The Internet of Things

The Internet of Things, or IoT, is transforming industries and user experiences across the entire global economy by bringing a fundamental shift in how value is created for the end-consumer, from reduced costs of production to improved efficiencies in areas like service and R&D. Software, sensors, and connectivity are increasingly embedded into the designed products and data streams are captured and analysed in real-time. This way products can be enhanced through remote operation and after-market service applications, accelerating smart product innovation and delivering new value through integrated services throughout the product’s lifecycle.



Technologies we use for this type of projects include: Arduino, Raspberry PI, ATMEL Studio, C++, Bluetooth, WiFi, Fog Computing, Microsoft Azure, R ( data analysis language), C# .NET, SD, I2C.







Home automation Motion capture & Analysis R&D CanGINE2 Tachograph

This project aimed to optimize the thermal comfort within a home equipped with electric heaters. All heaters had thermostat, but the type that only took into account the indoors temperature, not the difference between indoor and outdoor. The goal was to adjust the heaters’ output so that optimal thermal comfort could be achieved for each room, taking into account not only outdoor temperature, but also the desired temperature level depending on the room’s function, time of day, moment, timespan and temperature conditions while the windows are open and many other factors.

The system was designed to enable remote control, programing in advance for holiday modes and estimations of future electricity consumption based on previous usage. It’s also meant as a proof-of-concept for further applications that will allow users to remotely control carbon monoxide sensors, electronic blinds, anti-flooding sensors and anti-burglary systems, by using an application on their smartphone.

Building this system involved the purchase of weather stations equipped with TFA sensors that register temperature and humidity levels every minute, and placing one in each room in the house. An RF module then uses a 433.92 MHz radio protocol for wireless communication with a central unit, which consists of a Raspberry Pi receiver and emitter enclosed in a router housing.

The weather station’s sensor relays the temperature to the Raspberry Pi receiver. The central unit then determines if it’s necessary to turn off the electric heater or adjust its output on a 1 to 7 scale, in order to achieve a pre-set indoor temperature. The heaters are plugged into remote control sockets, which are controlled by the Raspberry Pi emitter in the central unit.

One of the challenges was caused by interferences with the radio protocol, which required the use of e pre-filter to eliminate most of the noise on the frequency. However, there was still enough interference that passed the firmware filter so that the heaters wouldn’t always start on the first command from the Raspberry emitter. This was solved by programming a second command, to follow 5 seconds after the first one, to ensure that the heaters would pick it up.

Pilight version 5 was the open source software installed on the Raspberry Pi device, to enable communication with its emitter and receiver over the 433 MHz protocol. Pilight was used as a plugin for Pimatic, a home automation server and framework for the Raspberry Pi running on node.js.

This software solution enables the user to define specific rules that regulate how the electric heater in each room operates, based on indoor temperature preferences, outdoor-indoor temperature differences, preferred modes for morning / evening / holidays etc. It also detects the user’s presence in the house, by detecting their smartphone, and additional settings can be programmed depending on whether the user’s at home or not.

Another sensor placed on the window in each room detects whether or not the window is closed and this enables the system to switch to a ventilation mode and turn off the heaters until the window is closed.

Based on the information collected by the outdoor temperature sensors, the user can obtain a graphic representation of the indoor – outdoor temperature correlation and estimate future energy costs by day / week / month / season.

The automation system is entirely controlled by a wireless dongle. To achieve this, while avoiding time-consuming reverse engineering on the electric sockets, an SDR antenna receptor was used. It captures, saves and clones the signal from each separate button on the remote control for the sockets and relays it to the radio dongle.

By using pre-existing solutions, building the entire automation system required a budget of less than 200 euros and achieved great performances in terms of comfort and energy costs: the temperature variations were brought down to +/- 0.3 Celsius degrees, while year round energy expenses were cut by 30%.

For the first three months of this project, we’ve had a dedicated team of two specialists working to bring the concept behind this project to life:

  • during training sessions, athletes will be able to wear a set of sensors that collect data about their movements;
  • based on this data, a baseline performance is established via Machine Learning algorithms, then the data from subsequent performances is analysed and compared against the current baseline;
  • following this analysis, recommendations for improvement are made and the athlete gets a visual representation of how the optimal movement should look like.

All movement data is collected and tracked using Microsoft Azure for machine learning, and a learning algorithm compares it to the athlete’s previous best.

The parameters for determining the optimal performance are set depending on the particular sport – this solution can be applied to a wide range of activities, from baseball and tennis to track and field athletics.

One of our experts is responsible for the hardware part of this solution, while the other manages the data analysis. The technology behind this project relies on an Arduino microcontroller and 3 MEMS sensors embedded on a FreeIMU board: an accelerometer, a compass and gyroscope.

The data is saved on an SD card and transferred to the cloud via Bluetooth. The athlete can access the information on his smartphone or PC, through an intuitive user interface.

We opted for an Arduino microcontroller because, compared to other physical computing tools, it offers a number of significant advantages:

  • it has a much lower cost
  • it requires less power, which translates in an increase in mobility and the device’s autonomy
  • it’s highly scalable, as both its software and hardware components are open source and extensible

However, this choice also presented a few performance challenges that our experts had to tackle: they replaced the Arduino libraries with custom Atmel studio 6 applications written in C++, thus optimizing the reading speed from 25 readings per second to 700 readings per second. Other improvements included:

  • making the libraries four times faster
  • optimizing the writing process on the SD card
  • simplifying the use of wearables cables (so the athlete doesn’t end up looking like a cyborg!)
  • enabling gesture recognition with optimal accuracy (eg.: forehand / backhand)
  • improving the sensors’ accuracy, reaching sensibility levels of +-2g , +-4g, +-8g, +-16g for the accelerometer and 200-2000rad/s for the gyroscope

The project is still ongoing and we’re very excited to pioneer the hardware programming industry in Romania.

The CanGINE2 is an application used in Fleet Management Systems for distribution companies.

The app enables companies to download tachograph data remotely via 3G or Wi- Fi, directly from the drivers equipped with an Android 2.3+ smartphone.

It uses a Bluetooth connection to remotely authenticate the tachograph device on the vehicle and a company card safely placed in a smart-card reader on a Windows XP+ server. The entire download sequence is managed by the application, so by simply pressing a button, the tachograph data will be saved on the Android device's SD card in .DDD format. The data can then be retrieved from the phone remotely, via a Wi-Fi or a 3G connection from the company office.

It supports all tachographs that use CAN protocol via the CANGine2 device and a LM048 serial to the Bluetooth device.


  • CAN protocol
  • Bluetooth SPP
  • TCP connection
  • REST Web Service
  • GSM
  • Push Notification
  • Presentation layer: XML
  • Android 2.2 +


  • Data download and display: the Android device is paired to the LM048 Bluetooth device, which in turn is connected to the CanGINE2 module and the VDO tachograph. It facilitates downloading the data from the tachograph on to the Android device and displaying it on the touchscreen.
  • Download data categories: Events and Faults, Overview, Technical Data, Detailed Speed, Driver Data.
  • Push notifications: the system uses push notifications to inform the driver of the about the download initiation.

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