Paperback. 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 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. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications. This book covers 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 IoT, the design considerations and challenges for green machine learning protocols for different future applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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T�tulo: Green Machine Learning Protocols for Future Communication Networks (Paperback)
Editor: Taylor & Francis Ltd, London
Fecha de publicaci�n: 2025
Idioma: Ingl�s
ISBN 10: 1032136871
ISBN 13: 9781032136875
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

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 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. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications.

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Saim Ghafoor (M’17, SM’19)

Saim Ghafoor received his Ph.D. in Computer Science from University College Cork, Ireland in 2018. He completed his M.S. in Computer Science and Engineering from Hanyang University, South Korea in 2010 and B.E in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan in 2005. Currently, he is working as an Assistant Lecturer at Atlantic Technological University (ATU), Donegal, Ireland. He worked at Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology (WIT), Waterford, Ireland as Post-Doctoral researcher from June 2018 to August 2020. He is currently an Associate Editor of the Elsevier, Computer and Electrical Engineering Journal and serving as a reviewer for many reputable journals and conferences. He co-organized a workshop on Terahertz wireless communication in IEEE INFOCOM 2019. He also received a best student paper award in International Conference on Disaster Management in 2017. He received best reviewer of the year award from Elsevier Computer and Electrical Engineering Journal in 2014 and 2015, and many reviewer recognitions certificates. His research interests are Terahertz and millimetre communication networks, autonomous and intelligent communication networks, cognitive radio networks and wireless sensor networks.

Personal Webpage: https://sites.google.com/view/saimghafoor

Email: saim.ghafoor@atu.ie

Mubashir Husain Rehmani (M’14-SM’15) received the B.Eng. degree in computer systems engineering from Mehran University of Engineering and Technology, Jamshoro, Pakistan, in 2004, the M.S. degree from the University of Paris XI, Paris, France, in 2008, and the Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently working as Lecturer in the Department of Computer Science, Munster Technological University (MTU), Ireland. Prior to this, he worked as Post Doctoral Researcher at the Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology (WIT), Waterford, Ireland. He also served for five years as an Assistant Professor at COMSATS Institute of Information Technology, Wah Cantt., Pakistan. He is serving as an Editorial Board Member of NATURE Scientific Reports. He is currently an Area Editor of the IEEE Communications Surveys and Tutorials. He served for three years (from 2015 to 2017) as an Associate Editor of the IEEE Communications Surveys and Tutorials. He served as Column Editor for Book Reviews in IEEE Communications Magazine. He is appointed as Associate Editor for IEEE Transactions on Green Communication and Networking. Currently, he serves as Associate Editor of IEEE Communications Magazine, Elsevier Journal of Network and Computer Applications (JNCA), and the Journal of Communications and Networks (JCN). He is also serving as a Guest Editor of Elsevier Ad Hoc Networks journal, Elsevier Future Generation Computer Systems journal, the IEEE Transactions on Industrial Informatics, and Elsevier Pervasive and Mobile Computing journal. He has authored/edited total eight books. Two books with Springer, two books published by IGI Global, USA, three books published by CRC Press – Taylor and Francis Group, UK, and one book with Wiley, U.K. He received “Best Researcher of the Year 2015 of COMSATS Wah” award in 2015. He received the certificate of appreciation, “Exemplary Editor of the IEEE Communications Surveys and Tutorials for the year 2015” from the IEEE Communications Society. He received Best Paper Award from IEEE ComSoc Technical Committee on Communications Systems Integration and Modeling (CSIM), in IEEE ICC 2017. He consecutively received research productivity award in 2016-17 and also ranked # 1 in all Engineering disciplines from Pakistan Council for Science and Technology (PCST), Government of Pakistan. He received Best Paper Award in 2017 from Higher Education Commission (HEC), Government of Pakistan. He is the recipient of Best Paper Award in 2018 from Elsevier Journal of Network and Computer Applications. He is the recipient of Highly Cited Researcher™ award thrice in 2020, 2021, and 2022 by Clarivate, USA. His performance in this context features in the TOP 1% by citations in the field of Computer Science and Cross Field in the Web of Science™ citation index. He is the ONLY Researcher from Ireland in the field of “Computer Science” who received this International prestigious award. In Oct 2022, he received Science Foundation Ireland’s CONNECT Centre’s Education and Public Engagement (EPE) Award 2022 for his research outreach work and being a spokesperson for achieving a work-life balance for a career in research.

Personal Webpage: https://sites.google.com/site/mubrehmani/

Email: Mubashir.rehmani@mtu.ie

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