On Mining Moving Patterns for Object Tracking Sensor Networks

• Main Authors: Yu-Jen Ko, 柯郁任
• Other Authors: Wen-Chih Peng
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/8cv2a5

碩士 === 國立交通大學 === 資訊工程系所 === 94 === The rapid progress of wireless communication and embedded technologies has made wireless sensor networks possible. Since sensor networks are typically used to monitor the environment, one promising application of sensor networks is object tracking. Based on the fact that the movements of the tracked objects generally reflect periodic behaviors, we propose a heterogeneous tracking model, referred to as HTM, to efficiently mine object moving patterns and track objects. Specifically, since the movements of objects have the feature of dependencies, we explore variable memory Markov to mine object moving patterns. Furthermore, due to the hierarchical feature of HTM, multi-resolution object moving patterns are provided. In light of object moving patterns, our proposed HTM is able to accurately predict the movements of objects and thus reduces the energy consumption for object tracking. Explicitly, HTM consists two phases: data collection and mining phase and prediction phase. In data collection and mining phase, all sensors will turn on and monitor the whole sensing region to collect movements of objects. Once collecting sufficient movements of objects, sensor nodes will be in prediction phase. In prediction phases, sensor nodes turn to sleep modes so as to save energy consumption. Only selected sensor nodes will be activated to track objects according to the object moving patterns. Moreover, due to the storage constraint on sensor nodes, we devise two storage strategies to build HTM. Performance of the proposed HTM is analyzed and sensitivity analysis on several design parameters is conducted. Simulation results show that HTM is able to not only effectively mine object moving patterns but also efficiently track objects.