Know Your Air (KYA) is a cloud based air quality platform we created during the recent AT&T developer summit at Las Vegas. Air pollution (especially in urban areas) is increasing day by day, and knowing the air quality help us in taking preventive actions as well as travel planning.
KYA has three sub systems
- Hardware – Input sensors and Output display unit
- Data storage and analytics
- Prediction service
The following photo is taken during the hackathon demo.
Let me give a quick overview of each sub systems.
- Hardware – Input sensors and Output display unit: – We used three hardware boards, and each one represents the real life use case when we deploy similar solution in production. Most of the public transit stations would have air sensors connected to KYA cloud end point, and in our demo we simulated a bus stop with Qualcomm Dragon board and Grove air sensors. Android is the default operating system for Dragon board, and we could easily wire up the sensors. We also connected a 13 inch display unit also to the Dragon board so that it show the bus number, bus arrival time, local air quality details and AQI (Air Quality Index) of the approaching bus. Yes, you are reading it right. I meant the air quality inside the approaching bus. If you are concerned about the CO2 inside the bus, check it before boarding the bus We had connected Intel Edison board with CO2 and Air quality sensor, inside the simulated bus. Here we have used the Intel XDK, and Node modules to push the air data to the KYA cloud. The third input was from a Drone. City officials might need to check the air quality on demand. If any construction site or any other events violates the city norms with respect to air pollutions, City authorities can use this method. We had connected Konekt board with air quality sensor on a drone. All the above-mentioned input methods feed air data along with GPS.
- Data storage and Analytics :- We used the AT&T M2X cloud for sensor data storage. M2X got pretty much stable client libraries, and integration with Node and Android was straightforward. M2X is great platform to manage the time series data. M2X also provides the built-in chart with historical data for any sensor, and we could use that with our dashboard. Air Quality Index (AQI) and the various gas (CO2, Methane etc) intensity were calculated for each geo coordinates, and REST end points will provide the nearby AQI for any geo coordinates.The Air Quality Index provides a number from 1 to 10+ to indicate the level of health risk associated with local air quality. On occasion, when the amount of air pollution is abnormally high, the number may exceed 10.We had also setup the air quality heat map on top of Google map, so that the consumers can visualize the air pollution for any location.
- Prediction service:- We used the IBM Bluemix predication service to predict the air quality for 6-12 months for given geo coordinate. Input data for the prediction service was coming from the IBM IoT hub, and we were feeding it from AT&T M2X.
Currently, Most of us plan our travel by checking the weather forecast, and in near future we might check the air quality of the destination city as well, and carry air bottles and masks.