Book Image

Learning AWS IoT

By : Agus Kurniawan
Book Image

Learning AWS IoT

By: Agus Kurniawan

Overview of this book

The Internet of Things market increased a lot in the past few years and IoT development and its adoption have showed an upward trend. Analysis and predictions say that Enterprise IoT platforms are the future of IoT. AWS IoT is currently leading the market with its wide range of device support SDKs and versatile management console. This book initially introduces you to the IoT platforms, and how it makes our IoT development easy. It then covers the complete AWS IoT Suite and how it can be used to develop secure communication between internet-connected things such as sensors, actuators, embedded devices, smart applications, and so on. The book also covers the various modules of AWS: AWS Greengrass, AWS device SDKs, AWS IoT Platform, AWS Button, AWS Management consoles, AWS-related CLI, and API references, all with practical use cases. Near the end, the book supplies security-related best practices to make bi-directional communication more secure. When you've finished this book, you'll be up-and-running with the AWS IoT Suite, and building IoT projects.
Table of Contents (14 chapters)
Title Page
Packt Upsell

Build a simple predictive analytics for your IoT project

In this section, we will develop a simple predictive analytics for IoT. Our project can be described in the following figure. We will perform sensing to acquire temperature and humidity from the sensor devices. Then, we send this sensor data to AWS Machine Learning to get a decision on whether the system will perform watering:

The project will focus on developing a machine learning model. Assume that we have temperature and humidity data from the sensor. We do not implement watering system, but we will make a decision system to trigger watering.

Next, we will implement the project with the following steps which are explained in detail in the upcoming sub-sections:

  1. Defining a machine learning model
  2. Preparing data
  3. Building a machine learning model
  4. Evaluating and testing a model

Defining a machine learning model

We will create a simple model for our project. We have two inputs—temperature and humidity. We also have historical data about sensor...