Sensors to Cloud - A Guide to the Internet of Things


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Sensors to Cloud - A Guide to the Internet of Things

Connect, Collect, Compute and Consume IoT Data

About the Book

Most IoT books are myopically focussed on the “thing” like Raspberry Pi and not the entire stack. The value is not in the “thing” but the data that is collected from the field. The value is when the right data is collected, reliably stored, and knowledge computed to arrive at actionable outcomes. This book is written to equip Developers and Data scientists with all the necessary skills/tools on Edge to Cloud Data management and Analytics. The concepts will help lay the foundations, followed with deep dives on architecture and hand-on coding. Along the way a vast trove of learning materials will be shared to help go beyond whats covered here.

In the Capstone project we will simulate a smart IoT power grid which gathers power shortfall and takes decisions in real time to switch on the right Power plants to generate additional power in the most efficient fashion.

What you will learn:

  • How data flows from IoT device to the cloud, along with variety of OT and IT protocols used
  • Variety of IoT Field Gateway architectures and software. Example: Node-red, Kura etc
  • Variety of IoT Cloud Gateway architectures and software. Example: Apache Kafka, RabbitMQ etc 
  • Tools for storing massive amounts of IoT data in the cloud. Example: Apache Cassandra, MongoDB etc
  • Statistical techniques for analytics. Example: Anomaly detection using Statistics
  • Machine learning techniques for analytics. Example: Linear Regression, Clustering and Classification 
  • Deep learning techniques for analytics. Example: Neural Networks
  • Variety of tools to run these analytics in the cloud. Example: Apache Spark etc
  • Tools to storing data on the edge. Example: SQLite
  • Tools and Techniques to perform Edge Analytics. 

Who is this book for:

  • This book is written to equip Developers and Data scientists with all the necessary skills/tools on Edge to Cloud Data management and Analytics.

About the Author

Sree Kamireddy
Sree Kamireddy

Sree is currently a Staff Technical Product Manger leading the product execution for Predix Data & Analytics team, an Industrial IoT platform from GE. Sree has deep expertise in Big Data and Analytics having shipped several pivotal enterprise and consumer products using by millions of customers billions of times a week.

Table of Contents

  • 1. Introduction
    • 1.1 What is Internet of Things?
    • 1.2 Why is IoT Important? and the advent of new Business models
    • 1.3 IoT applications
    • 1.4 Consumer Applications
      • 1.4.1 Healthcare
      • 1.4.2 Homes and Appliances
    • 1.5 Industrial Applications
      • 1.5.1 Cities
      • 1.5.2 Transportation
      • 1.5.3 Factories and Machines
      • 1.5.4 Agriculture
    • 1.6 Key components of IoT Applications
      • 1.6.1 Things
        • What is a Sensor?
        • What is an Actuator?
        • What is a Chipset?
      • 1.6.2 Connect
      • 1.6.3 Collect
      • 1.6.4 Compute
      • 1.6.5 Consume
    • 1.7 Quick outline of the book
    • 1.8 Capstone Project
      • 1.8.1 Business problem definition
      • 1.8.2 Selecting sensors
      • 1.8.3 Selecting actuators
      • 1.8.4 Next steps to have a running Capstone Project
  • 2. IoT Edge Elements and Architecture
    • 2.1 Architectures at the IoT Edge
    • 2.2 IoT field communication protocols
      • 2.2.1 Bluetooth
      • 2.2.2 Wifi
      • 2.2.3 Zigbee and Z-Wave
      • 2.2.4 LPWAN
      • 2.2.5 NB-IoT
    • 2.3 Capstone Project
      • 2.3.1 Selecting Sensors
      • 2.3.2 Selecting Actuators
      • 2.3.3 Selecting IoT field communication protocols
      • 2.3.4 Selecting software for field gateway
  • 3. IoT Cloud Gateway
    • 3.1 Why do we need Cloud Gateway?
    • 3.2 Needed properties of Cloud Gateway
    • 3.3 Available Tools and applications to IoT
      • 3.3.1 Kafka
      • 3.3.2 RabbitMQ
      • 3.3.3 ZeroMQ
    • 3.4 Important Cloud communication protocols applicable to IoT
      • 3.4.1 HTTP/2
      • 3.4.2 CoAP
      • 3.4.3 MQTT
      • 3.4.4 Others
    • 3.5 Capstone Project:
      • 3.5.1 Choosing cloud Gateway tools for the project
      • 3.5.2 Choosing cloud communication protocol
  • 4. Collect Principles on IoT Data
    • 4.1 5Vs
    • 4.2 CAP
    • 4.3 Write speed is important
    • 4.4 Dynamic schema is important
    • 4.5 Varied Analytics and Reads
    • 4.6 Relational Databases
      • 4.6.1 Pros and Cons of Relational DB’s
      • 4.6.2 Suitable IoT use cases
      • 4.6.3 The need for a different model
  • 5. NoSQL Databases
    • 5.1 Key Value Databases
      • 5.1.1 What is it
      • 5.1.2 Suitable IoT use cases
      • 5.1.3 Available tools
    • 5.2 Document Databases
      • 5.2.1 What is it
      • 5.2.2 Suitable IoT use cases
      • 5.2.3 Available tools
    • 5.3 Graph Databases
      • 5.3.1 What is it
      • 5.3.2 Suitable IoT use cases
      • 5.3.3 Available tools
    • 5.4 Columnar Databases
      • 5.4.1 What is it
      • 5.4.2 Suitable IoT use cases
      • 5.4.3 Available tools
    • 5.5 Project Cheetah: Storing Sensor Data
      • 5.5.1 Choosing a Data Model for Project
      • 5.5.2 Choosing Datastore for Project
  • 6. Timeseries Databases
    • 6.1 What are Timeseries Databases
    • 6.2 Suitable IoT use cases
    • 6.3 Available tools
  • 7. Compute Basics
    • 7.1 Methodology
    • 7.2 General purpose processing page
      • 7.2.1 Map Reduce
      • 7.2.2 Spark
      • 7.2.3 Flink/Storm etc
      • 7.2.4 Project Cheetah
    • 7.3 Data Understanding, Exploration and Visualization
      • 7.3.1 Statistics and IoT
      • 7.3.2 Mean and Median
      • 7.3.3 Standard Deviation
      • 7.3.4 Skewness
      • 7.3.5 Kurtosis
      • 7.3.6 Covariance and Correlation
    • 7.4 Visualization
      • 7.4.1 Box Plot
      • 7.4.2 Run Charts for Time Series Data
      • 7.4.3 Histograms
      • 7.4.4 Capstone Project Visualization
    • 7.5 Data Preparation
      • 7.5.1 Pre Processing
      • 7.5.2 Feature Selection
      • 7.5.3 Dimensionality Reduction
      • 7.5.4 Imputation
  • 8. Compute Techniques
    • 8.1 Regression and IoT Applications
    • 8.2 Classification and IoT Applications
    • 8.3 Clustering and IoT Applications
    • 8.4 Deep Learning and IoT Applications
    • 8.5 Time Series Analytics
  • 9. Consume: Outcomes from IoT Data
    • 9.1 Edge Analytics
      • 9.1.1 Fog computing
      • 9.1.2 Mist computing
    • 9.2 Real Time Streaming Analytics
    • 9.3 Improve Models
      • 9.3.1 Improve model through offline computations
      • 9.3.2 Push Improvements to the edge
  • 10. Conclusion
    • 10.1 Summary of what you learnt
    • 10.2 Steps to Break into IoT
      • 10.2.1 Product Managers
      • 10.2.2 Developers
      • 10.2.3 Data Scientists
    • 10.3 What next?
  • Notes

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