Evaluating location predictors with extensive Wi-Fi mobility data
[song:predict-tr]Libo Song, David Kotz, Ravi Jain, and Xiaoning He. Evaluating location predictors with extensive Wi-Fi mobility data. Technical Report number TR2004-491, Dartmouth Computer Science, February 2004. ©Copyright the authors. Revision of song:predict. Later revised as song:jpredict.
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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