A predictive location model for location-based services
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
A User Pattern Learning Strategy for Managing Users' Mobility in UMTS Networks
IEEE Transactions on Mobile Computing
Mobility prediction in cellular network using Hidden Markov model
CCNC'10 Proceedings of the 7th IEEE conference on Consumer communications and networking conference
Mobility profiler: A framework for discovering mobility profiles of cell phone users
Pervasive and Mobile Computing
Discovering routines from large-scale human locations using probabilistic topic models
ACM Transactions on Intelligent Systems and Technology (TIST)
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Nowadays, path prediction is being extensively examined for use in the context of mobile and wireless computing towards more efficient network resource management schemes. Path prediction allows the network and services to further enhance the quality of service levels that the user enjoys. In this paper we present a path prediction algorithm that exploits human creatures habits. In this paper, we present a novel hybrid Bayesian neural network model for predicting locations on Cellular Networks (can also be extended to other wireless networks such as WI-FI and WiMAX). We investigate different parallel implementation techniques on mobile devices of the proposed approach and compare it to many standard neural network techniques such as: Back-propagation, Elman, Resilient, Levenberg-Marqudat, and One-Step Secant models. In our experiments, we compare results of the proposed Bayesian Neural Network with 5 standard neural network techniques in predicting both next location and next service to request. Bayesian learning for Neural Networks predicts both location and service better than standard neural network techniques since it uses well founded probability model to represent uncertainty about the relationships being learned. The result of Bayesian training is a posterior distribution over network weights. We use Markov chain Monte Carlo methods (MCMC) to sample N values from the posterior weights distribution. These N samples vote for the best prediction. Simulations of the algorithm, performed using a Realistic Mobility Patterns, show increased prediction accuracy.