Build a network of ("edge") humidity sensors using Raspberry Pis with SenseHats and additional cheaper sensors
For our house, I want to make sure I can track the effectiveness of regularly ventilating the rooms by adding humidity sensors and tracking the measurements over time.
We've already started with this little project:
https://github.com/benediktwerner/humidity-logger
Goal for this Hackweek
The setup we built over the holidays works just fine, but there are a few practical issues and a few stretch goals I'd have:
With a Raspberry Pi plus the Sense Hat, a single sensor is pretty expensive and over-specced. Using a Raspberry Pi as the master is ok (especially as I already have two with two Sense Hats), but I'd like to add extra sensors that can be connected wirelessly directly to one of the Raspberry Pis using Bluetooth or WiFi. Those could either be ready to go or a combination of "bare" sensor and a cheap board like the Raspberry Pico W or a similar board (e.g., based on the ESP32).
Currently, there's only a Grafana dashboard with a "forever" history. Would love to add extra reporting, e.g., sending alerts when certain humidity thresholds are exceeded, archiving older data.
None of the setup is "SUSEfied" (using SUSE Linux images, k3s, Rancher, ...). I'd love to change that, so that the setup can be used as a showcase for SUSE Edge. The stretch goal would be to make the SUSE version at least as easy to use as the current Raspberry Pi OS setup.
I'm looking for contributors who want to hack on either the hardware part (building an affordable Bluetooth or WiFi humidity/temperature sensor from components) or the SUSEfied software stack or both.
The software stack has many areas to work on, from building out-of-the box containers that can be deployed from Rancher to improving the Grafana dashboards.
Resources
- https://github.com/benediktwerner/humidity-logger
- https://www.raspberrypi.com/products/sense-hat/
- https://www.raspberrypi.com/documentation/microcontrollers/raspberry-pi-pico.html
- https://community.ibm.com/community/user/cloud/blogs/alexei-karve/2022/05/08/microshift-15
Looking for hackers with the skills:
raspberrypi esp32 microos k3s rancher elemental sensors grafana influxdb containers helm microcontroller edge
This project is part of:
Hack Week 22
Activity
Comments
-
almost 3 years ago by idefx | Reply
Hello! Have you check on the Home Assistant and ESPHome projects?
I run Home Assistant on a k3s cluster, with 2 raspberry pi 4 and 2 intel low-power (a VM inside a NAS and a NUC). Everything is on SLE Micro, and I use Rancher for the management of the cluster, and longhorn for persistent data. For the sensor part, I have a couple of Arduino m5 atoms lite. They support a variety of sensors, and with ESPHome, it is super easy to connect them to Home Assistant. Then you can design automations, mobile notification, etc. from Home Assistant, and even plug it to other services so you get a phone call if something goes wrong, for example.
Don't hesitate to reach out to me if you want to discuss this!
-
almost 3 years ago by joachimwerner | Reply
Thanks for the great pointers! We started off with a much smaller scope (no home automation, really just data gathering and visualisation), but it makes perfect sense to think of it in the context of home assistant for the future (e.g., so that a smart thermostat automatically shuts down the heating in the room when it's being ventilated). Will certainly get back to you with some questions.
-
almost 3 years ago by joachimwerner | Reply
Found this on how to get the Sense Hat to work on openSUSE: https://community.ibm.com/community/user/cloud/blogs/alexei-karve/2022/05/08/microshift-15
-
over 2 years ago by gpathak | Reply
Hi @joachimwerner For adding extra sensors, I found out that it can be done with DHT22 and ESP8266. Some information about interfacing DHT22 with ESP8266 can be found here: Getting Started With the ESP8266 and DHT22 Sensor
-
over 2 years ago by bigironman | Reply
An alternative solution might be using a Raspberry Pi Pico W with MicroPython and BME280 sensor (temperature, humidity, pressure). It is easy to program and you can integrate it into nearly everything via Wifi. I'm using it in combination with Home Assistant and MQTT.
Similar Projects
Cluster API Provider for Harvester by rcase
Project Description
The Cluster API "infrastructure provider" for Harvester, also named CAPHV, makes it possible to use Harvester with Cluster API. This enables people and organisations to create Kubernetes clusters running on VMs created by Harvester using a declarative spec.
The project has been bootstrapped in HackWeek 23, and its code is available here.
Work done in HackWeek 2023
- Have a early working version of the provider available on Rancher Sandbox : *DONE *
- Demonstrated the created cluster can be imported using Rancher Turtles: DONE
- Stretch goal - demonstrate using the new provider with CAPRKE2: DONE and the templates are available on the repo
DONE in HackWeek 24:
- Add more Unit Tests
- Improve Status Conditions for some phases
- Add cloud provider config generation
- Testing with Harvester v1.3.2
- Template improvements
- Issues creation
DONE in 2025 (out of Hackweek)
- Support of ClusterClass
- Add to
clusterctl
community providers, you can add it directly withclusterctl
- Testing on newer versions of Harvester v1.4.X and v1.5.X
- Support for
clusterctl generate cluster ...
- Improve Status Conditions to reflect current state of Infrastructure
- Improve CI (some bugs for release creation)
Goals for HackWeek 2025
- FIRST and FOREMOST, any topic is important to you
- Add e2e testing
- Certify the provider for Rancher Turtles
- Add Machine pool labeling
- Add PCI-e passthrough capabilities.
- Other improvement suggestions are welcome!
Thanks to @isim and Dominic Giebert for their contributions!
Resources
Looking for help from anyone interested in Cluster API (CAPI) or who wants to learn more about Harvester.
This will be an infrastructure provider for Cluster API. Some background reading for the CAPI aspect:
A CLI for Harvester by mohamed.belgaied
[comment]: # Harvester does not officially come with a CLI tool, the user is supposed to interact with Harvester mostly through the UI [comment]: # Though it is theoretically possible to use kubectl to interact with Harvester, the manipulation of Kubevirt YAML objects is absolutely not user friendly. [comment]: # Inspired by tools like multipass from Canonical to easily and rapidly create one of multiple VMs, I began the development of Harvester CLI. Currently, it works but Harvester CLI needs some love to be up-to-date with Harvester v1.0.2 and needs some bug fixes and improvements as well.
Project Description
Harvester CLI is a command line interface tool written in Go, designed to simplify interfacing with a Harvester cluster as a user. It is especially useful for testing purposes as you can easily and rapidly create VMs in Harvester by providing a simple command such as:
harvester vm create my-vm --count 5
to create 5 VMs named my-vm-01
to my-vm-05
.
Harvester CLI is functional but needs a number of improvements: up-to-date functionality with Harvester v1.0.2 (some minor issues right now), modifying the default behaviour to create an opensuse VM instead of an ubuntu VM, solve some bugs, etc.
Github Repo for Harvester CLI: https://github.com/belgaied2/harvester-cli
Done in previous Hackweeks
- Create a Github actions pipeline to automatically integrate Harvester CLI to Homebrew repositories: DONE
- Automatically package Harvester CLI for OpenSUSE / Redhat RPMs or DEBs: DONE
Goal for this Hackweek
The goal for this Hackweek is to bring Harvester CLI up-to-speed with latest Harvester versions (v1.3.X and v1.4.X), and improve the code quality as well as implement some simple features and bug fixes.
Some nice additions might be: * Improve handling of namespaced objects * Add features, such as network management or Load Balancer creation ? * Add more unit tests and, why not, e2e tests * Improve CI * Improve the overall code quality * Test the program and create issues for it
Issue list is here: https://github.com/belgaied2/harvester-cli/issues
Resources
The project is written in Go, and using client-go
the Kubernetes Go Client libraries to communicate with the Harvester API (which is Kubernetes in fact).
Welcome contributions are:
- Testing it and creating issues
- Documentation
- Go code improvement
What you might learn
Harvester CLI might be interesting to you if you want to learn more about:
- GitHub Actions
- Harvester as a SUSE Product
- Go programming language
- Kubernetes API
Rancher/k8s Trouble-Maker by tonyhansen
Project Description
When studying for my RHCSA, I found trouble-maker, which is a program that breaks a Linux OS and requires you to fix it. I want to create something similar for Rancher/k8s that can allow for troubleshooting an unknown environment.
Goals for Hackweek 25
- Update to modern Rancher and verify that existing tests still work
- Change testing logic to populate secrets instead of requiring a secondary script
- Add new tests
Goals for Hackweek 24 (Complete)
- Create a basic framework for creating Rancher/k8s cluster lab environments as needed for the Break/Fix
- Create at least 5 modules that can be applied to the cluster and require troubleshooting
Resources
- https://github.com/celidon/rancher-troublemaker
- https://github.com/rancher/terraform-provider-rancher2
- https://github.com/rancher/tf-rancher-up
- https://github.com/rancher/quickstart
Flaky Tests AI Finder for Uyuni and MLM Test Suites by oscar-barrios
Description
Our current Grafana dashboards provide a great overview of test suite health, including a panel for "Top failed tests." However, identifying which of these failures are due to legitimate bugs versus intermittent "flaky tests" is a manual, time-consuming process. These flaky tests erode trust in our test suites and slow down development.
This project aims to build a simple but powerful Python script that automates flaky test detection. The script will directly query our Prometheus instance for the historical data of each failed test, using the jenkins_build_test_case_failure_age
metric. It will then format this data and send it to the Gemini API with a carefully crafted prompt, asking it to identify which tests show a flaky pattern.
The final output will be a clean JSON list of the most probable flaky tests, which can then be used to populate a new "Top Flaky Tests" panel in our existing Grafana test suite dashboard.
Goals
By the end of Hack Week, we aim to have a single, working Python script that:
- Connects to Prometheus and executes a query to fetch detailed test failure history.
- Processes the raw data into a format suitable for the Gemini API.
- Successfully calls the Gemini API with the data and a clear prompt.
- Parses the AI's response to extract a simple list of flaky tests.
- Saves the list to a JSON file that can be displayed in Grafana.
- New panel in our Dashboard listing the Flaky tests
Resources
- Jenkins Prometheus Exporter: https://github.com/uyuni-project/jenkins-exporter/
- Data Source: Our internal Prometheus server.
- Key Metric:
jenkins_build_test_case_failure_age{jobname, buildid, suite, case, status, failedsince}
. - Existing Query for Reference:
count by (suite) (max_over_time(jenkins_build_test_case_failure_age{status=~"FAILED|REGRESSION", jobname="$jobname"}[$__range]))
. - AI Model: The Google Gemini API.
- Example about how to interact with Gemini API: https://github.com/srbarrios/FailTale/
- Visualization: Our internal Grafana Dashboard.
- Internal IaC: https://gitlab.suse.de/galaxy/infrastructure/-/tree/master/srv/salt/monitoring
Technical talks at universities by agamez
Description
This project aims to empower the next generation of tech professionals by offering hands-on workshops on containerization and Kubernetes, with a strong focus on open-source technologies. By providing practical experience with these cutting-edge tools and fostering a deep understanding of open-source principles, we aim to bridge the gap between academia and industry.
For now, the scope is limited to Spanish universities, since we already have the contacts and have started some conversations.
Goals
- Technical Skill Development: equip students with the fundamental knowledge and skills to build, deploy, and manage containerized applications using open-source tools like Kubernetes.
- Open-Source Mindset: foster a passion for open-source software, encouraging students to contribute to open-source projects and collaborate with the global developer community.
- Career Readiness: prepare students for industry-relevant roles by exposing them to real-world use cases, best practices, and open-source in companies.
Resources
- Instructors: experienced open-source professionals with deep knowledge of containerization and Kubernetes.
- SUSE Expertise: leverage SUSE's expertise in open-source technologies to provide insights into industry trends and best practices.