PREDICTION IN WIRELESS NETWORKS BY MARKOV CHAINS

Abstract

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.