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