TY - GEN
T1 - On profiling mobility and predicting locations of wireless users
AU - Ghosh, Joy
AU - Beal, Matthew J.
AU - Ngo, Hung Q.
AU - Qiao, Chunming
PY - 2006/5/26
Y1 - 2006/5/26
N2 - In this paper, we analyze a year long wireless network users' mobility trace data collected on ETH Zurich campus. Unlike earlier work in [4,18], we profile the movement pattern of wireless users and predict their locations. More specifically, we show that each network user regularly visits a list of places such as a building (also referred to as "hubs") with some probability. The daily list of hubs, along with their corresponding visit probabilities, are referred to as a mobility profile. We also show that over a period of time (e.g., a week), a user may repeatedly follow a mixture of mobility profiles with certain probabilities associated with each of the profiles. Our analysis of the mobility trace data not only validate the existence of our so-called sociological orbits [8], but also demonstrate the advantages of exploiting it in performing hub-level location predictions. In particular, we show that such profile based location predictions are more precise than common statistical approaches based on observed hub visitation frequencies alone.
AB - In this paper, we analyze a year long wireless network users' mobility trace data collected on ETH Zurich campus. Unlike earlier work in [4,18], we profile the movement pattern of wireless users and predict their locations. More specifically, we show that each network user regularly visits a list of places such as a building (also referred to as "hubs") with some probability. The daily list of hubs, along with their corresponding visit probabilities, are referred to as a mobility profile. We also show that over a period of time (e.g., a week), a user may repeatedly follow a mixture of mobility profiles with certain probabilities associated with each of the profiles. Our analysis of the mobility trace data not only validate the existence of our so-called sociological orbits [8], but also demonstrate the advantages of exploiting it in performing hub-level location predictions. In particular, we show that such profile based location predictions are more precise than common statistical approaches based on observed hub visitation frequencies alone.
KW - Location prediction
KW - Mobile wireless networks
KW - Mobility profiles
KW - Sociological orbits
KW - WLAN mobility trace analysis
UR - https://www.scopus.com/pages/publications/33745935173
U2 - 10.1145/1132983.1132993
DO - 10.1145/1132983.1132993
M3 - Conference contribution
AN - SCOPUS:33745935173
SN - 1595933603
SN - 9781595933607
T3 - REALMAN 2006 - Proceedings of Second International Workshop on Multi-hop Ad Hoc Networks: from Theory to Reality
SP - 55
EP - 62
BT - REALMAN 2006 - Proceedings of Second International Workshop on Multi-hop Ad Hoc Networks
PB - Association for Computing Machinery (ACM)
T2 - 2nd International Workshop on Multi-hop Ad Hoc Networks: from Theory to Reality, REALMAN 2006 co-located with ACM MobiHoc 2006
Y2 - 26 May 2006 through 26 May 2006
ER -