Recent studies on mobility modeling have focused on characterizing user mobility from real traces of wireless LANs (WLANs) and creating mobility models based on such characterization. However, most of the work does not study how user mobility is correlated in time at different time scales. For example, the future APs with which a user will be associated are predicted without the knowledge of when the association will take place and for how long. In this paper, we build a mathematical model for characterizing both steadystate and transient behaviors of user mobility in WLANs. Specifically, we model user mobility by a semi-Markov process, and obtain the transition probability matrix and the sojourn time distribution from the association history of WLAN users available at Dartmouth college [21]. With the steady-state characterization of user mobility in WLANs, we can estimate the long-term wireless network usage among different access points. By comparing the steady-state distributions o...
Jong-Kwon Lee, Jennifer C. Hou