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- Дата: 27-08-2023, 01:59
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![](/uploads/posts/2023-08/thumbs/1693090837_green-machine-learning-protocols-for-future-communication-networks.jpg)
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.