DevConf.CZ 2020

With the ever increasing presence of smart devices all around our personal and professional lives, privacy seems to be becoming one of the most vulnerable aspects of our everyday lives. This session aims to address some of the biggest privacy concerns that we are faced with everyday in the age of Internet of things. The primary aim of this session is to spread awareness about digital privacy issues while also sharing some of the simplest tools and techniques by which we can take better control of our digital lives and our personal information.

The session presents the author's DIY experience with building components for a smart home.
Going through building simple components like sensors integrating them into data collection and controlling the environment.
All of this is done without any intervention to the building itself. The listener can expect to see the failures of my first designs.
Changes made to the HW and evolution choices of tools and libraries that I have used over time.
Demonstration of the current state of my smart home hence this is every week changing project.
Technologies that will be utilized in January does not have to reflect what I am using right now.
But choices like MQTT, ESPhome, motionEyE and Arduino will be probably always part of it.

Edge computing helps to break beyond the limitations imposed by, now, traditional cloud solutions. Some of the reasons might be privacy concerns, reducing the need for heavy processing resources, reducing the amount of data that is sent over the network – just to mention a few.

In this talk, we will introduce the concept and benefits of Edge Computing, how such devices can be built using affordable hardware and we will look at examples on how IoT Edge devices are in practice as well as comparing Edge Computing with other options.

As a live demo, we will be showing our system to process video streams on the Edge and designed to keep the privacy of the people intact; without leaking faces and identity of the people to a 3rd party. In addition to standard video processing, we will show ML techniques and models that are processed on to the device to demonstrate the power and flexibility of the Edge devices nowadays.

Virtual Kubelet is an open-source Kubernetes kubelet implementation that masquerades as a kubelet for the purposes of connecting Kubernetes to other APIs.
What if we run it on Raspberry Pi, with Podman as container runtime and Fedora ARM as an operating system. And schedule workloads from our Kubernetes cluster?
Small side project in progress, where IoT gets to know Kubernetes or “Connect your fridge to the Kubernetes cluster in the cloud”