Book Image

Hands-On Edge Analytics with Azure IoT

By : Colin Dow
Book Image

Hands-On Edge Analytics with Azure IoT

By: Colin Dow

Overview of this book

Edge analytics has gained attention as the IoT model for connected devices rises in popularity. This guide will give you insights into edge analytics as a data analysis model, and help you understand why it’s gaining momentum. You'll begin with the key concepts and components used in an edge analytics app. Moving ahead, you'll delve into communication protocols to understand how sensors send their data to computers or microcontrollers. Next, the book will demonstrate how to design modern edge analytics apps that take advantage of the processing power of modern single-board computers and microcontrollers. Later, you'll explore Microsoft Azure IoT Edge, MicroPython, and the OpenCV visual recognition library. As you progress, you'll cover techniques for processing AI functionalities from the server side to the sensory side of IoT. You'll even get hands-on with designing a smart doorbell system using the technologies you’ve learned. To remove vulnerabilities in the overall edge analytics architecture, you'll discover ways to overcome security and privacy challenges. Finally, you'll use tools to audit and perform real-time monitoring of incoming data and generate alerts for the infrastructure. By the end of this book, you'll have learned how to use edge analytics programming techniques and be able to implement automated analytical computations.
Table of Contents (15 chapters)
1
Section 1: Getting Started with Edge Analytics
5
Section 2: Understanding Edge Analytics Technologies
11
Section 3: The Road Ahead

Summary

In this chapter, we created a Smart Doorbell application that will announce who is at the door once they are picked up by a webcam. By sticking to an object-oriented approach, we were able to organize our edge code into three separate components, the Face class, the Message class, and the Camera script. At the heart of the Smart Doorbell application is the Face class. Using the object-oriented approach, we were able to write the code and test the functionality of this class quickly. We were also able to build and test the Message class the same way.

In the construction of our edge code, we were introduced to the OpenCV and face_recognition libraries. Having these libraries made our task so much easier as they did the majority of the heavy lifting for us. The paho.mqtt library made it very easy to connect to the outside world through the use of the MQTT protocol.

An MQTT...