The Internet of Things and Big Data Analytics

Integrated Platforms and Industry Use Cases

Pethuru Raj, T Poongodi, Balamurugan Balusamy, Manju Khari, Pethuru Raj, T Poongodi, Balamurugan Balusamy, Manju Khari

• Publisher: CRC Press
• Year: 2020
• eBook ISBN: 9781000057393
• Edition: 1
• Topic: Computer Science
• Subtopic: Cloud Computing
• Index: Computer Science

This book comprehensively conveys the theoretical and practical aspects of IoT and big data analytics with the solid contributions from practitioners as well as academicians. This book examines and expounds the unique capabilities of the big data analytics platforms in capturing, cleansing and crunching IoT device/sensor data in order to extricate actionable insights. A number of experimental case studies and real-world scenarios are incorporated in this book in order to instigate our book readers. This book

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• Analyzes current research and development in the domains of IoT and big data analytics

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• Gives an overview of latest trends and transitions happening in the IoT data analytics space

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• Illustrates the various platforms, processes, patterns, and practices for simplifying and streamlining IoT data analytics

The Internet of Things and Big Data Analytics: Integrated Platforms and Industry Use Cases examines and accentuates how the multiple challenges at the cusp of IoT and big data can be fully met. The device ecosystem is growing steadily. It is forecast that there will be billions of connected devices in the years to come. When these IoT devices, resource-constrained as well as resource-intensive, interact with one another locally and remotely, the amount of multi-structured data generated, collected, and stored is bound to grow exponentially. Another prominent trend is the integration of IoT devices with cloud-based applications, services, infrastructures, middleware solutions, and databases. This book examines the pioneering technologies and tools emerging and evolving in order to collect, pre-process, store, process and analyze data heaps in order to disentangle actionable insights.

Chapter 1

Taxonomy of Big Data and Analytics Solutions for Internet of Things

S. Karthikeyan, Balamurugan Balusamy, T. Poongodi

Galgotias University

Firoz Khan

Higher College of Technology

Contents

1.1 Introduction

1.1.1 IoT Emergence

1.1.2 IoT Architecture

1.1.2.1 Three Layers of IoT

1.1.2.2 IoT Devices

1.1.2.3 Cloud Server

1.1.2.4 End User

1.1.3 IoT Challenges

1.1.4 IoT Opportunities

1.1.4.1 IoT and the Cloud

1.1.4.2 IoT and Security

1.1.4.3 IoT at the Edge

1.1.4.4 IoT and Integration

1.1.5 IoT Applications

1.1.5.1 Real-Time Applications of IoT

1.1.6 Big Data and Analytics Solutions for IoT

1.1.6.1 Big Data in IoT

1.1.6.2 Big Data Challenges

1.1.6.3 Different Patterns of Data

1.7 Big Data Sources

1.7.1 Media

1.7.2 Business Data

1.7.2.1 Customer’s Details

1.7.2.2 Transaction Details

1.7.2.3 Interactions

1.7.3 IoT Data

1.8 Big Data System Components

1.8.1 Data Acquisition (DAQ)

1.8.2 Data Retention

1.8.3 Data Transportation

1.8.4 Data Processing

1.8.5 Data Leverage

1.9 Big Data Analytics Types

1.9.1 Predictive Analytics

1.9.1.1 What Will Happen If …?

1.9.2 Descriptive Analytics

1.9.2.1 What Has Happened?

1.9.3 Diagnostic Analytics

1.9.3.1 Why Did It Happen?

1.9.3.2 Real-Time Example

1.9.4 Prescriptive Analytics

1.9.4.1 What Should We Do about This?

1.10 Big Data Analytics Tools

1.10.1 Hadoop

1.10.1.1 Features of Hadoop

1.10.2 Apache Spark

1.10.3 Apache Storm

1.10.4 NoSQL Databases

1.10.5 Cassandra

1.10.6 RapidMiner

1.11 Conclusion

References

1.1 Introduction

Internet of Things (IoT) is the combination of wireless sensor network, cloud computing, user interface, as the role of wireless network is to sense the data alone, where the sensors are connected to cloud server for storing the recorded data. User interface such as mobile or web application is used to receive the IoT data from the cloud. IoT application can be pointed to various fields such as healthcare, farming, industry, transportation and so on. it can be applied from household things to military bases, the sensors and the application will be changed as per the needs of the people, if an individual needs to alert any suspicious activities in his/her home, then the individual should fix a motion sensor in the home, it alerts the user if any new motions are detected in home, it can be fine-tuned and can be called as smart home (Atzori et al. 2010).

IoT came into existence around 1999 and helped humans in many places to reduce their work. They were used for basic needs such as household devices and then they replaced manpower in large numbers with small sensors, global system for mobile communications (GSM), and few servers. The next terminology is the “big data”. As the IoT has evolved from wireless sensor networks and cloud computing, the IoT provides big data and handles large volumes of data that are produced by the IoT sensors or the actuators. The data can be structured, unstructured, or semi-structured depending upon the sensors that are used for the specific applications. The reason IoT generates big data is the traditional databases find it difficult to manage the unstructured as well as a huge volume of data. The generation of sensor data is continuous as the role of IoT is to alert or monitor the environment. The sensors record the data 24/7 and send a notification as the user has programmed it already in the environment (Atzori et al. 2010).

IoT generates large volumes of big data continuously from the sensors, and the recorded data are stored on cloud server, as the cloud services such as platform, software, infrastructure can be provided as per the need of the user and the requirements. In general, big data analytics is used to analyze the data and then take a decision based on the need of the user.

1.1.1 IoT Emergence

Pervasive computing, ubiquitous computing, wireless sensor network emerged from the Internet of Things around 1970s.The term “IoT” was originally coined by Kevin Ashton while working at Procter & Gamble. As the Internet was a hot topic in the late 1990s, Kevin Ashton, who was doing supply chain optimization, tried to combine two technologies: Internet and radio frequency identification (RFID). He called his presentation as “Internet of Things” (Al-Fuqaha et al. 2015).

IoT works on the principles and protocols of sensors, the Internet, cloud, and users. These components form the big picture of the IoT. The sensors or the actuators are connected to the cloud server through the Internet; then the data can be sent to the user whenever it is needed (Al-Fuqaha et al. 2015). The major roles of sensors are to capture the data from the environment; then it will be received by the user via cloud. For example, in smart homes, a motion sensor will be used to detect any suspicious activities around the location. It provides security to these smart homes by alerting and notifying the users irrespective of the distance between the home and the users.

1.1.2 IoT Architecture

The IoT architecture consists of three components: mainly IoT devices (sens...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Table of Contents
• Preface
• Author Biography
• Contributors
• 1 Taxonomy of Big Data and Analytics Solutions for Internet of Things
• 2 Big Data Preparation and Exploration
• 3 Emerging IoT-Big Data Platform Oriented Technologies
• 4 IoT-Big Data Systems (IoTBDSs) Enabling Technologies: Ubiquitous Wireless Communication, Real-Time Analytics, Machine Learning, Deep Learning, Commodity Sensors
• 5 Distinctive Attributes of Big Data Platform and Big Data Analytics Software for IoT
• 6 Big Data Architecture for IoT
• 7 Algorithms for Big Data Delivery over Internet of Things
• 8 Big Data Storage Systems for IoT – Perspectives and Challenges
• 9 Key Technologies Enabling Big Data Analytics for IoT
• 10 Internet of Things (IoT) and Big Data: Data Management, Analytics, Visualization and Decision Making
• 11 Big Data Programming Models for IoT Data
• 12 IoTBDs Applications: Smart Transportation, Smart Healthcare, Smart Grid, Smart Inventory System, Smart Cities, Smart Manufacturing, Smart Retail, Smart Agriculture, Etc.
• 13 Big Data Management Solutions for IoT: Case Study – Connected Car
• Index