Internet of Intelligent Things (IoIT): A Large-Scale Evaluation

Antreas Dionysiou

ISBN 10: 6200000441 / ISBN 13: 9786200000446

Published by LAP LAMBERT Academic Publishing, 2019

About this Item

Internet of Intelligent Things (IoIT): A Large-Scale Evaluation | Antreas Dionysiou | Taschenbuch | 76 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200000446 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Seller Inventory # 116440644

Bibliographic Details

• Title: Internet of Intelligent Things (IoIT): A Large-Scale Evaluation
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 6200000441
• ISBN 13: 9786200000446
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 131 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Internet of Things (IoT) and Machine Learning (ML) have been two among the most popular research topics for the last two decades. The benefits that these two technologies offer are indeed prominent. Nevertheless, the combination of these two breakthrough technologies can even bring more groundbreaking advantages to society, also transforming the classic IoT to an enhanced version called Internet of Intelligent Things (IoIT). In this paper, a large-scale evaluation of this enhanced concept is conducted, analyzing all of its core aspects, as well as explaining the advantages and potentials of this groundbreaking concept. Nonetheless, many challenges, that may act as an impediment on the benefits received through the IoIT, exist. In our large-scale evaluation, these challenges are detected and carefully examined, also suggesting ways to effectively overcome them. Data Mining (DM) is the process of extracting useful insights from large volumes of data. In this paper, we explain how ML- based DM techniques and methodologies could be utilized for the effective and efficient extraction of insights and valuable information from complex sequential data volumes.