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A Probabilistic Approach to User Mobility Prediction for Wireless Services

Authors: 

David Stynes, Ken Brown, Cormac Sreenan

Publication Type: 
Refereed Conference Meeting Proceeding
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.
Conference Name: 
IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), 2016
Proceedings: 
IEEE International Wireless Communications and Mobile Computing Conference (IWCMC), 2016
Digital Object Identifer (DOI): 
10.1109/IWCMC.2016.7577044
Publication Date: 
06/09/2016
Pages: 
120 - 125
Conference Location: 
Cyprus
Institution: 
National University of Ireland, Cork (UCC)
Open access repository: 
No