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
Contributors
Preface
Index

Make predictive analytics for IoT data


Predictive analytics is a method to make prediction for an unknown event. In the context of IoT, we can develop predictive analytics to make a decision-based streaming sensor data. This is a part of machine learning study. In general, we can make predictive analytics using a diagram that is shown as follows:

Defining business problems is the first step to develop predictive analytics. Some problems probably need experts to make clear those problems. For instance, economics, biology, and volcanology. 

We also should prepare data in order to develop a model. This data should have high impact factors on the model.  When we develop a model, we also perform some steps such as defining targets, extracting derived features from data, fitting the model, and evaluating the model. In a real project, we probably make some iterations to ensure the corrected model.

After we developed the model, we can deploy the model into our system. This could be deployed in web...