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The students created these examples using the lab hardware and installing the software for a complete custom end to end sensor network for real-time streaming.

Lab 2:

Step 5: Chart Creation from their streaming IoT device

The team downloaded a .csv that had a date range containing a walking trip one team member took to the post office while recording temperature and gps data. The following graph shows both temperature and speed graphed versus time of day. From this graph, it can be seen that for the first two minutes after the beaglebone was powered up, the board was stationary. This was when the team member was packing up the board into his backpack to take it to the post office. The next 6 minutes are during the walk, the temperature can be seen to be lower outside than inside the team member’s house. From 15:57 to 15:59, the velocity data drops out. This is because the team member entered the post office, and the GPS module lost its lock. The higher temperature in the post office can also be seen during this time. The next 5 minutes or so show the walk back to the team member’s house, and the last two minutes, where the velocity drops back to 0 is when the device was being unpacked and prepared for shutdown.

Step 6:  Google Fusion Tables Map Creation

The following map was created in google fusion tables using the data from the trip to the post office and back. The legend shows that the color of each dot depends on the temperature at that point, separated into 4 ranges, red being the hottest and blue-purple being the coldest. While the temperature does fluctuate quite a bit, it can definitely be seen that it was cooler outside than in either the team member’s house or the post office.  There is also an outlier data point north west of the post office where the team member never went. This was a glitch in the gps lock, and slightly erroneous data was reported.

Example 2 Step 6:

For part 6 of the lab, the team placed the Beagle Bone temperature measurement rig into a motor vehicle. The GPS was mounted on the roof of the car in order to get a clear signal as the car moved. The temperature probe was placed out the window in order to check for varying temperature as the car moved. The car was then driven for a few miles around Dayton. The raw results of this activity are shown in Figure 8. Plots of the temperature and speed as a function of time are shown in Figures 9 and 10, respectively. Finally, maps were created using Google Fusion of the temperature and speed data over varying positions, shown in Figures 11 and 12, respectively.

            Looking at the plots and maps, a general trend can be observed. As the speed of the vehicle increases, the temperature measured at the probe decreases. At the starting and ending points of the experiment, the temperature probe measured its max values, since the vehicle was at rest. The other speed variations can be attributed to the start and stop movements of the vehicle in traffic, resulting in fluctuations in the measured temperature.

Figure 9: Plot of the Temperature Variation Measured vs. Time when Placing the Temperature Sensor Outside of a Moving Vehicle

Figure 10: Plot of the Speed of the Vehicle vs. Time


Figure 11: Map of Temperature Data Collected While Moving

Figure 12: Map of Vehicle Speed While Moving



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