Prediction in wireless networks by Markov chains

Discrete sequence modeling and prediction is an important goal and a challenge for pervasive computing. Mobile clients' data request forecasting and location tracking in wireless cellular networks are characteristic application areas of sequence prediction in pervasive computing. This article presents information-theoretic techniques for discrete sequence prediction. It surveys, classifies, and compares the state-of-the-art solutions, suggesting routes for further research by discussing the critical issues and challenges of prediction in wireless networks.