The shadow cluster concept for resource allocation and call admission in ATM-based wireless networks
MobiCom '95 Proceedings of the 1st annual international conference on Mobile computing and networking
A class of mobile motion prediction algorithms for wireless mobile computing and communication
Mobile Networks and Applications - Special issue: routing in mobile communications networks
A predictive bandwidth reservation scheme using mobile positioning and road topology information
IEEE/ACM Transactions on Networking (TON)
Mobility prediction using future knowledge
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Mobility, Data Mining and Privacy: Geographic Knowledge Discovery
Visually driven analysis of movement data by progressive clustering
Information Visualization
A hierarchical prediction model for two nodes-based IP mobile networks
Proceedings of the 12th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
A new Markov-based mobility prediction algorithm for mobile networks
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Mobility Prediction Using Mobile User Profiles
MASCOTS '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Predictive mobility support for QoS provisioning in mobile wireless environments
IEEE Journal on Selected Areas in Communications
Femtocells sharing management using mobility prediction model
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
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We propose in this paper a mobility prediction model based on the notions of local and global mobile-user profiles. The local profiles are associated with a mobile user and correspond to its frequent and similar movements, whereas the global profiles match with the frequent and similar movements of the majority of users in the covered area. We consider the LTE network architecture with possible deployment of femtocells. The prediction model combines two complementary algorithms: the global profiles-based algorithm and the local profiles-based one. The former is implemented in the enhanced Node B and the home enhanced Node B and the latter works at the user terminal level. An algorithmic approach is used to identify such local and global profiles from real cellular network datasets and we show how to use them for an efficient mobility prediction. Simulation results show that our approach is significantly efficient in predicting both random and regular movements.