Our MEGR 3171 IOT Project was motivated by the spring weather and the arrival of summer in the coming months. Living on Lake Norman, the temperature and UV index is important to know when going on the water, and when you are trying to get a golden tan. We used a VEML 6070 UV index sensor and a DHT11 temperature sensor. Using any weather app or local news, you can get the temperature and UV index in the general area, but with our project, more accurate readings can be posted based on your precise location rather than a general area. This makes it easy to check the thingspeak graph on your laptop or phone before heading out on the water. These sensors use IOT technology to bring the solution alive. The wifi connection of the Argon devices is what allows the user to find the Temperature and UV index in their exact location. The sensors will work wherever there is a wifi connection. This is useful in solving the problem of bland, average weather reporting for a large region.
The DHT11 sensor is extremely useful and can output a variety of temperature readings. Our reading was posted in degrees Fahrenheit. The VEML 6070 sensor had to be calibrated. The output of the sensor is the UV light intensity and that has to be divided by a factor to get the UV index. Using a few websites and weather channels, a factor of 210 was settled on to get the UV index from the reading. It is important to note that the UV sensor must be soldered to the included pins for the sensor to work.
To allow the data to be sent to the cloud and graphed, a thingspeak channel and integration on particle must be created.
The argons use bi-directional communication to send their sensor value to the other argon, and that argon will post the value to thingspeak. This was done to test the communication between the argons. The project can easily be simplified by using one argon. Both sensors can be wired to the argon, and slight editing to the code will have to be done, and both values can be sent by one argon instead of the current setup of two argons. A flowchart of the communication can be seen in the figure below.
The temperature and UV sensor were set outside for their readings. The temperature was recorded every minute and sent to thingspeak. It was a sunny day, but some clouds were moving in. The incoming cloud cover tells us why the temperature was dropping slightly. During the recording time, various weather sources gave us a temperature in the mid to upper 60s, and a UV index of 4. Both of these were similar to our sensor outputs, but ours may have been more accurate for our exact location. The following graphs show the data. The first graph shows the temperature in Fahrenheit with respect to time.
The UV index decreased as more cloud coverage approached while the sensors were outside. The UV index value was recorded every 30 seconds to see the impact of changing clouds on the UV value. This second graph shows the UV index value with respect to time.
The live data from thingspeak can be found here :
https://thingspeak.com/channels/1358252
Here is a youtube video demonstrating our project. The argons are set up, then put outside to gather the temperature and UV index readings.
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