Although we could have installed ready-made sensors and climate systems, the market solutions that we evaluated were either too costly or did not provide all the features we needed, such as integrating all environmental data into a single visual system. Since our project team already had experience with embedded solutions and wanted to experiment further with IoT and AR (augmented reality) technologies, we decided to build a microclimate system from scratch. In addition to testing the efficiency of various sensors, devices, and protocols across a range of tasks, the exercise would enable us to develop a functional, cost-effective, scalable system that could:
- Collect information on temperature and air humidity
- Visualize this data in a user-friendly dashboard
- Analyze the data to create actionable statistics
We started the PoC process by interviewing members of the office maintenance team about their desires and requirements for a microclimate system. After processing their feedback, we began experimenting with various pieces of equipment. Our biggest challenge was selecting functional, reliable, high-performance hardware that was also inexpensive and could be easily mass produced. We ultimately selected DHT11 temperature and humidity sensors that use MQTT protocol to transmit information to special hubs through the below workflow:
- Hub gathers data from sensors and transfer to server via Wi-Fi
- Server processes, aggregates, and visualizes data
- Server transmits data to user’s computer via web interface
We also ran a cost analysis to understand the overall “purchase price” of the microclimate system in case of mass production. The cost of one sensor is $7-$8, and the cost of a hub is $9-$10. To cover an office area of about 5600 m2 (at an average of 1000 m2 per floor), an organization would need to purchase about 100 sensors and 4-5 hubs. Averaged out, the MeteoLogic system would cost an organization around $800.
We decided to apply AR functionality to MeteoLogic’s wall sensors in order to display temperature and humidity information. A user simply has to aim his/her smartphone camera at a sensor to see data about the room’s current environment. The user can also pull up information about how to maintain the MeteoLogic system, such as dismantling a faulty device, replacing a battery, reassembling a device, etc. This interactive component of the system ensures that absolutely anyone can use and maintain MeteoLogic.
We were able to implement AR in just a few months using technologies like Unity and Vuforia. We also developed a MeteoLogic mobile app (Android, iOS), which receives data from the server based on a unique sensor tag and then displays the system's main measures to the user.
We implemented the MeteoLogic system into our Kharkiv office’s existing ecosystem and, after a successful 12-month trial, began exploring ways to optimize IoT standards and data transfer protocols. For example, we extended MeteoLogic’s software architecture so that the platform can scale more easily and become even more functional through additional sensor connections using standard algorithms with peripheral modules (e.g., SPI, UART, I2C). One of the first external devices we connected to the system was a MH-Z14 carbon dioxide sensor.
The new software also supports two-way communication so that, in addition to receiving information from sensors, MeteoLogic can also control the devices with the help of “actuators” (a machine component that is responsible for moving or controlling a mechanism or system, "mover"). One of our goals for the future is to be able to remotely control the office’s air conditioners through an infrared channel using an external IR transmitter.
Our ultimate goal with the MeteoLogic project is to create a smart office system that is both interactive and capable of providing feedback. Not only will it be able to monitor the environmental conditions of an office, but it can also automatically control parameters such as temperature and air quality. Future areas of exploration include:
- Optimizing the network infrastructure by implementing a mesh-type network (and therefore minimizing the number of required hubs to just one per floor)
- Migrating to Bluetooth Low Energy (BLE) wireless technology to implement the mesh network and to minimize sensor energy consumption
- Designing a new version of the printed circuit board (PCB) to support BLE and enable integration with modern smartphones and other standard devices
We would also like to supplement the project with new technologies. Already our Big Data experts are collaborating with the MeteoLogic team to improve the platform’s real data processing, analysis, and visualization capabilities. In addition to deepening our expertise in IoT, data processing, and augmented reality, the MeteoLogic project demonstrates how Machine Learning is becoming a major force across all environments and industries.
Interested in learning more about MeteoLogic or BrainMade? Email firstname.lastname@example.org