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    廖国琼, 李 晶. 基于距离的分布式RFID数据流孤立点检测[J]. 计算机研究与发展, 2010, 47(5): 930-939.
    引用本文: 廖国琼, 李 晶. 基于距离的分布式RFID数据流孤立点检测[J]. 计算机研究与发展, 2010, 47(5): 930-939.
    Liao Guoqiong, Li Jing. Distance-Based Outlier Detection for Distributed RFID Data Streams[J]. Journal of Computer Research and Development, 2010, 47(5): 930-939.
    Citation: Liao Guoqiong, Li Jing. Distance-Based Outlier Detection for Distributed RFID Data Streams[J]. Journal of Computer Research and Development, 2010, 47(5): 930-939.

    基于距离的分布式RFID数据流孤立点检测

    Distance-Based Outlier Detection for Distributed RFID Data Streams

    • 摘要: RFID技术已广泛应用于实时监控、对象标识及跟踪等领域,及时发现被监控标签对象的异常状态显得十分重要.然而,由于无线通信技术的不可靠性及环境因素影响,RFID阅读器收集到的数据常常包含噪声.针对分布式RFID数据流的海量、易变、不可靠及分布等特点,提出了基于距离的局部流孤立点检测算法LSOD和基于近似估计的全局流孤立点检测算法GSOD.LSOD需要维护数据流结构CSL来识别安全内点,然后运用安全内点的特性来节省流数据的存储空间和查询时间.根据基于距离的孤立点定义,在中心节点上的全局孤立点是位于每个分布节点上孤立点集合的子集.GSOD采用抽样方法进行全局孤立点近似估计,以减少中心节点的通信量及计算负荷.实验表明,所给出的算法具有运行时间短、占用内存小、准确率高等特点.

       

      Abstract: Recently, RFID technologies have been widely used in many fields, such as real-time monitoring, object identification and item tracing, so it is very important to detect abnormal states of tagged objects in time. However, due to various environmental factors and unreliability of wireless communication technology, the data collected by the RFID readers are often noisy. According to the characteristics of distributed RFID data stream, such as huge-volume, variability, unreliability and distribution, the authors propose a distance-based local stream outlier detection algorithm (LSOD) and an approximate estimate-based global stream outlier detection algorithm (GSOD). LSOD need maintain a data stream structure, CSL, to identify the safe inlier. With the help of the characteristics of safe inliers, LSOD not only can reduce the memory space of stream data, but also can save the query time on stream objects. Under the distance-based outlier definition, it is a fact that global outlier on center node is a subset of the union of local outliers on every distributed node. Thus, GSOD uses a sample-based method to estimate approximately the global outliers for reducing the communication volume and calculate load of the centre node. Finally, several experiments have been accomplished, confirming that the proposed algorithms have the characteristics such as short running time, small memory space and high accuracy.

       

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