LeZi-update: an information-theoretic approach to track mobile users in PCS networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
A prediction system for multimedia pre-fetching in Internet
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Mobility modelling and trajectory prediction for cellular networks with mobile base stations
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Predictive distance-based mobility management for multidimensional PCS networks
IEEE/ACM Transactions on Networking (TON)
The predictive user mobility profile framework for wireless multimedia networks
IEEE/ACM Transactions on Networking (TON)
Building realistic mobility models from coarse-grained traces
Proceedings of the 4th international conference on Mobile systems, applications and services
BreadCrumbs: forecasting mobile connectivity
Proceedings of the 14th ACM international conference on Mobile computing and networking
Predictive methods for improved vehicular WiFi access
Proceedings of the 7th international conference on Mobile systems, applications, and services
Proceedings of the Eleventh Workshop on Mobile Computing Systems & Applications
Bartendr: a practical approach to energy-aware cellular data scheduling
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Route classification using cellular handoff patterns
Proceedings of the 13th international conference on Ubiquitous computing
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User mobility prediction can enable a mobile service provider to optimize the use of its network resources, e.g., through coordinated selection of base stations and intelligent content prefetching. In this paper, we study how to perform mobility prediction by leveraging the base station level location information readily available to a service provider. However, identifying real movements from handovers between base stations is non-trivial, because they can occur without actual user movement (e.g., due to signal fluctuation). To address this challenge, we introduce the leap graph, where an edge (or a leap) corresponds to actual user mobility. We present the properties of leap based mobility and demonstrate how it yields a mobility trace more suitable for mobility prediction. We evaluate mobility prediction on the leap graph using a Markov model based approach. We show that prediction using model can substantially improve the performance of content prefetching and base station selection during handover.