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{ "item_title" : "Machine Learning Modeling for Iout Networks", "item_author" : [" Ahmad A. Aziz El-Banna", "Kaishun Wu "], "item_description" : "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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/068/566/3030685667_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Machine Learning Modeling for Iout Networks|Ahmad A. Aziz El-Banna

Machine Learning Modeling for Iout Networks : Internet of Underwater Things

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Overview

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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Details

  • ISBN-13: 9783030685669
  • ISBN-10: 3030685667
  • Publisher: Springer
  • Publish Date: May 2021
  • Dimensions: 9.21 x 6.14 x 0.16 inches
  • Shipping Weight: 0.26 pounds
  • Page Count: 63

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