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BIG DATA ANALYTICS FOR INTERNET OF THINGS

Discover the latest developments in IoT Big Data with a new resource from established and emerging leaders in the field

Big Data Analytics for Internet of Things delivers a comprehensive overview of all aspects of big data analytics in Internet of Things (IoT) systems. The book includes discussions of the enabling technologies of IoT data analytics, types of IoT data analytics, challenges in IoT data analytics, demand for IoT data analytics, computing platforms, analytical tools, privacy, and security.

The distinguished editors have included resources that address key techniques in the analysis of IoT data. The book demonstrates how to select the appropriate techniques to unearth valuable insights from IoT data and offers novel designs for IoT systems.

With an abiding focus on practical strategies with concrete applications for data analysts and IoT professionals, Big Data Analytics for Internet of Things also offers readers:

  • A thorough introduction to the Internet of Things, including IoT architectures, enabling technologies, and applications
  • An exploration of the intersection between the Internet of Things and Big Data, including IoT as a source of Big Data, the unique characteristics of IoT data, etc.
  • A discussion of the IoT data analytics, including the data analytical requirements of IoT data and the types of IoT analytics, including predictive, descriptive, and prescriptive analytics
  • A treatment of machine learning techniques for IoT data analytics

Perfect for professionals, industry practitioners, and researchers engaged in big data analytics related to IoT systems, Big Data Analytics for Internet of Things will also earn a place in the libraries of IoT designers and manufacturers interested in facilitating the efficient implementation of data analytics strategies.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. List of Contributors
  5. List of Abbreviations
  6. 1 Big Data Analytics for the Internet of Things
  7. 2 Data, Analytics and Interoperability Between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3)
    1. 2.1 Context
    2. 2.2 Models in the Background
    3. 2.3 Problem Space: Are We Asking the Correct Questions?
    4. 2.4 Solutions Approach: The Elusive Quest to Build Bridges Between Data and Decisions
    5. 2.5 Avoid This Space: The Deception Space
    6. 2.6 Explore the Solution Space: Necessary to Ask Questions That May Not Have Answers, Yet
    7. 2.7 Solution Economy: Will We Ever Get There?
    8. 2.8 Is This Faux Naïveté in Its Purest Distillate?
    9. 2.9 Reality Check: Data Fusion
    10. 2.10 “Double A” Perspective of Data and Tools vs. The Hypothetical Porous Pareto (80/20) Partition
    11. 2.11 Conundrums
    12. 2.12 Stigma of Partition vs. Astigmatism of Vision
    13. 2.13 The Illusion of Data, Delusion of Big Data, and the Absence of Intelligence in AI
    14. 2.14 In Service of Society
    15. 2.15 Data Science in Service of Society: Knowledge and Performance from PEAS
    16. 2.16 Temporary Conclusion
    17. References
  8. 3 Machine Learning Techniques for IoT Data Analytics
    1. 3.1 Introduction
    2. 3.2 Taxonomy of Machine Learning Techniques
    3. References
  9. 4 IoT Data Analytics Using Cloud Computing
    1. 4.1 Introduction
    2. 4.2 IoT Data Analytics
    3. 4.3 Cloud Computing for IoT
    4. 4.4 Cloud‐Based IoT Data Analytics Platform
    5. 4.5 Machine Learning for IoT Analytics in Cloud
    6. 4.6 Challenges for Analytics Using Cloud
    7. 4.7 Conclusion
    8. References
  10. 5 Deep Learning Architectures for IoT Data Analytics
    1. 5.1 Introduction
    2. 5.2 DL Architectures
    3. 5.3 Conclusion
    4. References
  11. 6 Adding Personal Touches to IoT
    1. 6.1 Introduction
    2. 6.2 Enabling Technologies for BDA of IoT Systems
    3. 6.3 Personalizing the IoT
    4. 6.4 Related Work
    5. 6.5 User Sensitized IoT Architecture
    6. 6.6 The Tweaked Data Layer
    7. 6.7 The Personalization Layer
    8. 6.8 Concerns and Future Directions
    9. 6.9 Conclusions
    10. References
  12. 7 Smart Cities and the Internet of Things
    1. 7.1 Introduction
    2. 7.2 Development of Smart Cities and the IoT
    3. 7.3 The Combination of the IoT with Development of City Architecture to Form Smart Cities
    4. 7.4 How Future Smart Cities Can Improve Their Utilization of the Internet of All Things, with Examples
    5. 7.5 Conclusion
    6. References
  13. 8 A Roadmap for Application of IoT‐Generated Big Data in Environmental Sustainability
    1. 8.1 Background and Motivation
    2. 8.2 Execution of the Study
    3. 8.3 Proposed Roadmap
    4. 8.4 Identification and Prioritizing the Barriers in the Process
    5. 8.5 Conclusion and Discussion
    6. References
  14. 9 Application of High‐Performance Computing in Synchrophasor Data Management and Analysis for Power Grids
    1. 9.1 Introduction
    2. 9.2 Applications of Synchrophasor Data
    3. 9.3 Utility Big Data Issues Related to PMU‐Driven Applications
    4. 9.4 Big Data Analytics Platforms for PMU Data Processing
    5. 9.5 Conclusions
    6. References
  15. 10 Intelligent Enterprise‐Level Big Data Analytics for Modeling and Management in Smart Internet of Roads
    1. 10.1 Introduction
    2. 10.2 Fully Convolutional Deep Neural Network for Autonomous Vehicle Identification
    3. 10.3 Experimental Setup and Results
    4. 10.4 Practical Implementation of Enterprise‐Level Big Data Analytics for Smart City
    5. 10.5 Conclusion
    6. References
  16. 11 Predictive Analysis of Intelligent Sensing and Cloud‐Based Integrated Water Management System
    1. 11.1 Introduction
    2. 11.2 Literature Survey
    3. 11.3 Proposed Six‐Tier Data Framework
    4. 11.4 Implementation and Result Analysis
    5. 11.5 Conclusion
    6. References
  17. 12 Data Security in the Internet of Things
    1. 12.1 Introduction
    2. 12.2 IoT: Brief Introduction
    3. 12.3 IoT Security Classification
    4. 12.4 Security in IoT Data
    5. 12.5 Conclusion
    6. References
  18. 13 DDoS Attacks
    1. 13.1 Introduction
    2. 13.2 Cloud and DDoS Attack
    3. 13.3 Mitigation Approaches
    4. 13.4 Challenges and Issues with Recommendations
    5. 13.5 A Generic Framework
    6. 13.6 Conclusion and Future Work
    7. References
  19. 14 Securing the Defense Data for Making Better Decisions Using Data Fusion
    1. 14.1 Introduction
    2. 14.2 Analysis of Big Data
    3. 14.3 Data Fusion
    4. 14.4 Data Fusion for IoT Security
    5. 14.5 Conclusion
    6. References
  20. 15 New Age Journalism and Big Data (Understanding Big Data and Its Influence on Journalism)
    1. 15.1 Introduction
    2. References
  21. 16 Two Decades of Big Data in Finance
    1. 16.1 Introduction
    2. 16.2 Methodology
    3. 16.3 Article Identification and Selection
    4. 16.4 Description and Classification of Literature
    5. 16.5 Content and Citation Analysis of Articles
    6. 16.6 Reporting of Findings and Research Gaps
    7. References
  22. Index
  23. End User License Agreement