Group-Based Object Tracking Sensor Networks: Exploiting Group Moving Patterns

• Main Authors: Cheng-Huom Huang, 黃正和
• Other Authors: Wen-Chih Peng
• Format: Others
• Language: en_US
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/51868764888753366744

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 95 === 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.