Given the proliferation of Wi-Fi signals, a team of researchers at the University of California at Santa Barbara (UCSB) decided to see if a Wi-Fi link could be used to count the number of people walking in an area--without the people carrying any devices.
Students in UCSB Professor Yasamin Mostofi's lab demonstrated that a Wi-Fi signal can be used to count the number of people in a given space. "Our approach can estimate the number of people walking in an area, based on only the received power measurements of a Wi-Fi link," said Mostofi, a professor of electrical and computer engineering, in a release. Their approach does not require people to carry Wi-Fi-enabled telecom devices for them to be counted, according to Mostofi.
The researchers say people mainly impact the Wi-Fi link in two ways. First, as a person crosses the line of sight (LOS), the received power drops considerably. Second, when the person is not crossing the LOS, he or she acts as a scatterer, reflecting the signal and contributing to multipath fading. While these phenomena occur in any wave propagation scenario, the main challenge in this situation was finding a way to properly relate these to the number of people, which is what the researchers proposed.
To show it could be done, they put two Wi-Fi cards at opposite ends of a target area, a roughly 70-square-meter space. Using only the received power measurements of the link between the two cards, their approach estimates the number of people walking in that area. They did both indoor and outdoor experiments.
An example of the setup (left) in an outdoor setting and (right) in an indoor setting. In each case, the received power measurements of a Wi-Fi card are used to estimate the total number of people. The TX is a D-link WBR1310 wireless router and the RX is an Atheros ar5006x Wi-Fi card. (Source: UCSB)
"Through a Markov chain modeling, we have characterized the probability that any number of people cross the LOS," the researchers said in their project information. "Furthermore, we have utilized the well-known K-distribution to characterize the scattering impact of one person. After some derivations, we finally derive an expression for the probability density function (PDF) of the received power as a function of the number of people present. During the experiment, the receiver WiFi card records its power measurements for a small period of time, from which we can get the experimental PDF. By using KL divergence as a metric, we can then estimate the number of people by comparing the experimental PDF to our derived theoretical one."
Several potential applications can benefit from the abiltiy to estimate the number of people in an area. For example, the heating and cooling of a building can be better optimized based on learning the concentration of the people over the building. Emergency evacuation procedures can benefit from an estimation of the level of occupancy. Stores can profit from counting the number of shoppers for better business planning.
Graduate students Saandeep Depatla and Arjun Muralidharan participated in the demonstration, as well as a number of students who did the walking.
The findings of Mostofi's research group are scheduled for publication in the Institute of Electrical and Electronics Engineers Journal on Selected Areas in Communications' special issue on location-awareness for radios and networks.
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