Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1132983.1132993 | REALMAN@MobiHoc |
Keywords | Field | DocType |
daily list,location prediction,profiling mobility,eth zurich campus,wlan mobility trace analysis,mobility profiles,certain probability,sociological orbits,mobile wireless networks,hub-level location prediction,wireless user,mobility profile,wireless network user,network user,mobility trace data,algorithms,data collection,human factors,design,wireless network | Wireless network,Wireless,Profiling (computer programming),Computer science,Computer network,Mobility model,Location prediction | Conference |
ISBN | Citations | PageRank |
1-59593-360-3 | 32 | 1.92 |
References | Authors | |
13 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joy Ghosh | 1 | 141 | 9.83 |
| Matthew J. Beal | 2 | 600 | 64.31 |
| Hung Q. Ngo | 3 | 670 | 56.62 |
| Chunming Qiao | 4 | 3971 | 400.49 |