Group 4 Conclusion

What were your results?

  • All the trials-including the RF meter trial-demonstrate the highest amount of readings 1 meter away from the wireless access point (WAP), and as expected, all three trials show a negative association between distance and dBm. As previously mentioned, these are the circumstances for each of the trials:

  • Trial 1: WAP uncovered.
  • Trial 2: WAP covered with cinder block.
  • Trial 3: WAP covered with cinder block and surrounded by 12 wood planks.
  • Trial 4: RF electrosmog meter used, WAP uncovered.

Descending Values

  • We expected the signal strength to descend from Trial 1 to Trial 3, but instead, Trials 2 and 3 alternated in dominant strength between distances. There was an even split in averages, with 5 distance points of Trial 2 dBm readings being greater than Trial 3 dBm, and with 5 distance points of Trial 3 dBm readings being greater than Trial 2 dBm readings. Because of this even split, we are hesitant in reporting that increasing the volume of medium to an already-covered WAP decreases the signal strength.

  • A peculiar observation to note is that from 2 meters, trials 2 and 3 gave dBm readings that were significantly lower than the data of trial 1. Trial 1 at 2m yielded an average of -41.6dBm, while Trial 2 yielded -49.4dBm and Trial 3 yielded -50.6 dBm. The 2m was the distance that showed the largest gap in signal strength between Trial 1 and 2-3.

A Change in Procedure

  • Our trend prediction that the wireless signal (dBm) would decrease with the addition of obstacles proved to be true, although we changed the methodology we used to record our data. We deemed our RF meter to be an unreliable source of data, and decided not to explore the decrease in power density (measured in mW/m²) within a changing medium. The reason for this is because the RF proved difficult in recording reliable data, with the average value feature constantly reporting an average of 0 mW/m² , and with instantaneous values spiking and dropping too frequently for close observation, and in a high range that further complicated our attempts to use it as a reliable data collection instrument.

What do your results mean?

  • Our results showed that, for the most part, the placing of obstacles around the WAP decreased the power of the signal. Trial 2 suggests that concrete blocks effectively interfere with a Wi-Fi signal, while Trial 3 seems to suggest that wood has little effect on Wi-Fi signal.

  • The RF meter readings for mW/m² are too volatile to be interpreted reliably.

Were your results as predicted?  Why? or Why not?

  • Overall, the data we took support our hypothesis that materials can interfere with a Wi-Fi signal. At first, there is a significant difference in dBm between Trial 1 and the other two trials. Trials 2 and 3 both utilized concrete blocks to interfere with the WAP’s signal. Furthermore, as the distance increased between the router and the cellphone increased, the difference in dBm between Trial 1 and Trials 2 and 3 seem to have decreased.

What science did you learn during this project?

  • Radio signals passing through different media like concrete and wood have a diminutive effect on the signal strength of a wireless access point.

  • We were able to measure the average dBm ratings at specific distances that are characteristic of the specific router model we used for our project (a Securifi Almond touchscreen router).

  • Though our RF electrosmog meter was not reliable in collecting power density, it did allow us to observe that the levels of RF interference on campus are very high. This is likely due to the campus’ Wi-Fi network coverage, and because of the high popularity of cell phones and other personal devices that emit RF waves.

  • The decrease in average signal strength from 1m to 10m does not decrease with a constant slope; instead, fluctuating up and down in a generally decreasing trend.

What would you differently if you had to do this project again?

  • If this project could be performed in ideal conditions, we would use an area completely cut-off from outside Wi-Fi signals (ie: a concrete bunker, an open field).

  • We would be interested in taking further data with longer distances and with larger volumes of media. dBm at 50m would be a good value to predict for us to understand the distance limitations of a WAP, and increasing the volume to multiple meters of concrete or wood would most likely show the greatest data comparisons on our graphs.

What would you do next if you had to continue this project for another 6 weeks?

  • Researching the effects of RF signals on each other would most likely benefit the interpretation of our data. We suspect that RF interference from other devices may have resulted in the fluctuations in our data measurements in both mW/m².

  • In order to understand RF interference, we would also require testing to be in an RF-isolated environment (ie: underground bunker).

  • Recording data with a wider range of materials such as rubber and various types of metals would also allow us to explore how different materials affect Wi-Fi strength. We suspect that different materials can have a greater or lesser diminutive effect on signal strength.

  • We would find a location, off-campus, with minimum Wi-Fi interference in order to see if RF had significantly affected the results of the trials. This could explain why the RF meter was not a useful tool for measuring a Wi-Fi signal.

  • We feel that our data collection could have been improved if we had an automated solution for collecting data. A hypothetical application that would help us improve our data collection would also allow us to explore the change in signal in less than 1m intervals. This application would automatically and regularly take dBm readings while simultaneously logging its distance from the WAP.  This would allow us to have greater points of data between 1-10m and would give us a much more accurate graph. Also, this application could allow us to understand Wi-Fi signal strength with respect to another axes of position, allowing us to graph signal strength in a three-variable graph (preferably a heat map).

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