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)
Computers & Mathematics with Applications
Mobility prediction using future knowledge
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile 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
A new Markov-based mobility prediction algorithm for mobile networks
EPEW'10 Proceedings of the 7th European performance engineering conference on Computer performance engineering
Combining local and global profiles for mobility prediction in LTE femtocells
Proceedings of the 15th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Femtocells sharing management using mobility prediction model
Proceedings of the 16th ACM international conference on Modeling, analysis & simulation of wireless and mobile systems
Location Prediction Based on a Sector Snapshot for Location-Based Services
Journal of Network and Systems Management
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In this paper, a new mobility prediction model is investigated. We consider a two nodes (enhanced gateway and base station) mobile network architecture based on intelligent IP framework. The proposed model is based on two complementary algorithms for mobility prediction: a global prediction algorithm and a local one. While the former is implemented in the gateway, the latter is used by the base station. At the gateway level, the algorithm detects the regular movements of the mobile users whereas the algorithm, at the base station level, tracks more closely, within the cell, the movements of the mobile users. Our prediction model gives significant results in both mobility conditions, regular and random movements.