Название: Green Machine Learning Protocols for Future Communication Networks
Автор: Saim Ghafoor, Mubashir Husain Rehmani
Издательство: CRC Press
Год: 2024
Страниц: 223
Язык: английский
Формат: pdf (true)
Размер: 12.6 MB

Machine Learning (ML) 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 but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight Machine Learning protocols that can process and analyze the data at run time and provide a timely and efficient response. 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. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these Machine Learning algorithms.

For future scalable and sustainable network applications, efforts are required toward designing new Machine Learning protocols and modifying the existing ones, which consume 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. To realize the green Machine Learning protocols, this book presents different aspects of green Machine Learning for future communication networks. This book highlights mainly the green Machine Learning protocols for cellular communication, federated learning-based models, and protocols for Beyond Fifth Generation networks, approaches for cloud-based communications, and Internet-of-Things (IoT). This book also highlights the design considerations and challenges for green Machine Learning protocols for different future applications.

There are many books on Machine Learning, Deep Learning, and Federated Learning and their modeling and accuracy. However, none of the books are discussing the green effect requirement of Machine Learning algorithms, their significance, and their suitability for future 6th Generation (6G) communication. This is the first book presenting the green Machine Learning communication aspects for applications like cellular communication, cloud and data center communication, and Internet-of-Things. This book also highlights the challenges for different communication networks for 6G communications using Green Machine Learning mechanisms.




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