Document Type
Article
Publication Date
12-2006
Publication Title
IEEE Transactions on Mobile Computing
Department
Department of Computer Science
Abstract
Location is an important feature for many applications, and wireless networks can better serve their clients 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 over 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. \par 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.
DOI
10.1109/TMC.2006.185
Original Citation
Libo Song, David Kotz, Ravi Jain, and Xiaoning He. Evaluating next cell predictors with extensive Wi-Fi mobility data. In IEEE Transactions on Mobile Computing, December 2006. 10.1109/TMC.2006.185
Dartmouth Digital Commons Citation
Song, Libo; Kotz, David; Jain, Ravi; and He, Xiaoning, "Evaluating Next Cell Predictors with Extensive Wi-Fi Mobility Data" (2006). Dartmouth Scholarship. 3121.
https://digitalcommons.dartmouth.edu/facoa/3121