Call for Book Chapters: "Deep Learning in Ad-hoc Wireless Networks"

Due to high number of Book Proposal Applications

Time Line has been extended.

This book addresses the application of recent Deep Learning algorithms on various issues with security, privacy, routing approaches, data transmission, and localization in Wireless Ad-Hoc Networks (WANET) which are susceptible to cyber-attacks, traffic, and/or mobility management. It proposes deep learning-based approaches using recent algorithms to present potential applications, and future trends, challenges, and sustainable technologies for WANET environments. Due to the inadequacy of existing solutions to cover the entire WANET analysis spectrum, the book utilizes deep learning architectures, which are used to classify, cluster, recognize, perceive, interpret, and model complex WANET data including images, network traffic, resource management, mobility management, and localization settings, to enhance the network performance and level of security and privacy of WANET. Deep Learning is applied to several WANET applications which include wireless sensor networks (WSN), meter reading transmission in smart grids, industrial IoT, and connected networks. The book serves as a reference for researchers, academics, and network engineers, computer engineers, data engineers who want to develop enhanced management, security and privacy features in the design of WANET systems.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal/conference/book. The following paper categories are welcome:

Deep Learning in Ad-hoc Wireless Networks is committed to providing a streamlined submission process, rapid review and publication, and a high level of author service at every stage. It is a free-of-charge, community-focussed journal publishing research from across all fields relevant to “Deep Learning in Ad-hoc Wireless Networks“, providing cutting-edge and state-of-the-art research findings to researchers, academicians, students, and engineers.

●     Our streamlined submission process ensures a swift turnaround time to publish your research rapidly while maintaining the highest peer-review standards.

●      We ensure that your research is highly discoverable and instantly available globally to everyone under Springer policies.

If you are interested in contributing to Deep Learning in Ad-hoc Wireless Networks, initially, the authors should get a confirmation for their chapter proposals from the Editors. Submissions will be welcomed at any point up until 15 January 2024, but if you are unable to submit a proposal before this date, please let us know as we may be able to be flexible.

Proposal Submission Page: HERE

List of Topics

Deep learning Applications

• Deep Reinforcement Learning
• Deep Belief Networks
• Deep Q-Learning
• Convolutional Neural Networks
• Deep Autoencoders
• Recurrent Neural Networks
• Deep Neural Networks
• Deep Extreme Learning Machines
• Multi-agent reinforcement Algorithms
• Long Short-term Memory Networks
• Deep Generative Models
• Fuzzy Q-learning
• Hierarchical Attention Networks
• Transformer Neural Networks

Deep Learning Applications on Wireless Ad-Hoc Networks      

• Vehicular Ad-Hoc Network (VANET)
• Mobile Ad-Hoc Network (MANET)
• Wireless Sensor Network (WSN)
• Wireless Mesh Network (WMN)
• Flying Ad-Hoc Network (FANET)
• Security Issues

                ● Intrusion Detection, Prevention and/or Mitigation Approaches

                ● Trust-Based Solutions

                ● Secure Data Transmission

                ● Other Security Issues

• Routing Approaches
• Link and/or Physical Layer Solutions
• Localization Problem
• Traffic and/or Mobility Management
• SDN-assisted Ad-Hoc Applications

Editors

Assoc. Prof. Dr. Gokhan ALTAN, Iskenderun Technical University, Türkiye

ORCID: https://orcid.org/0000-0001-7883-3131

Assoc. Prof. Dr. İpek ABASIKELES-TURGUT, Iskenderun Technical University, Türkiye

ORCID: https://orcid.org/0000-0002-5068-969X

Publication

All manuscripts submitted to “Deep Learning in Ad-hoc Wireless Networks” (DL-AWN 2024) are assessed according to the standard peer review procedures and are subject to all standard Springer policies.

Deadlines

Contact

All questions about submissions should be emailed to 

Editors: gokhan.altan@iste.edu.tr   ipek.abasikeles@iste.edu.tr