针对不确定射频识别数据流的改进概率推导方法

聂艳明; 李战怀; 陈群

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针对不确定射频识别数据流的改进概率推导方法

更新时间：2025-07-09

- 针对不确定射频识别数据流的改进概率推导方法
- Enhanced Probabilistic Interpretation over Uncertain Radio Frequency Identification Data Streams
- 西安交通大学学报 2011年45卷第12期页码：45-52
- 作者机构：
  
  西北工业大学计算机学院,西安,710072
- 作者简介：
- 基金信息：
  
  : 2011-05-24. 作者简介: 聂艳明(1972-), 男,博士生;李战怀(通信作者),男,教授,博士生导师. 基金项目: 国家自然科学基金资助项目(60720106001, 61033007)
- DOI：
  中图分类号： TP311
- 网络首发：2011-12-10，
  
  纸质出版：2011
- 稿件说明：
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聂艳明, 李战怀, 陈群. 针对不确定射频识别数据流的改进概率推导方法[J]. 西安交通大学学报, 2011,45(12):45-52.

Enhanced Probabilistic Interpretation over Uncertain Radio Frequency Identification Data Streams[J]. 2011, 45(12): 45-52.
聂艳明, 李战怀, 陈群. 针对不确定射频识别数据流的改进概率推导方法[J]. 西安交通大学学报, 2011,45(12):45-52. DOI：

Enhanced Probabilistic Interpretation over Uncertain Radio Frequency Identification Data Streams[J]. 2011, 45(12): 45-52. DOI：

摘要

针对射频识别(RFID)数据存在漏读和交叉读而导致所提供的位置信息不准确

以及RFID数据与上层应用需求之间存在的信息鸿沟

提出了一种可以处理RFID交叉读问题的改进的RFID数据推导方法.该方法利用动态图模型并辅以历史RFID识读

从不确定RFID数据流上有效捕获对象的当前状态

采用基于熵的方法推导对象的最可能位置与包含

并且利用仿真物流仓库的RFID模拟数据进行算法评价.实验结果显示

该方法在获得准确推导结果的同时

能确保其高效性.对于常见RFID部署

位置推导和包含推导的错误率都可以控制在10%以内

针对超过17万个节点的推导所用时间小于1 s

采用修剪措施后内存使用低于700 MB.

Abstract

To address the uncertainty of radio frequency identification(RFID)data due to the existing missed readings and cross readings

and to mitigate the information gap between RFID data and the requirements of upstream applications

an enhanced data interpretation method is proposed

which is able to handle with cross RFID readings over uncertain RFID streams. A dynamic graph model is chosen

supplemented with the history of RFID readings

to efficiently capture the possible locations and containment for tagged objects. Then an entropy-based method is adopted to estimate the most likely location and containment for each object. The method with the synthetic RFID data is also evaluated. The experimental results show the accuracy and efficiency. The error rates for both inferences get within 10% for common RFID deployment. The inference time is no more than 1 s

as the number

of objects over 1.7×10

and the used memory less than 700 MB by employing likelihood threshold-based trimming.

关键词

Keywords

references

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谷峪,于戈,胡小龙,等. 基于监控对象动态聚簇的高效RFID数据清洗模型[J]. 软件学报,2010,21(4): 632-643.

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TRAN T, SUTTON C, COCCI R, et al. Probabilistic inference over RFID streams in mobile environments [C]∥Proceedings of the 25th International Conference on Data Engineering. Piscataway, NJ, USA: IEEE, 2009: 1096-1107.

COCCI R, TRAN T, DIAO Y, et al. Efficient data interpretation and compression over RFID streams [C]∥Proceedings of the 24th International Conference on Data Engineering. Piscataway, NJ, USA: IEEE, 2008: 1445-1447.

EPCglobal Inc. EPC Tag data standard version 1.5 [EB/OL]. [2010-08-18]. http:∥www.gs1.org/gsmp/kc/epcglobal/tds/tds_1_5-standard-20100818.pdf.

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