A probabilistic approach to user mobility prediction for wireless services.

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Stynes, David A.
Brown, Kenneth N.
Sreenan, Cormac J.
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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.
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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