Book Image

Developing IoT Projects with ESP32 - Second Edition

By : Vedat Ozan Oner
3 (2)
Book Image

Developing IoT Projects with ESP32 - Second Edition

3 (2)
By: Vedat Ozan Oner

Overview of this book

ESP32, a low-cost and energy-efficient system-on-a-chip microcontroller, has become the backbone of numerous WiFi devices, fueling IoT innovation. This book offers a holistic approach to building an IoT system from the ground up, ensuring secure data communication from sensors to cloud platforms, empowering you to create production-grade IoT solutions using the ESP32 SoC. Starting with IoT essentials supported by real-world use cases, this book takes you through the entire process of constructing an IoT device using ESP32. Each chapter introduces new dimensions to your IoT applications, covering sensor communication, the integration of prominent IoT libraries like LittleFS and LVGL, connectivity options via WiFi, security measures, cloud integration, and the visualization of real-time data using Grafana. Furthermore, a dedicated section explores AI/ML for embedded systems, guiding you through building and running ML applications with tinyML and ESP32-S3 to create state-of-the-art embedded products. This book adopts a hands-on approach, ensuring you can start building IoT solutions right from the beginning. Towards the end of the book, you'll tackle a full-scale Smart Home project, applying all the techniques you've learned in real-time. Embark on your journey to build secure, production-grade IoT systems with ESP32 today!
Table of Contents (15 chapters)
13
Other Books You May Enjoy
14
Index

Questions

Here are some questions to review what we have learned in the chapter:

  1. Which one of the following is not true about machine learning?
    1. Supervised learning needs labeled data to train models.
    2. Unsupervised learning tries to find outliers in data.
    3. The agent in reinforced learning interacts with the environment to learn.
    4. Reinforced learning is superior to others at detecting patterns in data.
  2. Which one of the following is not a step in the tinyML pipeline?
    1. Data collection and pre-processing
    2. Training the model on an IoT device
    3. Optimizing the model for deployment
    4. Running inference on an IoT device
  3. Which technique makes an ML model small enough to fit into the memory of an IoT device?
    1. Training
    2. Quantization
    3. Overfitting
    4. Validation
  4. With TFLM, we can:
    1. Optimize a TensorFlow model...