A mobility prediction system leveraging realtime location data streams : poster

Détails

ID Serval
serval:BIB_FE3B32726ED2
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
A mobility prediction system leveraging realtime location data streams : poster
Titre de la conférence
Proceedings of the 22nd Annual International Conference on Mobile Computing and Networking - MobiCom '16
Auteur(s)
Kulkarni V., Moro A., Garbinato B.
Editeur
ACM Press
Adresse
New York, USA
ISBN
9781450342261
Statut éditorial
Publié
Date de publication
2016
Pages
430-432
Langue
anglais
Résumé
Location-based services today, exceedingly depend on user mobility prediction, in order to push context aware services ahead of time. Existing location forecasting techniques are driven by large volumes of data to train the prediction models in a centralised server. This amounts to considerably long waiting times before the model kicks in. Disclosing highly sensitive location information to third party entities also exposes the user to several privacy risks. To address these issues, we put forth a mobility prediction system, able to provide swift realtime predictions, evading the strenuous training procedure. We enable this by constantly adapting the model to substantive user mobility behaviours that facilitate accurate predictions even on marginal time bounded movements. In comparison to existing frameworks, we utilise less volumes of data to produce satisfactory prediction accuracies. This in turn lowers the computational complexity making implementation on mobile devices feasible and a step towards privacy preservation. Here, only the predicted location can be sent to such services to maintain the utility/privacy tradeoff. Our preliminary evaluations based on real world mobility traces corroborate our hypothesis.
Création de la notice
13/07/2017 15:14
Dernière modification de la notice
21/08/2019 5:17
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