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 2 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 2 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.
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almost 2 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
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almost 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
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almost 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.
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I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time? 
There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).
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- Make it easy to install
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Resources
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- 1x RTL2832U DVB-T dongle
- 1x MicroSD card
- https://github.com/antirez/dump1090
- https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
- https://github.com/wiedehopf/tar1090
Project status (2024-11-22)
So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).
Current status:
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- Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
- Local web interface (tar1090) to easily visualize the results
- Cockpit pre-configured to ease maintenance
What's missing:
- MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
- https://g7.github.io/adsbreceiver/
- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
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Description
ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration
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Overview
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Features
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Description
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Non Goals
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Resources
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Description
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Resources
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Description
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Goals
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Resources
Core concepts:
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- Admission Controller: An admission controller will detect "bootled" CRDs being installed and replace the resource name used to store them.
- Aggregated API Server: By analyzing the author of a request, the aggregated API server will determine the correct bottle and route the request accordingly, making it transparent for the user.
Small healthcheck tool for Longhorn by mbrookhuis
Project Description
We have often problems (e.g. pods not starting) that are related to PVCs not running, cluster (nodes) not all up or deployments not running or completely running. This all prevents administration activities. Having something that can regular be run to validate the status of the cluster would be helpful, and not as of today do a lot of manual tasks.
As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.
This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.
Goal for this Hackweek
At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.
Overview
This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:
node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.
If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.
The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.
The code is writen for Python 3.11, but will also work on 3.6 (the default with SLES15.x). There is a venv present that will contain all needed packages. Also, the script can be run on the cluster itself or any other linux server.
Installation
To install this project, perform the following steps:
- Create the directory /opt/k8s-check
mkdir /opt/k8s-check
- Copy all the file to this directory and make the following changes:
chmod +x k8s-check.py
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Description
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Goals
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- Kiwi and EIB need to be running as containers.
- Configuration options for the images used for Kiwi and EIB build.
- X86 and ARM64 Support.
- SUSE Manager 4.3 and 5.X Support.
- SLES 15 SP6 / SL Micro 6.0 and SL Micro 6.1 Support.
Outcome
- Change the Kiwi build process to use Podman with the Kiwi image registry.suse.com/bci/kiwi:10.1.10
- Change the Edge Image Builder to produce a combustion-only ISO
- Extract the contents and write them to a dedicated /OEM partition integrated via Kiwi into the ISO Kiwi creates.
Sources and PRs
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- https://github.com/suse-edge/edge-image-builder/pull/618
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Description
The Uyuni server is provided as a container, but we still require it to run on Leap Micro? This is not how people expect to use containerized applications, so it would be great if we tested other host OSs and enabled them by providing builds of necessary tools for (e.g. mgradm). Interesting candidates should be:
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- Cent OS 7
- Ubuntu
- ???
Goals
Make it really easy for anyone to run the Uyuni containerized server on whatever OS they want (with support for containers of course).
SUSE AI Meets the Game Board by moio
Use tabletopgames.ai’s open source TAG and PyTAG frameworks to apply Statistical Forward Planning and Deep Reinforcement Learning to two board games of our own design. On an all-green, all-open source, all-AWS stack!
Results: Infrastructure Achievements
We successfully built and automated a containerized stack to support our AI experiments. This included:
- a Fully-Automated, One-Command, GPU-accelerated Kubernetes setup: we created an OpenTofu based script, tofu-tag, to deploy SUSE's RKE2 Kubernetes running on CUDA-enabled nodes in AWS, powered by openSUSE with GPU drivers and gpu-operator
- Containerization of the TAG and PyTAG frameworks: TAG (Tabletop AI Games) and PyTAG were patched for seamless deployment in containerized environments. We automated the container image creation process with GitHub Actions. Our forks (PRs upstream upcoming):
./deploy.sh and voilà - Kubernetes running PyTAG (k9s, above) with GPU acceleration (nvtop, below)
Results: Game Design Insights
Our project focused on modeling and analyzing two card games of our own design within the TAG framework:
- Game Modeling: We implemented models for Dario's "Bamboo" and Silvio's "Totoro" and "R3" games, enabling AI agents to play thousands of games ...in minutes!
- AI-driven optimization: By analyzing statistical data on moves, strategies, and outcomes, we iteratively tweaked the game mechanics and rules to achieve better balance and player engagement.
- Advanced analytics: Leveraging AI agents with Monte Carlo Tree Search (MCTS) and random action selection, we compared performance metrics to identify optimal strategies and uncover opportunities for game refinement .
- more about Bamboo on Dario's site
- more about R3 on Silvio's site (italian, translation coming)
- more about Totoro on Silvio's site
A family picture of our card games in progress. From the top: Bamboo, Totoro, R3
Results: Learning, Collaboration, and Innovation
Beyond technical accomplishments, the project showcased innovative approaches to coding, learning, and teamwork:
- "Trio programming" with AI assistance: Our "trio programming" approach—two developers and GitHub Copilot—was a standout success, especially in handling slightly-repetitive but not-quite-exactly-copypaste tasks. Java as a language tends to be verbose and we found it to be fitting particularly well.
- AI tools for reporting and documentation: We extensively used AI chatbots to streamline writing and reporting. (Including writing this report! ...but this note was added manually during edit!)
- GPU compute expertise: Overcoming challenges with CUDA drivers and cloud infrastructure deepened our understanding of GPU-accelerated workloads in the open-source ecosystem.
- Game design as a learning platform: By blending AI techniques with creative game design, we learned not only about AI strategies but also about making games fun, engaging, and balanced.
Last but not least we had a lot of fun! ...and this was definitely not a chatbot generated line!
The Context: AI + Board Games
Technical talks at universities by agamez
Description
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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
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ClusterOps - Easily install and manage your personal kubernetes cluster by andreabenini
Description
ClusterOps is a Kubernetes installer and operator designed to streamline the initial configuration
and ongoing maintenance of kubernetes clusters. The focus of this project is primarily on personal
or local installations. However, the goal is to expand its use to encompass all installations of
Kubernetes for local development purposes.
It simplifies cluster management by automating tasks and providing just one user-friendly YAML-based
configuration config.yml.
Overview
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- Automated Setup: Automates initial cluster configuration, including network settings, storage provisioning, special requirements (for example GPUs) and essential components installation.
- Ongoing Maintenance: Performs routine maintenance tasks such as upgrades, security updates, and resource monitoring.
- Extensibility: Easily extend functionality with custom plugins and configurations.
- Self-Healing: Detects and recovers from common cluster issues, ensuring stability, idempotence and reliability. Same operation can be performed multiple times without changing the result.
- Discreet: It works only on what it knows, if you are manually configuring parts of your kubernetes and this configuration does not interfere with it you can happily continue to work on several parts and use this tool only for what is needed.
Features
- distribution and engine independence. Install your favorite kubernetes engine with your package
manager, execute one script and you'll have a complete working environment at your disposal.
- Basic config approach. One single
config.ymlfile with configuration requirements (add/remove features): human readable, plain and simple. All fancy configs managed automatically (ingress, balancers, services, proxy, ...). - Local Builtin ContainerHub. The default installation provides a fully configured ContainerHub available locally along with the kubernetes installation. This configuration allows the user to build, upload and deploy custom container images as they were provided from external sources. Internet public sources are still available but local development can be kept in this localhost server. Builtin ClusterOps operator will be fetched from this ContainerHub registry too.
- Kubernetes official dashboard installed as a plugin, others planned too (k9s for example).
- Kubevirt plugin installed and properly configured. Unleash the power of classic virtualization (KVM+QEMU) on top of Kubernetes and manage your entire system from there, libvirtd and virsh libs are required.
- One operator to rule them all. The installation script configures your machine automatically during installation and adds one kubernetes operator to manage your local cluster. From there the operator takes care of the cluster on your behalf.
- Clean installation and removal. Just test it, when you are done just use the same program to uninstall everything without leaving configs (or pods) behind.
Planned features (Wishlist / TODOs)
- Containerized Data Importer (CDI). Persistent storage management add-on for Kubernetes to provide a declarative way of building and importing Virtual Machine Disks on PVCs for
ADS-B receiver with MicroOS by epaolantonio
I would like to put one of my spare Raspberry Pis to good use, and what better way to see what flies above my head at any time?
There are various ready-to-use distros already set-up to provide feeder data to platforms like Flightradar24, ADS-B Exchange, FlightAware etc... The goal here would be to do it using MicroOS as a base and containerized decoding of ADS-B data (via tools like dump1090) and web frontend (tar1090).
Goals
- Create a working receiver using MicroOS as a base, and containers based on Tumbleweed
- Make it easy to install
- Optimize for maximum laziness (i.e. it should take care of itself with minimum intervention)
Resources
- 1x Small Board Computer capable of running MicroOS
- 1x RTL2832U DVB-T dongle
- 1x MicroSD card
- https://github.com/antirez/dump1090
- https://github.com/flightaware/dump1090 (dump1090 fork by FlightAware)
- https://github.com/wiedehopf/tar1090
Project status (2024-11-22)
So I'd say that I'm pretty satisfied with how it turned out. I've packaged readsb (as a replacement for dump1090), tar1090, tar1090-db and mlat-client (not used yet).
Current status:
- Able to set-up a working receiver using combustion+ignition (web app based on Fuel Ignition)
- Able to feed to various feeds using the Beast protocol (Airplanes.live, ADSB.fi, ADSB.lol, ADSBExchange.com, Flyitalyadsb.com, Planespotters.net)
- Able to feed to Flightradar24 (initial-setup available but NOT tested! I've only tested using a key I already had)
- Local web interface (tar1090) to easily visualize the results
- Cockpit pre-configured to ease maintenance
What's missing:
- MLAT (Multilateration) support. I've packaged mlat-client already, but I have to wire it up
- FlightAware support
Give it a go at https://g7.github.io/adsbreceiver/ !
Project links
- https://g7.github.io/adsbreceiver/
- https://github.com/g7/adsbreceiver
- https://build.opensuse.org/project/show/home:epaolantonio:adsbreceiver
Grapesss: a physical Shamir's Secret Sharing application [ESP32-C3 + Mobile] by ecandino
Description
A couple of years ago I created StegoSecretS, a small cli used to encrypt and split a secret into multiple keys, using the Shamir's Secret Sharing algorithm.
The idea is to re-implement the project using physical devices. One device alone will be useless, but when close together they can be used to decrypt the secret.
On a practical side the user encrypts the secret with a mobile application. The same application is used to split the secret, and load the partial keys into different micro-controllers. Another user will be able to decrypt the secret only having at least N devices close together (using the application).
I'm planning to use a couple of ESP32-C3 I bought, and build a very simple Android mobile application.
Goals
- Learn about Rust and micro-controllers (ESP32-C3)
- Learn about mobile applications (Android and Kotlin)
Resources
Build Edge Image Builder ISO with SUSE Manager by mweiss2
Description
With SUSE Manager, we can build OS Images using KIWI and container images. As we have Edge Image Builder, we want to see if it is possible to use SUSE Manager to build/customize OS Images by integrating Edge Image Builder as well.
Goals
To make the process easier for customers, a single-build pipeline that automatically adds the combustion and artifact files from the EIB process is desirable.
- Kiwi and EIB need to come from a Git Repository.
- Kiwi and EIB need to be running as containers.
- Configuration options for the images used for Kiwi and EIB build.
- X86 and ARM64 Support.
- SUSE Manager 4.3 and 5.X Support.
- SLES 15 SP6 / SL Micro 6.0 and SL Micro 6.1 Support.
Outcome
- Change the Kiwi build process to use Podman with the Kiwi image registry.suse.com/bci/kiwi:10.1.10
- Change the Edge Image Builder to produce a combustion-only ISO
- Extract the contents and write them to a dedicated /OEM partition integrated via Kiwi into the ISO Kiwi creates.
Sources and PRs
- https://github.com/Martin-Weiss/kiwi-image-micro-gpu-60
- https://github.com/suse-edge/edge-image-builder/pull/618
- https://github.com/uyuni-project/uyuni/pull/9507
Edge Image Builder and mkosi for Uyuni by oholecek
Description
One part of Uyuni system management tool is ability to build custom images. Currently Uyuni supports only Kiwi image builder.
Kiwi however is not the only image building system out there and with the goal to also become familiar with other systems, this projects aim to add support for Edge Image builder and systemd's mkosi systems.
Goals
Uyuni is able to
- provision EIB and mkosi build hosts
- build EIB and mkosi images and store them
Resources
- Uyuni - https://github.com/uyuni-project/uyuni
- Edge Image builder - https://github.com/suse-edge/edge-image-builder
- mkosi - https://github.com/systemd/mkosi
Small healthcheck tool for Longhorn by mbrookhuis
Project Description
We have often problems (e.g. pods not starting) that are related to PVCs not running, cluster (nodes) not all up or deployments not running or completely running. This all prevents administration activities. Having something that can regular be run to validate the status of the cluster would be helpful, and not as of today do a lot of manual tasks.
As addition (read enough time), we could add changing reservation, adding new disks, etc. --> This didn't made it. But the scripts can easily be adopted.
This tool would decrease troubleshooting time, giving admins rights to the rancher GUI and could be used in automation.
Goal for this Hackweek
At the end we should have a small python tool that is doing a (very) basic health check on nodes, deployments and PVCs. First attempt was to make it in golang, but that was taking to much time.
Overview
This tool will run a simple healthcheck on a kubernetes cluster. It will perform the following actions:
node check: This will check all nodes, and display the status and the k3s version. If the status of the nodes is not "Ready" (this should be only reported), the cluster will be reported as having problems
deployment check: This check will list all deployments, and display the number of expected replicas and the used replica. If there are unused replicas this will be displayed. The cluster will be reported as having problems.
pvc check: This check will list of all pvc's, and display the status and the robustness. If the robustness is not "Healthy", the cluster will be reported as having problems.
If there is a problem registered in the checks, there will be a warning that the cluster is not healthy and the program will exit with 1.
The script has 1 mandatory parameter and that is the kubeconf of the cluster or of a node off the cluster.
The code is writen for Python 3.11, but will also work on 3.6 (the default with SLES15.x). There is a venv present that will contain all needed packages. Also, the script can be run on the cluster itself or any other linux server.
Installation
To install this project, perform the following steps:
- Create the directory /opt/k8s-check
mkdir /opt/k8s-check
- Copy all the file to this directory and make the following changes:
chmod +x k8s-check.py