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.

TAUSIFA JAN SALEEM is pursuing her PhD at the Department of Computer Science & Engineering, National Institute of Technology Srinagar, India. She has completed a Bachelors in Information Technology from National Institute of Technology Srinagar and Masters in Computer Engineering from University of Jammu, India. Her research focuses on Internet of Things, Data analytics, and Machine Learning. She has 7 international research publications to her credit.

MOHAMMAD AHSAN CHISHTI, PhD, is an Assistant Professor in the Department of Computer Science & Engineering, National Institute of Technology Srinagar, India. He has more than 50 research publications to his credit and 12 patents with two granted International Patents. He is Senior Member Institute of IEEE, Member IEI, Life Member CSI, and Member IETE. He is a certified White belt in Six Sigma by Six Sigma Advantage Inc. of USA (SSAI).

1. Big Data Analytics for the Internet of Things: An Overview

          Tausifa Jan Saleem and Mohammad Ahsan Chishti

2. Data, Analytics and Interoperability between Systems (IoT) is Incongruous with the Economics of Technology: Evolution of Porous Pareto Partition (P3)

Shoumen Palit Austin Datta, Tausifa Jan Saleem, Molood Barati, María Victoria López López, Marie-Laure Furgala, Diana C. Vanegas, Gérald Santucci and Eric S. McLamore

2.1. Context

2.2. Models In The Background

2.3.  Problem Space: Are We Asking The Correct Questions?

2.4.  Solutions Approach: Elusive Quest To Build Bridges Between Data & Decisions

2.5. Avoid This Space – The Deception Space

2.6.  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 Porous Pareto (80/20) Partition

2.11. Conundrums

2.12. Stigma Of Partition Versus Astigmatism Of Vision

2.13. 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

Acknowledgements

3. Machine Learning Techniques for IoT Data Analytics

             Nailah Afshan and Ranjeet Kumar Rout

3.1. Introduction

3.2. Taxonomy of Machine Learning Algorithms

3.2.1. Supervised ML Algorithms

3.2.1.1. Classification

3.2.1.1.1. K Nearest Neighbours (K-NN) Algorithm

3.2.1.1.2. Naïve Bayes Classifier

3.2.1.2. Regression Analysis

3.2.1.2.1. Linear Regression

3.2.1.3. Classification and Regression Tasks

3.2.1.3.1. Support Vector Machine

3.2.1.3.2. Support Vector Regression

3.2.1.3.3. Classification and regression trees

3.2.1.3.4. Random forest

3.2.1.3.5. Bootstrap Aggregating

3.2.2. Unsupervised Machine Learning Algorithms

3.2.2.1. Clustering

3.2.2.1.1. K-Means Clustering

3.2.2.1.2. Density-based spatial clustering of applications with noise (DBSCAN)

3.2.2.1.3. Neural Networks

3.2.2.2. Feature Extraction

3.2.2.2.1. Principal Component Analysis (PCA)

3.2.2.2.2. Canonical Correlation Analysis (CCA)

3.3.  Conclusion

References

4. IoT Data Analytics using Cloud Computing

Anjum Sheikh, Sunil Kumar and Asha Ambhaikar

4.1. Introduction

4.2. IoT Data Analytics

4.2.1. Process of IoT Analytics

4.2.2. Types of Analytics

4.3. Cloud Computing for IoT

4.3.1. Deployment Models for Cloud

4.3.2. Service Models for Cloud Computing

4.3.3. Data Analytics on Cloud

4.4. Cloud-Based IoT Data Analytics Platform

4.4.1. Atos Codex

4.4.2. AWS IoT

4.4.3. IBM Watson IoT

4.4.4. Hitachi Vantara Pentaho, Lumada

4.4.5. Microsoft Azure IoT

4.4.6. Oracle IoT Cloud Services

4.5. Machine Learning for IoT analytics in Cloud

4.5.1. ML algorithms for Data Analytics

4.5.2. Types of Predictions supported by ML and Cloud

4.6. Challenges for Analytics using Cloud

4.7. Conclusion

References

5. Deep Learning Architectures for IoT Data Analytics

Snowber Mushtaq and Omkar Singh

5.1.  Introduction

5.1.1. Types of Learning Algorithms

5.1.2. Steps involved in solving a problem

5.1.2.1.  Basic Terminology

5.1.2.2. Training Process

5.1.3. Modeling in Data Science

5.1.4. Why Deep Learning and IoT?

5.2. Deep Learning Architectures

5.2.1. Restricted Boltzmann Machine

5.2.1.1. Training Boltzmann Machine

5.2.1.2.  Applications of Restricted Boltzmann Machine

5.2.2. Deep Belief Networks

5.2.2.1. Training Deep Belief Networks

5.2.2.2.  Applications of Deep Belief Networks

5.2.3. Auto Encoders

5.2.3.1.  Training of Auto Encoders

5.2.3.2.  Applications of Auto Encoders

5.2.4. Convolutional Neural Networks

5.2.4.1.  Layers of Convolution Neural Network

5.2.4.2.  Activation functions used in Convolution Neural Networks

5.2.4.3.  Applications of Convolution Neural Networks

5.2.5. Generative Adversarial Network

5.2.5.1.  Training of Generative Adversarial Network

5.2.5.2.  Applications of Generative Adversarial Network

5.2.6.  Recurrent Neural Networks

5.2.6.1.  Training of Recurrent Neural Networks

5.2.6.2.  Applications of Recurrent Neural Networks

5.2.7. Long Short Term Memory

5.2.7.1.  Training of Long Short Term Memory

5.2.7.2.  Applications of Long Short Term Memory

5.3. Conclusion

References

6. Adding Personal Touches to IoT: A User-Centric IoT Architecture

Sarabjeet Kaur Kochhar

6.1.  Introduction

6.2.  Enabling Technologies for Big Data Analytics of IoT Systems

6.3.  Personalizing the IoT

6.3.1. Personalization for Business

6.3.2. Personalization for Marketing

6.3.3. Personalization for product improvement and service optimization

6.3.4. Personalization for automated recommendations

6.3.5. Personalization for improved user experience.

6.4.  Related Work

6.5.  User sensitized IoT architecture

6.6.  Concerns and Future Directions

6.7.  Conclusion

References

7. Smart Cities and the Internet of Things

Hemant Garg, Sushil Gupta and Basant Garg

7.1. Introduction                 

7.2. Development of Smart Cities and the Internet of Things               

7.3. The combination of the internet of things with cities to form smart cities 

7.3.1. Unification of the Internet of Things                                                                               

7.3.2.  Security of Smart Cities

7.3.3. Management of water and related amenities

7.3.4. Power Distribution and Management                                                                                         

7.3.5. Revenue collection and administration 

7.3.6. Management of City assets and Human Resources 

7.3.7. Environmental pollution management

7.4. How future smart cities can improve use of internet of things

7.5. Conclusion

             References

8. A Roadmap for Application of IoT Generated Big Data  in Environmental Sustainability

Ankur Kashyap

8.1. Background and motivation

8.2.  Execution of study

8.2.1. Role of Big Data in sustainability

8.2.2. Present status and future possibilities of IoT in environmental sustainability

8.3.  Proposed roadmap

8.4.  Identification & prioritizing the barriers in the process

8.4.1. Internet infrastructure

8.4.2. High hardware & software cost

8.4.3. Less qualified workforce

8.5.  Conclusion and discussion

9. Application of High-Performance Computing in Synchrophasor Data Management and Analysis for Power Grids

              CM Thasnimol and R. Rajathy

9.1.  Introduction

9.2.  Types of Application of Synchrophasor Data

9.2.1. Voltage Stability Analysis

9.2.2. Transient Stability

9.2.3. Out of Step Splitting Protection

9.2.4. Multiple Event Detection

9.2.5. State Estimation

9.2.6. Fault Detection

9.2.7. Loss of Main (LOM) Detection

9.2.8. Topology Update Detection

9.2.9. Oscillation Detection

9.3.  Utility Big Data Issues Related to PMU Driven Applications

9.3.1. Heterogeneous Measurement Integration

9.3.2. Variety and Interoperability

9.3.3. Volume and Velocity

9.3.4. Data Quality and Security

9.3.5. Utilization and Analytics

9.3.6. Visualization of Data

9.4.  Big Data Analytics Platforms for PMU Data Processing

9.4.1. Hadoop

9.4.2. Apache Spark

9.4.3. Apache Hbase

9.4.4. Apache Storm

9.4.5. Cloud-Based Platforms

9.5. Conclusion

References

10. Intelligent enterprise-level big data analytics for modelling and management in smart internet of roads

            Amin Fadaeddini, Babak Majidi and Mohammad Eshghi

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

             Tanuja Patgar and Ripal Patel

11.1. Introduction

11.2. Literature Survey

11.3. Proposed Six tier Data Framework

11.3.1. Methodology

11.3.2. Proposed Algorithn

11.4. Implementation and Result Analysis

11.4.1. Water Report for Home1 and Home2 Module

11.5. Conclusion

References

12. Data Security in the Internet-of-Things: Challenges and Opportunities

            Shashwati Banerjea, Shashank Srivastava and Sachin Kumar

12.1. Introduction

12.2. Internet-of-Things (IoT): Brief introduction

12.2.1. Challenges in a Secure IoT

12.2.2. Security Requirements in IoT Architecture

12.2.2.1. Sensing layer

12.2.2.2. Network Layer

12.2.2.3. Interface Layer

12.2.3. Common Attacks in IoT

12.3. IoT Security Classification

12.3.1. Application Domain

12.3.1.1. Authentication

12.3.1.2. Authorization

12.3.1.3. Depletion of Resources

12.3.1.4. Establishment of Trust

12.3.2. Architectural Domain

12.3.2.1. Authentication in IoT architecture

12.3.2.2. Authorization in IoT architecture

12.3.3. Communication channel

12.4. Security in IoT Data

12.4.1. IoT Data Security: Requirements

12.4.1.1. Data: Confidentiality Integrity Authentication

12.4.1.2. Data Privacy

12.4.2. IoT Data Security: Research Directions

12.5. Conclusion

References

13. DDoS Attacks: Tools, Mitigation Approaches, and Probable Impact on Private Cloud Environment

            Rup Kumar Deka, Dhruba Kumar Bhattacharyya and Jugal Kumar Kalita

13.1. Introduction

13.1.1. State of the Art

13.1.2. Contribution

13.1.3. Organization

13.2. Cloud and DDoS Attack

13.2.1. Cloud Deployment Models

13.2.1.1. Differences between Private Cloud and Public Cloud

13.2.2. DDoS Attacks

13.2.2.1. Attacks on Infrastructure level

13.2.2.2. Attacks on Application level

13.2.3. DoS/DDoS Attack on Cloud: Probable Impact

13.3. Mitigation Approaches

13.3.1. Discussion

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

Syed Rameem Zahra

14.1. Introduction

14.2. Analysis of Big Data

14.2.1. Existing IoT Big Data Analytics System

14.2.2. Big Data Analytical Methods

14.2.3. Challenges in IoT Big Data Analytics

14.3. Data Fusion

14.3.1. Opportunities provided by Data Fusion

14.3.2. Data Fusion Challenges

14.3.3. Stages at which Data Fusion can happen

14.3.4. Mathematical model for Data Fusion

14.4. Data Fusion for IoT Security

14.4.1. Defense Use Case

14.5. Conclusion

References

15. New age Journalism and Big data (Understanding big data & its influence on Journalism)

Asif Khan and Heeba Din

15.1. Introduction

15.1.1. Big Data Journalism: The Next Big Thing

15.1.2. All About Data

15.1.3. Accessing Data for Journalism

15.1.4. Data Analytics: Tool for Journalist

15.1.5. Case Studies-Big Data

15.1.5.1. BBC Big Data

15.1.5.2. The Guardian Data Blog

15.1.5.3. Wiki-leaks

15.1.5.4. World Economic Forum

15.1.6. Big Data-Indian Scenario

15.1.7. Internet of Things and Journalism

15.1.8. Impact on Media/Journalism

References

16. Two decades of big data in finance: Systematic literature review and future research agenda

 Nufazil Ahangar

16.1. Introduction

16.2. Methodology

16.3. Article Identification and Selection’

16.4. Description and Classification of Literature

16.4.1. Research Method Employed

16.4.2. Articles Published Year Wise

16.4.3. Journal of Publication

16.5. Content and Citation Analysis of articles

16.5.1. Citation Analysis

16.5.2. Content Analysis

16.5.2.1. Big Data in Financial Markets

16.5.2.2. Big Data in Internet Finance

16.5.2.3. Big Data in Financial Services

16.5.2.4. Big Data and other Financial Issues

16.6. Reporting of Findings and Research Gaps

16.6.1. Findings from literature review

16.6.1.1. Lack of Symmetry

16.6.1.2. Dominance of Research on financial Markets, Internet Finance and Financial Services

16.6.1.3. Dominance of Empirical Research

16.6.2. Directions for Future Research

References

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