Analytics for the Internet of Things (IoT)

Break through the hype and learn how to extract actionable intelligence from the flood of IoT data
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Analytics for the Internet of Things (IoT)

Andrew Minteer

Break through the hype and learn how to extract actionable intelligence from the flood of IoT data
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Book Details

ISBN 139781787120730
Paperback378 pages

Book Description

We start with the perplexing task of extracting value from huge amounts of barely intelligible data. The data takes a convoluted route just to be on the servers for analysis, but insights can emerge through visualization and statistical modeling techniques. You will learn to extract value from IoT big data using multiple analytic techniques.

Next we review how IoT devices generate data and how the information travels over networks. You’ll get to know strategies to collect and store the data to optimize the potential for analytics, and strategies to handle data quality concerns.

Cloud resources are a great match for IoT analytics, so Amazon Web Services, Microsoft Azure, and PTC ThingWorx are reviewed in detail next. Geospatial analytics is then introduced as a way to leverage location information. Combining IoT data with environmental data is also discussed as a way to enhance predictive capability. We’ll also review the economics of IoT analytics and you’ll discover ways to optimize business value.

By the end of the book, you’ll know how to handle scale for both data storage and analytics, how Apache Spark can be leveraged to handle scalability, and how R and Python can be used for analytic modeling.

Table of Contents

Chapter 1: Defining IoT Analytics and Challenges
The situation
Defining IoT analytics
IoT analytics challenges
Business value concerns
Summary
Chapter 2: IoT Devices and Networking Protocols
IoT devices
Networking basics
IoT networking connectivity protocols
IoT networking data messaging protocols
Analyzing data to infer protocol and device characteristics
Summary
Chapter 3: IoT Analytics for the Cloud
Building elastic analytics
Elastic analytics concepts
Designing for scale
Cloud security and analytics
The AWS overview
Microsoft Azure overview
The ThingWorx overview
Summary
Chapter 4: Creating an AWS Cloud Analytics Environment
The AWS CloudFormation overview
The AWS Virtual Private Cloud (VPC) setup walk-through
How to terminate and clean up the environment
Summary
Chapter 5: Collecting All That Data - Strategies and Techniques
Designing data processing for analytics
Applying big data technology to storage
Apache Spark for data processing
To stream or not to stream
Handling change
Summary
Chapter 6: Getting to Know Your Data - Exploring IoT Data
Exploring and visualizing data
Look for attributes that might have predictive value
R (the pirate's language...if he was a statistician)
Summing it all up
Solving industry-specific analysis problems
Summary
Chapter 7: Decorating Your Data - Adding External Datasets to Innovate
Adding internal datasets
Adding external datasets
Summary
Chapter 8: Communicating with Others - Visualization and Dashboarding
Common mistakes when designing visuals
The Hierarchy of Questions method
Designing visual analysis for IoT data
Creating a dashboard with Tableau
Creating and visualizing alerts
Summary
Chapter 9: Applying Geospatial Analytics to IoT Data
Why do you need geospatial analytics for IoT?
The basics of geospatial analysis
Vector-based methods
Raster-based methods
Storing geospatial data
Processing geospatial data
Solving the pollution reporting problem
Summary
Chapter 10: Data Science for IoT Analytics
Machine learning (ML)
Anomaly detection using R
Forecasting using ARIMA
Deep learning
Summary
Chapter 11: Strategies to Organize Data for Analytics
Linked Analytical Datasets
Managing data lakes
The data retention strategy
Summary
Chapter 12: The Economics of IoT Analytics
The economics of cloud computing and open source
Cost considerations for IoT analytics
Thinking about revenue opportunities
The economics of predictive maintenance example
Summary
Chapter 13: Bringing It All Together
Review
A sample project
Summary

What You Will Learn

  • Overcome the challenges IoT data brings to analytics
  • Understand the variety of transmission protocols for IoT along with their strengths and weaknesses
  • Learn how data flows from the IoT device to the final data set
  • Develop techniques to wring value from IoT data
  • Apply geospatial analytics to IoT data
  • Use machine learning as a predictive method on IoT data
  • Implement best strategies to get the most from IoT analytics
  • Master the economics of IoT analytics in order to optimize business value

Authors

Table of Contents

Chapter 1: Defining IoT Analytics and Challenges
The situation
Defining IoT analytics
IoT analytics challenges
Business value concerns
Summary
Chapter 2: IoT Devices and Networking Protocols
IoT devices
Networking basics
IoT networking connectivity protocols
IoT networking data messaging protocols
Analyzing data to infer protocol and device characteristics
Summary
Chapter 3: IoT Analytics for the Cloud
Building elastic analytics
Elastic analytics concepts
Designing for scale
Cloud security and analytics
The AWS overview
Microsoft Azure overview
The ThingWorx overview
Summary
Chapter 4: Creating an AWS Cloud Analytics Environment
The AWS CloudFormation overview
The AWS Virtual Private Cloud (VPC) setup walk-through
How to terminate and clean up the environment
Summary
Chapter 5: Collecting All That Data - Strategies and Techniques
Designing data processing for analytics
Applying big data technology to storage
Apache Spark for data processing
To stream or not to stream
Handling change
Summary
Chapter 6: Getting to Know Your Data - Exploring IoT Data
Exploring and visualizing data
Look for attributes that might have predictive value
R (the pirate's language...if he was a statistician)
Summing it all up
Solving industry-specific analysis problems
Summary
Chapter 7: Decorating Your Data - Adding External Datasets to Innovate
Adding internal datasets
Adding external datasets
Summary
Chapter 8: Communicating with Others - Visualization and Dashboarding
Common mistakes when designing visuals
The Hierarchy of Questions method
Designing visual analysis for IoT data
Creating a dashboard with Tableau
Creating and visualizing alerts
Summary
Chapter 9: Applying Geospatial Analytics to IoT Data
Why do you need geospatial analytics for IoT?
The basics of geospatial analysis
Vector-based methods
Raster-based methods
Storing geospatial data
Processing geospatial data
Solving the pollution reporting problem
Summary
Chapter 10: Data Science for IoT Analytics
Machine learning (ML)
Anomaly detection using R
Forecasting using ARIMA
Deep learning
Summary
Chapter 11: Strategies to Organize Data for Analytics
Linked Analytical Datasets
Managing data lakes
The data retention strategy
Summary
Chapter 12: The Economics of IoT Analytics
The economics of cloud computing and open source
Cost considerations for IoT analytics
Thinking about revenue opportunities
The economics of predictive maintenance example
Summary
Chapter 13: Bringing It All Together
Review
A sample project
Summary

Book Details

ISBN 139781787120730
Paperback378 pages
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