Clustering object moving patterns for prediction-based object tracking sensor networks
Chih‐Chieh Hung, Wen-Chih Peng · 2009
Prior works have shown that probabilistic suffix trees (PST) could predict accurately the moving behaviors of objects for prediction-based object tracking sensor networks. However, maintaining PSTs for objects incurs a considerable amount of storage spaces for resource-constrained sensor nodes. In this paper, we derive a distance function between two PSTs and propose an algorithm to determine the similarity between them. By the distance between PSTs, we propose a clustering algorithm to partition objects with similar moving behaviors into groups. Furthermore, for each group, one PST is selected to predict movements of objects within one group. Experimental results show that our proposed approaches not only effectively reduce the storage cost but also provide good prediction accuracy.