基于布隆过滤器的RFID数据冗余处理算法研究

RFID技术作为物联网领域的关键技术，具有广阔的应用前景。然而RFID设备在读取标签信息时会产生大量冗余数据。因此，RFID数据冗余处理的研究对于减少RFID中间件系统负荷、快速检测出入标签有着重要的意义。之前针对RFID数据冗余过滤的研究往往是单维度、静态场景的简单过滤，无法实现复杂场景下标签的出入检测。因此，本文提出一种名为时间距离布隆过滤器（TDBF）的算法，该算法从时间和空间两个维度进行冗余过滤。与常用的时间布隆过滤器相比，该算法兼顾了RFID标签的读取时间和读取距离，极大的降低了数据的冗余问题。在保证漏读率较低的情况下，极大的降低了数据的误读率。同时该算法支持动态场景中移动标签的冗余过滤，能够较好的满足出入监控需求。

Redundant RFID Data Filtering Algorithm Research Based on Bloom Filter

As a key technology in the field of Internet of Things, RFID technology has broad application prospects. However, RFID devices generate a large amount of redundant data when reading tag information. Therefore, the research on RFID data redundancy processing is of great significance for reducing the load of RFID middleware system and quickly detecting incoming and outgoing tags. Previous studies on the redundancy filtering of RFID data are often simple filtering of single-dimensional and static scenes, and it is impossible to detect the ingress and egress of tags in complex scenarios. Therefore, this paper proposes an algorithm called Time Distance Bloom Filter (TDBF), which performs redundant filtering from both time and space. Compared with the Time Bloom filter, this algorithm takes into account the reading time and reading distance of the RFID tags, which greatly reduces the redundancy of the data. In the case of ensuring a low miss rate, the false-positive rate of the data is greatly reduced. At the same time, the algorithm supports redundant filtering of mobile tags in dynamic scenarios, which can better meet the requirements of access control.