標題: 無線感測網路探勘物體移動路徑機制
On Mining Moving Patterns for Object Tracking Sensor Networks
作者: 柯郁任
Yu-Jen Ko
彭文志
Wen-Chih Peng
資訊科學與工程研究所
關鍵字: 物體追蹤;資料探勘;可變式馬可夫模型;object tracking;data mining;variable memory Markov
公開日期: 2005
摘要: 由於無線傳輸和嵌入式技術的快速發展,越來越多應用能夠用於無線感測網路,物體移動追蹤乃是前景看好的應用之一。基於物體移動通常具有其規律性,我們提出一追蹤模式,稱為HTM,以利有效率地探勘物體移動樣式和監控物體。另一方面,由於物體移動通常和其之前所走過的路徑有關,我們採用variable memory Markov的方式來探勘物體移動樣式。再者,由於HTM有階層的特性,我們利用此特性使移動樣式具有不同解析度。按照探勘出的移動樣式,我們所提出的HTM追蹤模式可以精確地預測物體移動路徑,進而降低感測器的能量損耗。更明確的說,HTM分為兩個階段:資料收集和探勘階段和預測階段。在資料收集和探勘階段中,感測器必須持續感測其所負責的區域以收集物體移動的資料。當資料收集足夠且已探勘出足夠的移動樣式,便進入預測階段。在預測階段中,感測器轉為休眠狀態,只有些許必要的感測器會被喚醒以追蹤物體,藉此達到省電的目的。除此之外,由於感測器的容量有限,我們提出兩個方式來建立HTM及一記憶體管理的方法。實驗當果証明我們提出的HTM能夠有效地探勘物體移動樣式及追蹤物體。
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.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT009217633
http://hdl.handle.net/11536/74379
Appears in Collections:Thesis