Category Archives: coding

Build a wireless MQTT temperature and humidity sensor for your Home Assistant

Over the last months, I became more and more addicted to Home Assistant (Hass.io) and MQTT low cost wireless sensors. I was already familiar with several home and industrial automation systems that all come with a certain hardware (and price) and build upon a completely proprietary software stack. So long story short, I was searching for a good community-backed open source home automation system that is easy to set up and runs on my old Raspberry.

As home automation seems to be a broad area of interest I thought there should be hundreds of open source community projects out there. I was not as easy as I thought and there are not so many home automation projects out there. It seems as if the market is broadly dominated by large vendors that offer integrated solutions.

After some cumbersome fails I was finally able to find a real gem in the home automation area, which is called the Home Assistant, or short Hass.io. Home Assistant comes as a lightweight installation that perfectly fulfills following requirements:

  1. Its lightweight, low resource consuming
  2. Easy to set up
  3. Nice web interface, that also comes pretty well with my tablet and smartphone (no app required, responsive web UI is great on your mobile device too) See a live demo here.
  4. Lots of community components available (>1000), such as Alexa, IFTTT, Hue, Sonos, Cromecast, Webcam, and many more.
  5. Fully configurable through plaintext YAML files
  6. It comes with an integrated MQTT broker!
  7. Supports automation scripts, such as turn light on at sunset
  8. Best of all its written in Python and its open source

The first step towards building my own MQTT wireless weather station was to set up a Home Assistant instance on my old Linux laptop. If you already got Python3 running on your system, the set up process is pretty straight forward, just type:

python3 -m pip install homeassistant

After successful installation you just enter the .homeassistant configuration folder and adapt the .yaml configurations that control what your Home Assistant instance is showing and how elements are organized in Web UI.

The most important configuration files are configuration.yaml that contains the core configuration about sensors and components and groups.yaml that groups all your components into visual tabs within the UI. Within my installation i chose to use a default group, one for my living room and one for controlling my pool, as i is shown in the screenshot below:

As my screenshot already shows, my Home Assistant instance already contains some MQTT based sensors for continuously informing me about the temperature and humidity (outside, and in living room). You can put the sensor output into any of your configured tabs. The same sensor info can also be present in multiple tabs at the same time.

To add a new MQTT sensor into your core configuration file, simply add following sensor section into your core configuration.yaml file:

sensor:
  - platform: mqtt
    name: "Temperature"
    state_topic: "/home/outdoor/sensor1"
    value_template: "{{ value_json.temperature }}"
    unit_of_measurement: '°C'
  - platform: mqtt
    name: "Humidity"
    state_topic: "/home/outdoor/sensor1"
    value_template: "{{ value_json.humidity }}"
    unit_of_measurement: '%'

You can then show this newly added sensor value in any of your configured groups, as shown below:

default_view:
  name: Home
  view: yes
  entities:
    - sensor.airquality
    - sensor.temperature
    - sensor.humidity
    - sensor.yr_symbol
    - sun.sun
    - camera.mjpeg_camera
    - device_tracker.alice
    - device_tracker.bob
    - switch.robby
    - switch.lamp
indoor:
  name: Livingroom
  view: yes
  entities:
    - sensor.temperaturelivingroom
    - sensor.humiditylivingroom
    - media_player.livingroom
pool:
  name: Pool
  view: yes
  entities:
    - sensor.watertemperature
    - switch.poolcover
    - switch.poollight
    - switch.poolpump
    - switch.poolbot

Now its time to test if the sensor would show a value in case it receives an MQTT value through the configured MQTT topic. Therefore, Home Assistant offers a simple MQTT test message UI in which you can simulate any incoming MQTT message, as shown below. Just enter your MQTT topic and send a static value:

After a click on the ‘publish’ button those two values 30 and 70 will appear in your sensors for temperature and humidity. You can do that try-run for all of your MQTT bound sensors, which is a convenient feature for testing the server side functionality of your home automation.

Next step is to build a cheap temperature and humidity sensor that sends its measurements over WLAN to your Home Assistant MQTT broker. As base sensor board I decided to use an Esp32 microcontroller board that offers a cheap (~5 USD platform) with integrated WLAN stack and many digital and analog input pins. See below an image of the chosen Esp32 board:

The Esp32 board can easily be flashed over a USB cable and it runs with a standard Arduino bootloader. You can use your Arduino Studio to program your tiny Esp32 board. To measure the temperature and humidity, the combined digital DHT22 sensor was used, as shown below:

To connect the DHT22 sensor to your Esp32 board simply attach the Vin pin to the 3V pin of the Esp32 board, the Ground to any of the Ground pins and the signal pin to any of the Esp32 digital input pins.

Following Arduino code snippet shows how to initialize the DHT22 sensor and how to read and report the sensor value through a MQTT message:

#include <ESP8266WiFi.h>
#include <EEPROM.h>
#include <DHT.h>
#include <DHT_U.h>
#include <PubSubClient.h>
#include <ArduinoJson.h>

/* Globals used for business logic only */
#define MQTT_VERSION MQTT_VERSION_3_1_1
// MQTT: ID, server IP, port, username and password
const PROGMEM char* MQTT_CLIENT_ID = "sensor2_dht22_s";
const PROGMEM uint16_t MQTT_SERVER_PORT = 1883;
// MQTT: topic
const PROGMEM char* MQTT_SENSOR_TOPIC = "/home/house/sensor1";
// sleeping time
const PROGMEM uint16_t SLEEPING_TIME_IN_SECONDS = 60; // 10 minutes x 60 seconds
// DHT - D1/GPIO5
#define DHTPIN 5

#define DHTTYPE DHT22

DHT dht(DHTPIN, DHTTYPE);
WiFiClient wifiClient;
PubSubClient client(wifiClient);

/* Business logic */
// function called to publish the temperature and the humidity
void publishData(float p_temperature, float p_humidity, float p_airquality) {
    // create a JSON object
    StaticJsonBuffer<200> jsonBuffer;
    JsonObject& root = jsonBuffer.createObject();
    // INFO: the data must be converted into a string; a problem occurs when using floats...
    root["temperature"] = (String)p_temperature;
    root["humidity"] = (String)p_humidity;
    root["airquality"] = (String)p_airquality;
    root.prettyPrintTo(Serial);
    Serial.println("");
    /*
    {
    "temperature": "23.20" ,
    "humidity": "43.70"
    }
   */
    char data[200];
    root.printTo(data, root.measureLength() + 1);
    client.publish(MQTT_SENSOR_TOPIC, data, true);
    yield();
}

setup() {
    dht.begin();
    Serial.print("INFO: Connecting to ");
    WiFi.mode(WIFI_STA);
    WiFi.begin(cconfig.ssid, cconfig.pwd);
    while (WiFi.status() != WL_CONNECTED) {
        delay(500);
        Serial.print(".");
    }
    Serial.println("");
    Serial.println("INFO: WiFi connected");
    Serial.println("INFO: IP address: ");
    Serial.println(WiFi.localIP());
    // init the MQTT connection
    client.setServer(cconfig.mqtt, MQTT_SERVER_PORT);
}

 

void loop() {
    dht.begin();

    if (WiFi.status() != WL_CONNECTED) {
        WiFi.mode(WIFI_STA);
        WiFi.begin(cconfig.ssid, cconfig.pwd);
         
        // Reading temperature or humidity takes about 250 milliseconds!
        // Sensor readings may also be up to 2 seconds 'old' (its a very slow sensor)
        float h = dht.readHumidity();
        // Read temperature as Celsius (the default)
        float t = dht.readTemperature();
         
        if (isnan(h) ||isnan(t)) {
            Serial.println("ERROR: Failed to read from DHT sensor!");
        }
        else {
            publishData(t, h, aq);
        }
        delay(5000);
    }
}

Download the full source code at Github.

After connecting, flashing and running our tiny 15 USD wireless sensor we will continuously receive updates of actual temperature and humidity measurements. Those measurements are shown within your Home Assistant views. A very nice feature of Home Assistant is also that it stores historic measurements and that you can get a chart of past trends by a single click into the UI, as shown below:

Overall, Home Assistant is the perfect open source platform for your own home automation projects, no matter if you run it on your old laptop or on a tiny Raspberry Pi. It offers all the flexibility in terms of attaching any kind of MQTT sensor or message provider and is a great platform for playing around with your electronics hardware and it has a cool Web UI too!

Teach your Kids to code: Build your own OttoDIY robot

Coding is the lingua franca for all citizen in a modern technological society. By adapting any programming language your kids can learn very important skills, such as abstraction of a problem, defining and structuring a solution and to use a sequence of simple steps to fulfill complex tasks. Beside all the educational benefits of learning to use a programming language it is a lot of fun to see and experience your own programs while performing their autonomous tasks.

Another important skill within the actual technological society is to understand and control robotic hardware or electronics in general.

Nothing is more exiting for your kids as if something moves, makes a sound or blinks a lot of lights. Believe me when I say that kids are native robot and automation enthusiasts!

That said, I was really exited as I read about a vivid community of electronics and programming experts that shared the same idea of building the open educational robotics platform OttoDIY. OttoDIY offers all necessary resources, such as electronics, servos, sensors along with 3D printing models of the robot’s body parts to quickly jump into the world of electronics and robotic motion.

The OttoDIY community does share all information that is necessary to quickly print your own Otto robot and assemble the electronics.

Fortunately, the company I work for (kudos to Dynatrace) strongly supports innovation and coding for kids. Therefore, I had the chance to print our own Otto robot within the Dynatrace lab and I was astonished how easy it is to reproduce the body parts offered on thingiverse. See some impressions of the printing process below:

OttoDIY print UltimakerOttoDIY print Ultimaker

Otto’s brain arrived some weeks later and we immediately started to assemble the complete OttoDIY robot. With the assembly instructions given by Camilo Parra Palacio it was pretty easy to set the complete bot up and get it running within an hour.

One important hint here is to first check if the shipped servos do exactly fit into the dedicated sockets within your 3D print. Otherwise, you have to disassemble the complete bot again and rasp some more space.

After we assembled the complete OttoDIY bot, we downloaded the mBlock coding environment that was specifically built for kids and children. mBlock is a combination of Scratch and Arduino that allows kids to play around with physical computing and program first hardware and bots by simply using a structured visual block programming language, as it is shown below:

After some practice we finally were able to teach our Otto robot some quite cool dance moves, see below:

 

Kaggle: Join the global machine learning and AI community

Around a halve year back I stumbled over Kaggle.com, a vital community portal of Artificial Intelligence and machine learning experts. Kaggle not only encourages people around the world to share thoughts and example data sets on popular machine learning tasks, they also host great AI challenges.

Since I joined the Kaggle community 6 month ago, I was fascinated about the individual challenges that were published. Those challenges range from predicting Mercari product prices over detecting icebergs from radar data to speech recognition tasks.

Many companies such as Google, Mercari or Zillow are hosting challenges where more than thousand of teams try to predict the best results. Often it is unbelievable how those teams solve these complex machine learning tasks.

Besides providing the challenges and the data sets necessary to wake the interest of global leaders within the machine learning and AI community, Kaggle also offers a tremendously powerful kernel execution environment. This execution environment consists of preconfigured Docker containers that were specifically designed for training models. In order to design and execute a machine learning kernel you simply edit the code online (Python, R, Notebook) and execute it within the Kaggle infrastructure.

As Kaggle docker containers are completely preconfigured you save a lot of time to download and prepare your environment.     

 

 

Kaggle really pushes the AI community forward in terms of offering a flexible and open platform for executing kernels and to quickly get hands on interesting data sets. The community platform also does a pretty good job in bringing the global community together and stimulates a broader and practical discussion outside the theoretical scientific community.

Besides if you need a quick start tutorial on how to train your first neural network, grab my eBook at Amazon:

Android Paint and Draw for your Kids

Over the last years I kept asking my now 5 year old daughter how she would design a simple painting App and how this Painting App should look like. We discussed about the background color as well as how to change and choose colors, the absolute requirement to add the color pink and how to change the brush thickness. The design of the paint and drawing App went completely without the need to read a single piece of written text or menu. We came up with a very simple and intuitive way to touch-draw images for children and to store these images as .png pictures. You can find your free Android Painting App for Kids and Children in the Google App store.

Software Structure Analysis and Metric Calculation with Neo4J and Cypher

During the last weeks the Software Analytics and Evolution research team at the Software Competence Center Hagenberg (which is the group i am actually working in) built a software tool for parsing large scale legacy software systems, such as C, C++ but also FORTRAN, Structured Text (IEC 61131 Machinery and Robot programs) or Matlab source code with the goal to analyse its structure by using a Neo4J graph database and Cypher queries. As you can see within this demo video, the tool is able to visualize important aspects and metrics as well as the software architecture and structure of the analyzed software system. The tool is meant for supporting companies to develop and maintain their large software systems and code bases.

Lean Tablet Cash Register for Entrepreneurs and Small Businesses

TabShopLogoProMany startups, entrepreneurs and small businesses are spending a large amount of their spare budget for operating a cash register system. Most of these systems are built upon old stationary touchscreen hardware that is on the one hand quite expensive and on the other hand quite unflexible. These old point of sale systems do not represent the lean and flexible spirit of todays entrepreneurs and startups. Nowadays small businesses are moving fast, offer high mobility and react flexible on new opportunity.
By offering a complete cash register and stock management system in your pocket TabShop climbed the top position in Google Play Marketplace. TabShop is the leading point of sale system app on Android tablets and smartphones. It allows users to manage their stock and directly checkout the customers invoices. With TabShop entrepreneurs always take their stock information and cashier system with them. So small businesses are always ready to take up great selling opportunities.

Screenshot_2014-04-16-08-13-18

MobileVNC controls Industry Panel with HTML5 VNC Viewer

By adding support for Websockets MobileVNC is now fully compatible with HTML5 based VNC Viewers such as noVNC! See in the following video how MobileVNC remote controls an Windows CE Industry Panel by just using a Chrome Webbrowser. The Browser is able to directly connect to the Windows CE embedded industrial Panel by typing in its IP address.

TabShop Submission @ SAMSUNG App Challenge 2013

samsung_app_challenge

In line with the publication of the 51th production version of TabShop Retail Point of Sale App last week, TabShop was also submitted to the SAMSUNG 2013 App Challenge. Between September 5 and 30 November 2013 SAMSUNG opened an App challenge specifically for Galaxy Note HD Apps.

TabShop version 51 comes with some major improvements and a new possibility to add stock updates directly within the inventory screen. The sorting of items was improved and some extra reports were added within the daily Point of Sale business report.

TabShop free Android Restaurant POS App

TabShop Android Retail Point of Sale adds Easy Accounting

TabShop Android Retail Point of Sale App

TabShop the free Android Point of Sale App has just been released in version 35 which adds new features such as Share reports and exports with Google Drive. One of the major new features is an easy Accounting option for keeping a good overview about profits, taxes and costs prices. Additionally a detailed overview about the daily result is shown as well as TabShop offers a revised daily PDF report generation with additional business information. The generation of Profit and Sales charts has been improved, especially the low performance of chart generation was improved significantly. With the new payed feature for setting an Administration password the owner of a shop is now able to secure sensible areas of TabShop POS against unintended changes by employees.

With its over 13.000 downloads, TabShop Android Retail Point of Sale App climbed the top ranking within the Google Play App marketplace within several month and will be improved constantely.

Thanks a lot for all the Bar/Restaurants, Retail Shops, individual Stores or even the owners of Bands who gave a lot of supporting feedback within the last months!

Invoice List within TabShop Android Point of Sale POS AppAccounting and Daily Reports within TabShop Android Point of Sale POS App