In this experiment, we are using a Particle photon that documents BSSID and RSSI as well as sensor that measures temperature & humidity. We are interested in finding the relationship, if any, between the strength of surrounding Wifi signals (RSSI) and the surrounding temperature & humidity. Knowing this data might be useful to more easily locate areas (labeled by their BSSID) with stronger wifi signals.
RSSI signal strength is measured from 0 to -100 dBm, where values closer to -100 represent extremely poor strength, and values closer to 0 reflect stronger signal strength.
Unlike other sensors, this photon and sensor were not deployed in a permanent location; our photon and sensor were made mobile in order to flexibly collect data from anywhere and at any time.
Our mote, the case, was made small enough to conveniently carry it around, yet large enough to also store the battery into which the photon needed to be powered by at all times. The mote was made of wood, and a clear acrylic top that was made to be screwed easily for quick access to the photon and battery. Holes were made for the battery USB wire to connect to the photon, and slits were lasered onto the back wooden piece of the mote so that we could insert Velcro strips to securely attach it to a backpack strap, once again, because the entire mote was mobile.
Because we did a temperature & humidity demo in class, we used the same code for this project, but added new variables to suit our project, such as BSSID and RSSI. Furthermore, we started with the RSSI which represents the wifi strength. We soon realized it would be more accurate to compare the strength to its location in the building. This led us to find code for the BSSID.
Challenges we encountered were problems with the code. We had to rewrite our code several times in order for the sensor to pick up data onto our spreadsheet. This was a challenge because right when we would fix one problem, another problem would occur. This was a good learning experience because we were able to learn from our mistakes and we had to figure out how to problem solve.
Together, we learned how to work to combine thoughts to help further expand our ideas, to explain some theories and reasonings as to why we obtained the results that we did, and how to make our data more understandable. As a group of all girls, it was a unique experience because we all had great ideas and combining them only made them stronger, and it inspired us to prove that girls are entirely capable of creating productive and challenging experiments in computer science.
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