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Discrete sequence modeling and prediction is a fundamental goal and a challenge for location-aware computing. Mobile client’s data request forecasting and location tracking in wireless cellular networks are characteristic application areas of sequence prediction in pervasive computing, where learning of sequential data could boost the underlying network’s performance. Approaches inspired from information theory comprise ideal solutions to the above problems, because several overheads in the mobile computing paradigm can be attributed to the randomness or uncertainty in a mobile client’s movement or data access. This article presents a new information-theoretic technique for discrete sequence prediction. It surveys the state-of-the-art solutions and provides a qualitative description of their strengths and weaknesses. Based on this analysis it proposes a new method, for which the preliminary experimental results exhibit its efficiency and robustness.

A Suffix Tree Based Prediction Scheme for Pervasive Computing Environments