Call for Chapters: Springer Book “Deep Learning in Ad-hoc Wireless Networks”

By gokhan, 8 Eylül 2023

Deep Learning in Ad-hoc Wireless Networks

Dear Colleague,

We hope this email finds you well.

We are writing to let you know that our Springer Book is currently welcoming submissions to the Topical Book “Deep Learning in Ad-hoc Wireless Networks”, Edited by Assoc. Prof. Dr. Gokhan ALTAN and Assoc. Prof. Dr. İpek ABASIKELEŞ-TURGUT. We hope you will consider this Book as an outlet for a future research paper.

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.

The included Deep Learning Algorithms are listed below.

•           Deep Reinforcement Learning
•           Deep Q-Learning
•           Deep Belief Networks
•           Convolutional Neural Networks
•           Transfer Learning on pre-trained architectures
•           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

The types of Wireless Ad-Hoc Networks are Vehicular Ad-Hoc Networks (VANET), Mobile Ad-Hoc Networks (MANET), Wireless Sensor Networks (WSN), Wireless Mesh Networks (WMN), Flying Ad-Hoc Networks (FANET) etc. The potential applications areas of Deep Learning in Wireless Ad-Hoc Networks are listed below:

•           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

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


Chapter proposalsJanuary 15, 2024
Decisions from editorsFebruary 15, 2024
Full submission of chaptersMarch 15, 2024
Feedback of reviewsMarch 31, 2024
Revised chapter submissionApril 20, 2024
Final acceptance notificationsMay 15, 2024

Chapter proposals can either include reviews or research papers. Research papers should include Sections on “Materials and Methods”, ”Experimental Setup”, “Experimental Results”, and “Discussion and Conclusion”. Review papers should include highlights, the necessity of the review, the advantages and disadvantages of the title, and the discussions.

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

This Book is a great opportunity to highlight this important area of research, and we hope you will be able to contribute.

Please don’t hesitate to let us know if you have any questions.

Kind regards,


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

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