Abstract
Understanding user mobility and its effect on access points (APs) is important in designing location-aware systems and wireless networks. Although various studies of wireless networks have provided useful insights, it is hard to apply them to other situations. Here we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to reveal important periods and chose the two strongest periods to serve as parameters to a classification system based on Bayes’ theory. Analysis of 1-month traces shows that while a daily pattern is common among both users and APs, a weekly pattern is common only for APs. Analysis of 1-year traces revealed that both user mobility and AP popularity depend on the academic calendar. By plotting the classes of APs on our campus map, we discovered that their periodic behavior depends on their proximity to other APs.
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Notes
We discuss the reason that we used a trace of 4 weeks instead of 1 month in Sect. 3.4.
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Acknowledgments
This project was supported by Cisco Systems, NSF Infrastructure Award EIA-9802068, and Dartmouth’s Center for Mobile Computing. We are grateful for the assistance of the staff in Dartmouth’s Peter Kiewit Computing Services in collecting the data used for this study. We would like to thank Songkuk Kim for the insightful suggestions throughout the process of developing our method. We also thank Tristan Henderson for commenting on draft versions.
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Kim, M., Kotz, D. Periodic properties of user mobility and access-point popularity. Pers Ubiquit Comput 11, 465–479 (2007). https://doi.org/10.1007/s00779-006-0093-4
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DOI: https://doi.org/10.1007/s00779-006-0093-4