Detecting high indoor crowd density with Wi-Fi localization: a statistical mechanics approach

We address the problem of detecting highly raised crowd density in situations such as indoor dance events. We propose a new method for estimating crowd density by anonymous, non-participatory, indoor Wi-Fi localization of smart phones. Using a probabilistic model inspired by statistical mechanics, and relying only on big data analytics, we tackle three challenges: (1) the ambiguity of Wi-Fi based indoor positioning, which appears regardless of whether the latter is performed with machine learning or with optimization, (2) the MAC address randomization when a device is not connected, and (3) the volatility of packet interarrival times. The main result is that our estimation becomes more-rather than less-accurate when the crowd size increases. This property is crucial for detecting dangerous crowd density.