Book Image

Enterprise Internet of Things Handbook

By : Arvind Ravulavaru
Book Image

Enterprise Internet of Things Handbook

By: Arvind Ravulavaru

Overview of this book

There is a lot of work that is being done in the IoT domain and according to Forbes the global IoT market will grow from $157B in 2016 to $457B by 2020. This is an amazing market both in terms technology advancement as well as money. In this book, we will be covering five popular IoT platforms, namely, AWS IoT, Microsoft Azure IoT, Google IoT Core, IBM Watson IoT, and Kaa IoT middleware. You are going to build solutions that will use a Raspberry Pi 3, a DHT11 Temperature and humidity sensor, and a dashboard to visualize the sensor data in real-time. Furthermore, you will also explore various components of each of the platforms that are needed to achieve the desired solution. Besides building solutions, you will look at how Machine Learning and IoT go hand in hand and later design a simple predictive web service based on this concept. By the end of this book, you will be in a position to implement an IoT strategy best-fit for your organization
Table of Contents (12 chapters)

Rain prediction

Now that we understand machine learning, and we have already worked on IoT, let's work on a simple experiment that shows the value of using ML in IoT.

In this chapter, we are going to perform the following operations:

  1. Use historical data to build a data model
  2. Get real-time data from a sensor and feed it to the model
  3. Based on the inputs to the model, predict the output

Here, we are going to use historical weather data and then build a data model that accepts temperature and humidity values and returns the probability of rain.

We are going to use Azure Machine Learning Studio for this, and we are going to work along the lines of an article named Weather forecast using the sensor data from your IoT hub in Azure Machine Learning at

We are not going to implement all the...