Machine Learning Modeling for Iout Networks: Internet of Underwater Things - Brossura

Aziz El-banna, Ahmad A.; Wu, Kaishun

Brossura

ISBN 10: 3030685667 ISBN 13: 9783030685669

Casa editrice: Springer Nature, 2021

Riassunto

This book discusses how machine learning and the Internet of Things (IoT) are playing a part in smart control of underwater environments, known as Internet of Underwater Things (IoUT). The authors first present seawater’s key physical variables and go on to discuss opportunistic transmission, localization and positioning, machine learning modeling for underwater communication, and ongoing challenges in the field. In addition, the authors present applications of machine learning techniques for opportunistic communication and underwater localization. They also discuss the current challenges of machine learning modeling of underwater communication from two communication engineering and data science perspectives.

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Informazioni sull?autore

Ahmad A. Aziz El-Banna received his Master’s degree in 2011 from Benha University and his Ph.D in 2014 from Egypt-Japan University of Science & Technology. Since June 2018, he has been a postdoctoral fellow at Shenzhen University, China. He also holds the position of an assistant professor at Benha University, Egypt. He served as a visiting researcher at Osaka University at Japan (2013–2014). His research interests include cooperative networking, MIMO, space-time coding, IoT, machine learning, and underwater communication.

 

Kaishun Wu received the Ph.D. degree in computer science and engineering from HKUST in 2011. After that, he worked as a Research Assistant Professor with HKUST. In 2013, he joined SZU as a Distinguished Professor. He has coauthored two books and published over 100 high quality research articles in international leading journals and primer conferences, such as IEEE TMC, IEEE TPDS, ACM MobiCom, and IEEE INFOCOM. He is also the inventor of 6 U.S. and over 90 Chinese pending patents. He is a fellow of IET. He received the 2012 Hong Kong Young Scientist Award and the 2014 Hong Kong ICT Awards: Best Innovation and 2014 IEEE ComSoc Asia–Pacific Outstanding Young Researcher Award.

Dettagli dell?edizione

• Editore: Springer Nature
• Data di pubblicazione: 2021
• Lingua: Inglese
• ISBN 10: 3030685667
• ISBN 13: 9783030685669
• Rilegatura: Copertina flessibile
• Numero edizione: 1
• Numero di pagine: 76
• Contatto del produttore: non disponibile
• Persona responsabile: CMN Printpool Inh. Mathis Neumann; https://www.also.com/ec/cms5/en_6000/6000/index.jsp; ; Friedrich-Karl-Str. 9; Hamburg; Germania