A probabilistic approach to user mobility prediction for wireless services.

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dc.contributor.author Stynes, David A.
dc.contributor.author Brown, Kenneth N.
dc.contributor.author Sreenan, Cormac J.
dc.date.accessioned 2016-12-02T10:15:20Z
dc.date.available 2016-12-02T10:15:20Z
dc.date.issued 2016-09
dc.identifier.citation 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 en
dc.identifier.startpage 120 en
dc.identifier.endpage 125 en
dc.identifier.isbn 9781509003051
dc.identifier.issn 2376-6506
dc.identifier.uri http://hdl.handle.net/10468/3343
dc.identifier.doi 10.1109/IWCMC.2016.7577044
dc.description.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. en
dc.description.sponsorship Science Foundation Ireland (SFI Grant number 10/CE/I 1853, as part of CTVR) en
dc.description.uri http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7571053 en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher IEEE en
dc.relation.ispartof Wireless Communications and Mobile Computing Conference (IWCMC), 2016 International
dc.rights © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. en
dc.subject Mobility management (mobile radio) en
dc.subject Probability en
dc.subject Telecommunication services en
dc.subject G-Stat predictors en
dc.subject HEM predictors en
dc.subject Geolocation data sets en
dc.subject Mobile networks en
dc.subject Mobility predictions en
dc.subject Network resource management en
dc.subject Probabilistic approach en
dc.subject Probability distribution en
dc.subject User mobility prediction en
dc.subject Wireless services en
dc.subject Handover en
dc.subject History en
dc.subject Mobile communication en
dc.subject Prediction algorithms en
dc.subject Training data en
dc.subject Location Based Services en
dc.subject Mobile networking en
dc.subject Mobility prediction en
dc.subject Mobility and nomadicity en
dc.title A probabilistic approach to user mobility prediction for wireless services. en
dc.type Conference item en
dc.internal.authorcontactother David Andrew Stynes, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: d.stynes@4c.ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2016-12-02T10:04:42Z
dc.description.version Accepted Version en
dc.internal.rssid 374128635
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Paphos, Cyprus en
dc.internal.IRISemailaddress d.stynes@4c.ucc.ie en
dc.internal.IRISemailaddress c.sreenan@cs.ucc.ie en


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