Evaluating Next-Cell Predictors with Extensive Wi-Fi Mobility Data
Libo Song
Kotz, D.
Ravi Jain
Xiaoning He
Dept. of Comput. Sci., Dartmouth Coll., Hanover, NH;
This paper appears in: Mobile Computing, IEEE Transactions on
Publication Date: Dec. 2006
Volume: 5,
Issue: 12
On page(s): 1633-1649
ISSN: 1536-1233
INSPEC Accession Number: 9239194
Digital Object Identifier: 10.1109/TMC.2006.185
Current Version Published: 2006-10-30
Abstract
Location is an important feature for many applications, and wireless networks may serve their clients better by anticipating client mobility. As a result, many location predictors have been proposed in the literature, though few have been evaluated with empirical evidence. This paper reports on the results of the first extensive empirical evaluation of location predictors using a two-year trace of the mobility patterns of more than 6,000 users on Dartmouth's campus-wide Wi-Fi wireless network. The surprising results provide critical evidence for anyone designing or using mobility predictors. We implemented and compared the prediction accuracy of several location predictors drawn from four major families of domain-independent predictors, namely, Markov-based, compression-based, PPM, and SPM predictors. We found that low-order Markov predictors performed as well or better than the more complex and more space-consuming compression-based predictors
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