DISCRIMINATION OF SENSING DATA IN NORMAL AND ABNORMAL SITUATIONS OF THE MONITORED OBJECT OR ENVIRONMENT

摘要：

The huge volume of history sensing data of a wireless sensor network need to be processed and discriminated to help the users of the data to analyze and judge the different situations of the monitored object and environment. A novel approach is proposed to first divide the history sensing data into partitions so that the data, measured when the monitored object or environment is normal, are roughly distinguishable from those measured when the object or environment is abnormal. Then the method uses a new centroid-based clustering algorithm to group the data in the partitions into different clusters. Finally the clusters of data are labeled “normal or “abnormal by applying the suggested heuristics.

关键词： history sensing data data partitioning clustering data discrimination normal or abnormal situations

作者: Yinghua Zhou Xuemei Cai

作者单位: College of Computer Science and Technology, College of Optoelectronic Engineering,Chongqing University of Posts and Telecommunications, Chongqing

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

会议地点: 北京

会议语种:英文

页码: 36-40

在线出版日期: 2009-11-06（万方平台首次上网日期，不代表论文的发表时间）