  This paper proposes a novel approach for detecting groups of people that walk
"together" (group mobility) as well as the people who walk "alone" (individual
movements) using wireless signals. We exploit multiple wireless sniffers to
pervasively collect human mobility data from people with mobile devices and
identify similarities and the group mobility based on the wireless
fingerprints. We propose a method which initially converts the wireless packets
collected by the sniffers into people's wireless fingerprints. The method then
determines group mobility by finding the statuses of people at certain times
(dynamic/static) and the space correlation of dynamic people. To evaluate the
feasibility of our approach, we conduct real world experiments by collecting
data from 10 participants carrying Bluetooth Low Energy (BLE) beacons in an
office environment for a two-week period. The proposed approach captures space
correlation with 95% and group mobility with 79% accuracies on average. With
the proposed approach we successfully 1) detect the groups and individual
movements and 2) generate social networks based on the group mobility
characteristics.
