Context Prediction Based on Branch Prediction Methods

Ubiquitous systems use context information to adapt appliance behavior to human needs. Even more convenience is reached if the appliance foresees the user’s desires and acts proactively. This paper focuses on context prediction based on previous behavior patterns. The proposed prediction algorithms originate in branch prediction techniques of current high-performance microprocessors which are transformed to handle context prediction. We propose and evaluate the onelevel one-state, two-state, and multiple-state predictors, and the two-level two-state predictors with local and global first-level histories. Evaluation is performed by simulating the predictors with behavior patterns of people walking through a building as workload. The evaluations show that the proposed context predictors perform well but exhibit differences in training and retraining speed and in their ability to learn complex patterns.