Screen LibraryGreen machine learning protocols for future communication networks /
"Machine Learning 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 transmi...
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| Group Author: | ; |
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| Published: |
CRC Press,
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| Publisher Address: | Boca Raton, FL : |
| Publication Dates: | 2024. |
| Literature type: | Book |
| Language: | English |
| Edition: | First edition. |
| Subjects: | |
| Summary: |
"Machine Learning 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 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 towards 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, in this book, different aspects of green machine learning for future communication networks are presented. This book highlights mainly the green machine learning protocols for cellular communication, federated learning-based models and protocols for beyond 5th-generation networks, approaches for cloud-based communications, and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications"-- |
| Carrier Form: | xxii, 200 pages : illustrations ; 24 cm |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: |
9781032136851 1032136855 9781032136875 1032136871 |
| Index Number: | TK5102 |
| CLC: | TN926-05 |
| Call Number: | TN926-05/G797 |
| Contents: | Green machine learning for cellular networks / Saad Aslam, Houshyar Honar Pajooh, Muhammad Nadeem and Fakhrul Alam -- Green machine learning protocols for cellular communication / Mamoon M. Saeed, Elmustafa Sayed Ali, Rashid A. Saeed and Mohammad Abdul Azim -- Green federated learning-based models and protocols / Afaf Taik, Amine Abouaomar and Soumaya Cherkaoui |