無線感測網路探勘群集式物體移動路徑機制

Abstract

在無線感測網路中利用預測技術來追□物體的移動路徑可以減少能源的耗費。在之前的研究中，我們透過探勘物體的移動模式來預測無線感測網路中物體的移動路徑並且發展階層式的架構來有效率地追□物體。感測節點本質上具有儲存空間的限制。然而，儲存每一個物體的移動樣式需要消耗感測節點大量的儲存空間，這會增加實際應用的難度。因此，在本篇論文中，我們利用群組移動模型的特色，提出了在具儲存空間限制的無線感測感網路中進行群集式物體追□的應用，簡稱GBOT。首先，我們制定物體移動樣式之間的相似度，在此移動樣式被表示成散發樹。根據所制定的相似度，我們可以推知物體間的相似度關係。在給定物體間的相似度關係，我們進一步地提出分群演算法，將具有相似移動模式的物體分成一個群組。之後，我們會為每一個群組選出最具代表性的散發樹並且使用這個散發樹來預測群組的移動路徑。此外，當物體的移動行為改變時，我們也設計了一個演算法來維持無線感測網路的預測準確率。實驗結果顯示GBOT應用於具儲存空間限制的無線感測感網路中，不僅有效地減少儲存的成本並且有著極佳的預測準確率。

關鍵字：物體追□，無線感測網路，群組移動模型，分群。

Predication-based techniques are able to reduce the energy consumption in object tracking sensor networks. Prior works exploit mining object moving patterns for prediction-based object tracking sensor network and developed a hierarchical architecture to efficiently track objects. Note that sensors are inherently storage-constrained. Clearly, mining and storing individual object moving patterns unavoidably need a considerable amount of storage spaces in sensor nodes, which is not of practical. Thus, in this paper, we propose a group-based object tracking sensor network (abbreviated as GBOT) which explores the feature of group mobility of objects for storage-constrained object tracking sensor networks. Specifically, we first formulate a dissimilarity function among object moving patterns, where object moving patterns are viewed as emission trees. In light of the dissimilarity function, the dissimilarity relationships among objects are derived. Given dissimilarity relationships among objects, we further propose two clustering schemes to discover group mobility patterns of objects. Furthermore, for each group, we judiciously select one representative emission tree and utilize this emission tree for prediction. In addition, a maintenance algorithm is derived to preserve the prediction accuracy when moving behaviors of objects vary. Experimental results show that GBOT not only effectively reduces storage cost but also has a good prediction accuracy in storage-constrained sensor networks.