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This is Leela. She is a 7 year old lilac white British short hair cat that lives with us. Leela had a sister who used to live with us as well but she developed a heart condition and passed away last year. Witnessing how quickly such conditions develop and evaluate we thought that we can do something to monitor Leelas health a bit to just have some sort of pre-alert if something is changing.
Kid in a Candystore
As this Internet of Things is becoming a real thing these days I found myself in a candy store when I’ve encountered that there are a couple of really really cheap options to get a small PCB with input/output connectors into my house WiFi network.
One of the main actors of this story is the so called ESP8266. A very small and affordable system-on-a-chip that allows you to run small code portions and connect itself to a wireless network. Even better it comes with several inputs that can be used to do all sorts of wonderful things.
And so it happened that we needed to know the weight of our cat. She seemed to get a bit chubby over time and having a point of reference weight would help to get her back in shape. If you every tried to weigh a cat you know that it’s much easier said than done.
The alternative was quickly brought up: Build a WiFi-connected scale to weigh her litter box every time she is using it. And since I’ve recently bought an evaluation ESP8266 I just had to figure out how to build a scale. Looking around the house I’ve found a broken human scale (electronics fried). Maybe it could be salvaged as a part donor?
A day later I’ve done all the reading on that there is a thing called “load-cell”. Those load cells can be bought in different shapes and sizes and – when connected to a small ADC they deliver – well – a weight value.
I cracked the human scale open and tried to see what was broken. It luckily turned out to have completely fried electronics but the load-cells where good to go.
Look at this load cell:
That brought down the part list of this project to:
- an ESP8266 – an Adafruit Huzzah in my case
- a HX711 ADC board to amplify and prepare the signal from the load-cells
- a human scale with just enough space in the original case to fit the new electronics into and connect everything.
The HX711 board was the only thing I had to order hardware wise – delivered the next day and it was a matter of soldering things together and throwing in a small Arduino IDE sketch.
My soldering and wiring skills are really sub-par. But it worked from the get-go. I was able to set-up a small Arduino sketch and get measurements from the load-cells that seemed reasonable.
Now the hardware was all done – almost too easy. The software would be the important part now. In order to create something flexible I needed to make an important decision: How would the scale tell the world about it’s findings?
Two basic options: PULL or PUSH?
Pull would mean that the ESP8266 would offer a webservice or at least web-server that exposes the measurements in one way or the other. It would mean that a client needs to poll for a new number in regular intervals.
Push would mean that the ESP8266 would connect to a server somewhere and whenever there’s a meaningful measurement done it would send that out to the server. With this option there would be another decision of which technology to use to push the data out.
Now a bit of history: At that time I was just about to re-implement the whole house home automation system I was using for the last 6 years with some more modern/interoperable technologies. For that project I’ve made the decision to have all events (actors and sensors) as well as some additional information being channeled into MQTT topics.
Let’s refer to Wikipedia on this:
“MQTT1 (formerly MQ Telemetry Transport) is an ISO standard (ISO/IEC PRF 20922) publish-subscribe-based “lightweight” messaging protocol for use on top of the TCP/IP protocol. It is designed for connections with remote locations where a “small code footprint” is required or the network bandwidth is limited. The publish-subscribe messaging pattern requires a message broker. Thebroker is responsible for distributing messages to interested clients based on the topic of a message. Andy Stanford-Clark and Arlen Nipper of Cirrus Link Solutions authored the first version of the protocol in 1999.”
Something build for oil-pipelines can’t be wrong for your house – can it?
So MQTT uses the notation of a “topic” to sub-address different entities within it’s network. Think of a topic as just a simple address like “house/litterbox/weight”. And with that topic MQTT allows you to set a value as well.
The alternative to MQTT would have been things like WebSockets to push events out to clients. The decision for the home-automation was done towards MQTT and so far it seems to have been the right call. More and more products and projects available are also focussing on using MQTT as their main message transport.
For the home automation I had already set-up a demo MQTT broker in the house – and so naturally the first call for the litterbox project was to utilize that.
The folks of Adafruit provide the MQTT library with their hardware and within minutes the scale started to send it’s measurements into the “house/litterbox/weight” topic of the house MQTT broker.
Some tweaking and hacking later the litterbox was put together and the actual litterbox set on-top.
Since Adafruit offers platform to also send MQTT messages towards and create neat little dashboards I have set-up a little demo dashboard that shows a selection of data being pushed from the house MQTT broker to the Adafruit.io MQTT broker.
These are the raw values which are sent into the weight topic:
You can access it here: https://io.adafruit.com/bietiekay/stappenbach
So the implementation done and used now is very simple. On start-up the ESP8622 initialises and resets the weight to 0. It’ll then do frequent weight measurements at the rate it’s configured in the source code. Those weight measurements are being monitored for certain criteria: If there’s a sudden increase it is assumed that “the cat entered the litterbox”. The weight is then monitored and averaged over time. When there’s a sudden drop of weight below a threshold that last “high” measurement is taken as the actual cat weight and sent out to a /weight topic on MQTT. The regular measurements are sent separately to also a configurable MQTT topic.
You can grab the very ugly source code of the Arduino sketch here: litterbox_sourcecode
And off course with a bit of logic this would be the calculated weight topic:
Of course it is not enough to just send data into MQTT topics and be done with it. Of course you want things like logging and data storage. Eventually we also wanted to get some sort of notification when states change or a measurement was taken.
MQTT, the cloud and self-hosted
Since MQTT is enabling a lot of scenarios to implement such actions I am going to touch just the two we are using for our house.
- We wanted to get a push notification to our phones whenever a weight measurement was taken – essentially whenever the cat has done something in the litterbox. The easiest solution: Set-Up a recipe on If This Than That (IFTTT) and use PushOver to send out push notifications to whatever device we want.
- To log and monitor in some sort of a dashboard the easiest solution seemed to be Adafruits offer. Of course hosted inside our house a combination of InfluxDB to store, Telegraf to gather and insert into InfluxDB and Chronograf to render nice graphs was the best choice.
Since most of the above can be done in the cloud (as of: outside the house with MQTT being the channel out) or inside the house with everything self-hosted. Some additional articles will cover these topics on this blog later.
There’s lots of opportunity to add more logic but as far as our experiments and requirements go we are happy with the results so far – we now regularly get a weight and the added information of how often the cat is using her litterbox. Especially for some medical conditions this is quite interesting and important information to have.
I am a frequent podcast live-stream listener. And being that I am enjoying the awesome service called xenim streaming network.
Any Podcast producer can join the xsn and with that can live-stream his own Podcast while recording. It’s CDN is based on voluntarily provided resources and pretty rock-solid as far as my experience with it goes.
Since I am a frequent user of this – and I’ve got that gorgeous SONOS hardware scattered around my house – I thought I need to have that service integrated into my SONOS set.
The SONOS system knows the concept of “Music Services”. There are quite a lot of them but xsn is missing. But SONOS is awesome and they got an API!
Unfortunately the API documentation is hidden behind a NDA wall so that would be a no-go. What’s not hidden is what the SONOS controllers have to discuss with all the existing services. Most of the time these do not use HTTPS so we’re free to listen to the chatters. I did just that and was able, for the sake of interoperability, to reverse engineer the SONOS SMAPI as far as it is necessary to make my little xsn Music Service work.
As usual you can get the source-code distributed freely through Github. If you’re not into that sort of compiling and programming things, you are invited to use my free-of-charge provided service. To set it up on your home SONOS just follow these simple steps:
Step 1: Start your SONOS Controller Application and find out the IP address of your SONOS.
Click on “About My Sonos System” and check the IP address written next to the “Associated ZP”.
Step 2: Add the xsn Music Service.
By opening a browser window and browsing to: http://<your-associated-zp-ip>:1400/customsd.htm
When you’re there – fill out the fields as below. The SID is either 255, or if you used that previously, something between 240-253. The service name is “xenim streaming network”. The Endpoint URL and Secure Endpoint URL both are http://xsn.schrankmonster.de/xsn
Set the Polling interval to 30 seconds. Click on the Anonymous Authentication SOAP header policy and you’re good to go. Click on “send” to finish.
Step 3: Add the new Music Service to your SONOS Controller.
Click on “Add Music Services” and click through until you see “xenim streaming network”. Add the service and you’re set!
p.s.: It’s normal that the service icon is a question mark.
Step 4: Enjoy Live Podcasts!
I am using an external podcast download tool to stay updated on all podcasts I subscribed to. For this purpose SubSonic is a good choice – actually for a lot more also.
One of the quirks of the SONOS products is that Podcasts are not really well supported. In fact there is no support at all.
So I wrote a tool that extends the SONOS players with the functionality to “remember” play positions within audiobooks and podcasts. Now what’s left to properly have podcasts supported is a view of the most recently updated podcasts. Wouldn’t it be nice to have a “Folder View” in the SONOS controller of what’s new across all the different podcasts you are subscribed to?
Now here’s the trick:
Use a small script on any RaspberryPi in the house to dynamically create hardlinks to the podcasts files in a “Recently Updated Podcasts” folder.
The script is something like this:
find /where-your-podcasts-are/ -type f -printf ‘%TY-%Tm-%Td %TT %p\n’ | sort | tail -n 25 | cut -c 32- | sed -e “s/^/ln \”/” -e “s/$/\”/” -e “s/$/ \”\/recentPodcasts\/\”/” | sh
This short line will go through all folders and subfolders in /where-your-podcasts-are/ and then create Hardlinks in /recentPodcasts to the most recent 25 files.
That way, and when /recentPodcasts/ is made accessible to your SONOS controllers, you’ll have something like this:
After setting up Boblight on two TVs in the house – one with 50 and one with 100 LEDs – I’ve used it for the last 5 months on a daily basis almost.
First of all now every screen that does not come with “added color-context” on the wall seems off. It feels like something is missing. Second of all it has made watching movies in a dark room much more enjoyable.
The only concerning factor of the past months was that the RaspberryPi does not come with a lot of computational horse-power and thus it has been operating at it’s limits all the time. With 95-99% CPU usage there’s not a lot of headroom for unexpected bitrate spikes and what-have-you.
So from time to time the Pis where struggling. With 10% CPU usage for the 50 LEDs and 19% CPU usage for the 100 LEDs set-up there was just not enough CPU power for some movies or TV streams in Full-HD.
So since even overclocking only slightly improved the problem of Boblight using up the precious CPU cycles for a fancy light-show I started looking around for alternatives.
“Hyperion is an opensource ‘AmbiLight’ implementation controlled using the RaspBerry Pi running Raspbmc. The main features of Hyperion are:
- Low CPU load. For a led string of 50 leds the CPU usage will typically be below 1.5% on a non-overclocked Pi.
- Json interface which allows easy integration into scripts.
- A command line utility allows easy testing and configuration of the color transforms (Transformation settings are not preserved over a restart at the moment…).
- Priority channels are not coupled to a specific led data provider which means that a provider can post led data and leave without the need to maintain a connection to Hyperion. This is ideal for a remote application (like our Android app).
- HyperCon. A tool which helps generate a Hyperion configuration file.
- XBMC-checker which checks the playing status of XBMC and decides whether or not to capture the screen.
- Black border detector.
- A scriptable effect engine.
- Generic software architecture to support new devices and new algorithms easily.
Especially the Low CPU load did raise interest in my side.
Setting Hyperion up is easy if you just follow the very straight-forward Installation Guide. On Raspbmc the set-up took me 2 minutes at most.
If you got everything set-up on the Pi you need to generate a configuration file. It’s a nice JSON formatted config file that you do not need to create on your own – Hyperion has a nice configuration tool. Hypercon:
So after 2 more minutes the whole thing was set-up and running. Another 15 minutes of tweaking here and there and Hyperion replaced Boblight entirely.
What have I found so far?
- Hyperions network interfaces are much more controllable than those from Boblight. You can use remote clients like on iPhone / Android to set colors and/or patterns.
- It’s got effects for screen-saving / mood-lighting!
- It really just uses a lot less CPU resources. Instead of 19% CPU usage for 100 LEDs it’s down to 3-4%. That’s what I call a major improvement
- The processing filters that you can add really add value. Smoothing everything so that you do not get bright flashed when content flashes on-screen is easy to do and really helps with the experience.
All in all Hyperion is a recommended replacement for boblight. I would not want to switch back.
Source 1: Setting up Boblight
Source 2: https://github.com/tvdzwan/hyperion/wiki/Installation
Airplay allows you to conveniently play music and videos over the air from your iOS or Mac OS X devices on remote speakers.
Since we just recently “migrated” almost all audio equipment in the house to SONOS multi-room audio we were missing a bit the convenience of just pushing a button on the iPad or iPhones to stream audio from those devices inside the household.
To retrofit the Airplay functionality there are two options I know of:
1: Get Airplay compatible hardware and connect it to a SONOS Input.
You have to get Airplay hardware (like the Airport Express/Extreme,…) and attach it physically to one of the inputs of your SONOS Set-Up. Typically you will need a SONOS Play:5 which has an analog input jack.
2: Set-Up a RaspberryPi with NodeJS + AirSonos as a software-only solution
You will need a stock RaspberryPi online in your home network. Of course this can run on virtually any other device or hardware that can run NodeJS. For the Pi setting it up is a fairly straight-forward process:
You start with a vanilla Raspbian Image. Update everything with:
sudo apt-get update
sudo apt-get upgrade
Then install NodeJS according to this short tutorial. To set-up the AirSonos software you will need to install additional avahi software. Especially this was needed for my install:
sudo apt-get install git-all libavahi-compat-libdnssd-dev
You then need to get the AirSonos software:
sudo npm install airsonos -g
After some minutes of wait time and hard work by the Pi you will be able to start AirSonos.
And it’ll come up with an enumeration of all active rooms.
And on all your devices it’ll show up like this:
“The internet of things” is a buzzword used more and more. It means that things around you are connected to the (inter)network and therefore can talk to each other and, when combined, offer fantastic new opportunities.
So NodeRed is a NodeJS based toolset that allows you to create so called “flows” (see picture above). Those flows determine what reacts and happens when things happen. Fantastic, told you!
How to fix a mono CS0589 Internal compiler error during parsingSystem.FormatException error on the RaspberryPi
When you want to compile some C# code using MONO on Linux on your RaspberryPi and you encounter this strange error message:
error CS0589: Internal compiler error during parsingSystem.FormatException
You need to do:
- Update your Debian by running:
sudo apt-get upgrade
sudo apt-get update
- Upgrade your RaspberryPi firmware:
- Reboot your RaspberryPi
- Retry compiling – should work now.
The reason for Mono to crap out like above: Previous Mono versions and RaspberryPi firmwares where not compatible due to one side using HardFP and the other not.
How to install NodeJS and NPM on the RaspberryPi without getting “Illegal Instruction” error messages
I tried a couple of times to compile NodeJS on the RaspberryPi and failed miserably. It not only takes ages to compile NodeJS on the Pi. After the successful compile and install run most of the time running it just results in an error message “Illegal Instruction” or “Ungültiger Maschinencode”.
Now there’s a pretty easy way to do that on your own. Run these commands:
After the download is finished successfully you can install it by running this as root:
sudo dpkg -i node_latest_armhf.deb
This will have installed a relatively new NodeJS built as well as NPM on your RaspberryPi. Don’t panic when NPM is slower than you would expect… just be patient.