In the dynamic realm of embedded systems, the right combination of hardware and software components can transform the development process and empower engineers to build robust and efficient solutions. This article explores a streamlined device-to-cloud embedded design utilizing the STM32G0 Nucleo board from STMicroelectronics, an AWS IoT ExpressLink module from Espressif, AWS IoT Core for secure MQTT communication, and Memfault for remote debugging.

Connected devices require a secure point-to-point channel to ensure that there is no possibility of exposing important data for the integrity of an embedded system. This is especially true when we talk about over-the-air (OTA) software updates, where the new firmware has a long way to go before reaching its destination and being installed by our bootloader. In this publication, we will explore a simple method to encrypt the firmware using the AES algorithm, using open-source libraries in Python.

All hardware devices experience bugs and need debugging. Android devices in specific are exceptionally complex with several hundred gigabytes of source code, dozens of components, and wide range of uses. In this article we will explore the different facilities and tools available to debug Android based devices and produce robust systems that can handle a wide range of applications from smart fridges, to payment terminals, and of course mobile phones.

Sometimes, C/C++ projects have a long development cycle. When working on such a project, it can be easy to take our development environment for granted, and forget about the effort invested in its bring-up. The build environment works like magic, the test framework is neatly integrated, and the CI/CD pipeline relieves us of tedious, repetitive tasks.

I’ve been a firmware engineer at two wearable companies in the past, Pebble and Fitbit, and there was always one class of customer support tickets and user complaints that never went away: issues around battery life. It was a constant game of whack-a-mole with every new firmware version introducing a battery regression.

I’m Markus, software engineer @ Tridonic, where we are working on Internet-connected wireless lighting solutions based on the Matter standard. To be able to monitor the reliability of those devices we’ve been using Memfault and tied it into Matter/Thread and its UDP/IPv6 stack based on the Nordic Connect SDK. In the following, I’ll show you the modifications we’ve done to enable Memfault in an IPv6 solution. Like Interrupt?

The Raspberry Pi, a compact single-board computer, is widely used for DIY projects to industrial applications. These devices ship with a customized Linux distribution that differs from standard Linux, adding a layer of complexity for developers trying to troubleshoot application problems and dependencies.

Embedded systems often require a careful eye to where memory resources are being spent, especially runtime memory utilization like stack and heap memories. This article is intended to shed some light on strategies for measuring stack memory usage on a small embedded system.

Selecting a build system is an essential decision when creating a project. Changing is always painful, especially in a mature repository. Therefore the choice should be made carefully. With this article, I will try to describe a few advantages of what Bazel can provide in the context of an embedded repository and show how to set up a build environment with a cross compiler from scratch.

Zephyr includes many built-in features like stacks for networking and BLE, Flash storage APIs, and many kernel services. These components allow you to quickly get up and running with a project and maintain less code! Taking advantage of these is a huge win for small firmware teams and was a huge motivation in bringing Zephyr to my teams. This post covers Zephyr’s built-in ring buffer API, a component commonly used in producer-consumer scenarios.