A probabilistic approach to user mobility prediction for wireless services.
Stynes, David A.
Brown, Kenneth N.
Sreenan, Cormac J.
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 ,  and Crawdad . We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.
Mobility management (mobile radio) , Probability , Telecommunication services , G-Stat predictors , HEM predictors , Geolocation data sets , Mobile networks , Mobility predictions , Network resource management , Probabilistic approach , Probability distribution , User mobility prediction , Wireless services , Handover , History , Mobile communication , Prediction algorithms , Training data , Location Based Services , Mobile networking , Mobility prediction , Mobility and nomadicity
Stynes, D., Brown, K. N.;Sreenan, C. J. (2016) 'A probabilistic approach to user mobility prediction for wireless services', 2016 International Wireless Communications and Mobile Computing Conference (IWCMC) Wireless Communications and Mobile Computing Conference (IWCMC), Paphos, Cyprus, 05/09/2016- 09/12/2016. doi: 10.1109/IWCMC.2016.7577044
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