| Summary: | The study presents a preliminary design of an electronic system to detect carbon dioxide, CO2 and particular matter with diameter of 2.5μm (PM2.5) and data visualization with IoT implementation. This study focuses on the development on the interfacing MQ-135 gas sensor and GP2Y1010AU0F dust sensor with Intel Galileo processing platform to implement the data acquisition of CO2 and PM2.5 respectively. MQ-135 gas sensor direct interface with Intel Galileo, whereas GP2Y1010AU0F dust sensor direct interface with Arduino Pro Mini. All the data is sent to Intel Galileo via I2C communication. To collect the data, 1 sample per second is taken for MQ-135 gas sensor, whereas 30 samples per second is taken for GP2Y1010AU0F dust sensor. The displayed data for GP2Y1010AU0F dust sensor is average data for actual of 30 samples per second. A message for sending to Internet is generated on Intel Galileo. Next, it will be sent to MQTT broker under the service of IBM Bluemix. The data received by the MQTT broker is subscribed by the node-RED for the visualization of data. The IoT technology is implemented by node-RED. The graphical user interface (GUI) developed by using node-RED flow editor. The GUI is created on a webpage which is programmed by using html and JavaScript languages. Only the highlighted information is displayed and the guideline of air quality is presented on the GUI. The results of the sensor test presented that taking 30 samples per second for GP2Y1010AU0F dust sensor will obtain a more stable signal. The results also illustrated that the GP2Y1010AU0F dust sensor can detect the present of smoke, whereas MQ-135 gas sensor can detect the present of high concentration of CO2. Besides that, the functionality test of the proposed system is run on 6 different locations and prove that the system was functioning well. In conclusion, the sensors were successfully integrated with Intel Galileo processing platform and implementation of IoT technology. Simple GUI helps users to visualize and understand the displayed data.
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