Indexing Future Trajectories of Moving Objects in a Constrained Network

Abstract Advances in wireless sensor networks and positioning technologies enable new applications monitoring moving objects. Some of these applications, such as traffic management, require the possibility to query the future trajectories of the objects. In this paper, we propose an original data access method, the ANR-tree, which supports predictive queries. We focus on real life environments, where the objects move within constrained networks, such as vehicles on roads. We introduce a simulation-based prediction model based on graphs of cellular automata, which makes full use of the network constraints and the stochastic traffic behavior. Our technique differs strongly from the linear prediction model, which has low prediction accuracy and requires frequent updates when applied to real traffic with velocity changing frequently. The data structure extends the R-tree with adaptive units which group neighbor objects moving in the similar moving patterns. The predicted movement of the...

• Type: Journal
• Year: 2007
• Where: JCST
• Authors: Ji-Dong Chen, Xiao-Feng Meng
• Keywords: Access Methods, Adaptive Unit, JCST 2007, Prediction Model