GMLFCN 2021 : Call for Book Chapters: Green Machine-Learning Protocols for Future Communication Networks
Call For Papers
------ Call for Book Chapters Proposal --------------------------------------
Title: Green Machine-Learning Protocols for Future Communication Networks
Editors: Saim Ghafoor (Letterkenny Institute of Technology, Ireland)
Mubashir Husain Rehmani (Munster Technological University, Cork, Ireland),
Publisher: CRC group, Taylor & Francis Group, USA.
Proposal Submission Deadline: July 1st, 2021
Notification of Proposal Acceptance: July 15th, 2021
Draft of Full Chapter Submission: November 15th, 2021
Notification of Chapter Acceptance: December 30th, 2021
Final Chapter Submission: January 30th, 2022
Machine learning (ML) has been used recently in many communication networks like wireless sensor networks, Internet-of-Things, cognitive radio networks, satellite communication, cloud/edge computing, software-defined networking, and machine-to-machine networks. It has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However, heavy resources are required to process and analyze the data which can be done either offline or using edge computing, which also requires heavy transmission rates to provide a timely response. The need here is to provide light-weight ML protocols that can process and analyze the data at run time and provide a timely and efficient response. The focus so far was on producing highly accurate models for these communication networks without considering energy consumption of these ML algorithms. In fact, these algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing, memory, communication, and storage.
Little effort has been made so far on reducing the energy consumption of these models and algorithms. For future scalable and sustainable network applications, efforts are required towards designing new ML protocols and modifying the existing ones, which consumes less energy i.e., green machine learning protocols. In other words, novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint.
This book will provide new and novel methods and protocols that should essentially be light-weight and energy-efficient for future communication networks.
The recommended topics include, but are not limited to the following,
• Recent advancements in Green Machine Learning for Future Communication networks.
• Green Deep learning and Neural networks models and protocols
• Green Reinforcement learning-based models and protocols
• Green Federated learning-based models and protocols
• Novel Hardware and Software design for Green Machine learning
• New simulation models for Green Machine learning for future communication networks
• Power models for Green Machine Learning communication networks
• Green Machine learning for Physical/MAC/Network layer communication protocols
• Resource management for Green Machine learning protocols
• Green Machine learning protocols for Beyond 5G and 6G communication networks
• Green Machine learning protocols for Cloud Communication
• Green Machine learning protocols for Cellular Communication
• Green Machine learning protocols for Internet-of-Things
• Green Machine learning protocols for Machine-to-Machine communication
• Green Machine learning protocols for Cognitive Radio networks
• Green Machine learning protocols for Satellite Communication
Authors are invited to submit a two (2) page proposal in MS-Word or PDF format until 1st of July 2021. Multiple proposals can be submitted by the same authors. The proposals should include the following,
Authors names and affiliation
Authors of accepted proposal will be notified by 15th July 2021.
All submitted chapters will be reviewed by at least two reviewers.
Contributors may also be requested to serve as reviewers for other book chapters.
Please send your book chapter proposals to Dr. Saim Ghafoor, cc’ing Dr. Mubashir.
Inquiries and submissions can be forwarded electronically to:
Saim Ghafoor (firstname.lastname@example.org, email@example.com )
Mubashir Husain Rehmani (firstname.lastname@example.org)
Thanks, and kind regards,
Dr. Saim Ghafoor
Associate Editor, Elsevier Computers & Electrical Engineering
Letterkenny Institute of Technology, Ireland
Email: email@example.com, firstname.lastname@example.org