Movement Simulation and Analysis

Abstract

Local mining phase finds movement patterns based on the local trajectories. This is derived on the movement patterns and moving object with similar single object or group of object. To address the energy conservation issue in resource-constrained from transmits local grouping results. Mining group movement patterns for tracking moving object efficiently is the tracking with similar movement patterns as found using a single object or group of object. And the mining results to track moving the object efficiently, at the same time data mining algorithm achieves to reduce the energy consumption by reducing the amount of data to be transmitted from one local to another local group mining. The proposed algorithm comprises a local mining phase and a cluster ensemble phase. In the local mining phase, the algorithm finds movement patterns based on local trajectories. Then, based on the derived patterns, we propose a new similarity measure to compute the similarity of moving objects and identify the local group relationships. To address the energy conservation issue in resource-constrained environments, the algorithm only transmits the local grouping results to the sink node for further assembling. In the cluster ensemble phase, our algorithm combines the local grouping results to derive the group relationships from a global view. We further leverage the mining results to track moving objects efficiently. The results of experiments show that the proposed mining algorithm achieves good grouping quality, and the mining technique helps reduce the energy consumption by reducing the amount of data to be transmitted.