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Introduction to Edge Analytics

Everything old is new again! The rise of the personal computer (PC) in the 1980s and 1990s led to a revolution in computing. Instead of so-called dumb terminals connected to a large computer, many computers were connected in a network, spreading the processing power around. Edge analytics is like the personal computer revolution, but for IoT (Internet of Things) devices. We will start this chapter by comparing edge analytics to the computer revolution before we discuss the advantages of using edge analytics in an IoT application. We will look at the basic edge analytics architecture, as well as introduce the Microsoft Azure IoT Edge platform.

This chapter will cover the following topics:

What is edge analytics?
Applying and comparing architectures
Key benefits of edge analytics
Edge analytics architectures
Using edge analytics in the real world

Description

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.

What you will learn

Discover the key concepts and architectures used with edge analytics Understand how to use long-distance communication protocols for edge analytics Deploy Microsoft Azure IoT Edge to a Raspberry Pi Create Node-RED dashboards with MQTT and Text to Speech (TTS) Use MicroPython for developing edge analytics apps Explore various machine learning techniques and discover how machine learning is related to edge analytics Use camera and vision recognition algorithms on the sensory side to design an edge analytics app Monitor and audit edge analytics apps

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