A Subjective Introduction to the IoT

If you have no idea what is IoT and how to start your adventure with IoT, programming and networking, keep studying. This course is a painless introduction to the IoT technology.

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Created by: Karlis Berkolds

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Course Description

This course covers an overview of selected IoT technologies, tools and methods. Firstly you'll find out what is IoT then we will guide you through the popular IoT hardware, including Arduino and Espressif ESP8266 and ESP32 System on Chips, sensors and actuators, finally through the introduction to the networking. Right enough for now to let you understand IoT idea, start programming and embrace IoT technology. Even configure your devices to talk to your home network and send data over MQTT to the cloud.
In details we discuss deeply the genesis of the IoT, enabling technologies that made IoT devices to hit over 20 billion of the devices to be interconnected by 2021. We also make a short review of the popular scenarios that extensively utilise IoT approach and devices. Once you get an IoT overview, it is time to step into the technical details. In the following part of the course, we will introduce you to the Arduino microcontrollers, Espressif ESP8266 and ESP32 as well as a number of popular sensors and actuators. Following section is on microcontrollers programming in popular Arduino IDE. Finally, we will introduce you to the IoT networking. In this course we use regular IP network with elementary MQTT application protocol presentation.
Course contains example codes in C++, usually "Hello World" style, including networking, communication with sensors and actuators and even short programming in C++ crash course.

Beginner Level

Time: 8 Weeks - 4-5 hours per week

Instructor Details

Karlis Berkolds

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Karlis Berkolds is a full time researcher at Riga Technical University, Department of artificial intelligence and systems engineering. His research focuses on hardware based deep learning artificial neural networks. He received his master degree in computer science in 2017. Currently works on his PhD research focusing on deep learning neural networks.