基于监控对象动态聚簇的高效RFID数据清洗模型

由于RFID(radio frequency identification)技术采用无线射频信号进行数据通信,漏读和多读现象时有发生,降低了其在事件检测中查询结果的准确性.在很多RFID监控应用中,监控物体都是以动态变化的小组为单位进行活动的.通过定义关联度和动态聚簇对各个RFID监控物体所在的小组进行动态的分析,并在此基础上定义了一套关联度维护和数据清洗的模型和算法,通过对图模型进行压缩,提出了基于分裂重组思想的链模型关联度维护策略,提高了维护的时空效率.模拟实验结果表明,该数据清洗模型可以获得较好的效率和准确性.

Efficient RFID Data Cleaning Model Based on Dynamic Clusters of Monitored Objects

Because wireless radio frequency signal is adopted during the RFID (radio frequency identification) communication, many false negative and false positive readings may be produced leading to inaccurate event detection. In RFID-based applications, monitored objects often progress in the form of group. In this paper, by analyzing the group changes based on the defined association degree and dynamic clusters, a series of models and algorithms about association degree maintenance and data cleaning are proposed. Especially, by compressing the graph model, an association degree maintenance strategy based on splitting and recombining list model is discussed to improve the time and space performance. Simulated experiments have demonstrated the efficiency and accuracy of the proposed data cleaning model.