An Adaptive Machine Learning Algorithm for Location Prediction

Abstract

Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. In this paper, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Machine Learning (ML) is used for trajectory classification. Our algorithm adopts spatial and temporal on-line clustering, and relies on Adaptive Resonance Theory (ART) for trajectory prediction. The proposed algorithm applies a Hausdorff-like distance over the extracted trajectories handling location prediction. Since our approach is time-sensitive, the Hausdorff distance is considered more advantageous than a simple Euclidean norm. Two learning methods (non-reinforcement and reinforcement learning) are presented and evaluated. Finally, we compare our algorithm with Offline kMeans and Online kMeans algorithms. Our findings are very promising for the use of the proposed algorithm in mobile context aware applications.