@inbook{165e9ef7ba004539bf552bba339aac5e,
title = "A probabilistic approach to user mobility prediction for wireless services",
abstract = "Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2\% inaccuracy in certain scenarios.",
keywords = "Location Based Services, Mobile networking, Mobility and Nomadicity, Mobility Prediction",
author = "David Stynes and Brown, \{Kenneth N.\} and Sreenan, \{Cormac J.\}",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 12th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2016 ; Conference date: 05-09-2016 Through 09-09-2016",
year = "2016",
month = sep,
day = "26",
doi = "10.1109/IWCMC.2016.7577044",
language = "English",
series = "2016 International Wireless Communications and Mobile Computing Conference, IWCMC 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "120--125",
booktitle = "2016 International Wireless Communications and Mobile Computing Conference, IWCMC 2016",
address = "United States",
}
