What is a Bluetooth Low Energy (BLE) Dice?
Bluetooth Low Energy (BLE) Dice is a system consisting of hardware and software applications. Our Innovation Lab specialists developed it in C/C++ on the embedded side and Python on the server side for full black box test automation. The Python test automation app uses BlueZ and Bluetoothcfl technologies for Bluetooth Low Energy connection and can work as a centre for collecting and exchanging data from/between different BLE devices and performing continuous tests according to predefined test cases. Along with the test framework, our developers also created a BLE peripheral hardware and software device which has been equipped with inertial measurement unit sensors, programmed to collect data on position, velocity and G-forces.
For testing reasons and to show what this application can do, our developers have placed the created embedded system inside a dice. Once you throw it, the information about the number of meshes on the cube can be sent to any connected device. The application pairs and connects with it according to BLE standards, providing a secured connection – the device can then receive data in the dice from the sensors and based on that, calculate which side of the dice faces up.
Of course, using the application and the BLE dice for online games is just one of the use cases. We’ve already successfully employed both of these solutions for a wellness smartwatch that we helped develop last year.
They will be a true gamechanger for one of the sectors Spyrosoft is already heavily involved in: Industry 4.0. Our experts already support multiple projects for some of the largest companies in Germany and the US.
How can the BLE Dice be used for Industry 4.0 projects?
The BLE Dice peripheral enables next level Machine-to-Machine communication. It could be used as a black box, tracking every move and every action that a machine takes and collecting data for how precise it is. This data could be then used to create an AI matrix to recalibrate the rest of the machines and send this model to all of them. They could then learn from each other, instantly and without fail.
This would allow for the resolution of two of the biggest challenges of the modern Industry 4.0 companies: excessive throughput and waste management. Small discrepancies in discrete manufacturing – i.e. occurring every 100 produced items – may not be a problem when you take one machine into consideration, but they can become an issue if you are looking at the overall amount of waste at a plant. Money spent on material and machine maintenance services quite literally turns into trash.
Watch this video to learn more about how this would happen:
The black boxes with the peripheral inside of them would be relatively inexpensive to produce and mount onto the machines, allowing your business to limit the amount of waste. The boxes could be also protected from any environmental factors such as heat, humidity, dust and radiation using shields, so their usage does not have to be limited to inside the plants.
Additionally, the data collected from the machines could be migrated to cloud and used in a type of device or a production performance/management system, including Machine Learning analysis tools.
This would also enable manufacturers to tackle another stifling issue in the sector: lack of innovation due to lagging when it comes to digitalisation maturity. By employing our solution, your company will be able to run micro experiments by calibrating machines to perform certain tasks slightly differently and then checking if the results will be better – all without breaking the existing workflows.
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