Mo-Fi: Discovering Human Presence Activity with Smartphones Using Non-intrusive Wi-Fi Sniffers

With the explosive growth and wide spread of smartphones with Wi-Fi enabled, people are used to accessing the internet through Wi-Fi network interfaces of smartphones. In the meanwhile, smartphones periodically transmit Wi-Fi messages, even when not to connect to a network. In this paper, we describes the Mo-Fi system which monitors and aggregates Wi-Fi message transmissions in the area of interest using non-intrusive Wi-Fi sniffers. In this paper, we proposes an optimized Wi-Fi channel detection and selection method to switch the best channels automatically to aggregate the Wi-Fi messages based on channel data transmission weights, and human presence activity classification method based on the features of human dwell duration sequence in order to evaluate the user engagement index. By deploying in the real-world office environment, we found that the performance of Wi-Fi messages aggregation of CAOCA and CACFA algorithms is over 3.8 times than the worst channel of FCA algorithms and about 76% than the best channel of FCA algorithms, and the human presence detection rate reaches 87.4%.