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Cognitive Computing for Big Data Systems Over IoT

Frameworks, Tools and Applications

  • Book
  • © 2018

Overview

Editors:
  1. Arun Kumar Sangaiah

    1. School of Computing Science and Engineering, VIT University, Vellore, India

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  2. Arunkumar Thangavelu

    1. School of Computing Science and Engineering, VIT University, Vellore, India

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  3. Venkatesan Meenakshi Sundaram

    1. Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Mangalore, India

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  • Explores domain knowledge and data reasoning technologies and cognitive methods using the Internet of Things (IoTs)
  • Focuses on the design of the best cognitive embedded data technologies to process and analyze the large amount of data collected via the IoT and aid good decision-making
  • Explores different perspectives of data analytics, ranging from cognitive design principles and best practices for IoT application development such as cyber-physical systems, context awareness, situation awareness, and ambient intelligence
  • Includes supplementary material: sn.pub/extras

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies (LNDECT, volume 14)

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Table of contents (15 chapters)

  1. Front Matter

    Pages i-xvi
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  2. Beyond Automation: The Cognitive IoT. Artificial Intelligence Brings Sense to the Internet of Things

    • Pijush Kanti Dutta Pramanik, Saurabh Pal, Prasenjit Choudhury
    Pages 1-37

  3. Cybercrimes Investigation and Intrusion Detection in Internet of Things Based on Data Science Methods

    • Ezz El-Din Hemdan, D. H. Manjaiah
    Pages 39-62

  4. Modelling and Analysis of Multi-objective Service Selection Scheme in IoT-Cloud Environment

    • Chinu Singla, Nitish Mahajan, Sakshi Kaushal, Amandeep Verma, Arun Kumar Sangaiah
    Pages 63-77

  5. Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning

    • Goran Klepac
    Pages 79-95

  6. Reliable Cross Layer Design for E-Health Applications—IoT Perspective

    • P. Sarwesh, N. Shekar V. Shet, K. Chandrasekaran
    Pages 97-113

  7. Erasure Codes for Reliable Communication in Internet of Things (IoT) Embedded with Wireless Sensors

    • C. Pavan Kumar, R. Selvakumar
    Pages 115-137

  8. Review: Security and Privacy Issues of Fog Computing for the Internet of Things (IoT)

    • Binara N. B. Ekanayake, Malka N. Halgamuge, Ali Syed
    Pages 139-174

  9. A Review on Security and Privacy Challenges of Big Data

    • Manbir Singh, Malka N. Halgamuge, Gullu Ekici, Charitha S. Jayasekara
    Pages 175-200

  10. Recent Trends in Deep Learning with Applications

    • K. Balaji, K. Lavanya
    Pages 201-222

  11. High-Level Knowledge Representation and Reasoning in a Cognitive IoT/WoT Context

    • Gian Piero Zarri
    Pages 223-262

  12. Applications of IoT in Healthcare

    • Prabha Susy Mathew, Anitha S. Pillai, Vasile Palade
    Pages 263-288

  13. Security Stipulations on IoT Networks

    • Sumod Sundar, S. Sumathy
    Pages 289-306

  14. A Hyper Heuristic Localization Based Cloned Node Detection Technique Using GSA Based Simulated Annealing in Sensor Networks

    • D Rajesh Kumar, A Shanmugam
    Pages 307-335

  15. Review on Analysis of the Application Areas and Algorithms used in Data Wrangling in Big Data

    • Chiranjivi Bashya, Malka N. Halgamuge, Azeem Mohammad
    Pages 337-353

  16. An Innovation Model for Smart Traffic Management System Using Internet of Things (IoT)

    • Amardeep Das, Prasant Dash, Brojo Kishore Mishra
    Pages 355-370

  17. Back Matter

    Pages 371-375
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Keywords

About this book

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge

The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective.

Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collectedthrough the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice.

This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

Editors and Affiliations

  • School of Computing Science and Engineering, VIT University, Vellore, India

    Arun Kumar Sangaiah, Arunkumar Thangavelu

  • Department of Computer Science and Engineering, National Institute of Technology, Surathkal, Mangalore, India

    Venkatesan Meenakshi Sundaram

Bibliographic Information

  • Book Title: Cognitive Computing for Big Data Systems Over IoT

  • Book Subtitle: Frameworks, Tools and Applications

  • Editors: Arun Kumar Sangaiah, Arunkumar Thangavelu, Venkatesan Meenakshi Sundaram

  • Series Title: Lecture Notes on Data Engineering and Communications Technologies

  • DOI: https://doi.org/10.1007/978-3-319-70688-7

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2018

  • Softcover ISBN: 978-3-319-70687-0Published: 10 January 2018

  • eBook ISBN: 978-3-319-70688-7Published: 30 December 2017

  • Series ISSN: 2367-4512

  • Series E-ISSN: 2367-4520

  • Edition Number: 1

  • Number of Pages: XVI, 375

  • Number of Illustrations: 30 b/w illustrations, 51 illustrations in colour

  • Topics: Computational Intelligence, Data Mining and Knowledge Discovery

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