Abstract
The Radio Frequency Identification (RFID) data acquisition rate used for monitoring is so high that the RFID data stream contains a large amount of redundant data, which increases the system overhead. To balance the accuracy and real-time performance of monitoring, it is necessary to filter out redundant RFID data. We propose an algorithm called Time-Distance Bloom Filter (TDBF) that takes into account the read time and read distance of RFID tags, which greatly reduces data redundancy. In addition, we have proposed a measurement of the filter performance evaluation indicators. In experiments, we found that the performance score of the TDBF algorithm was 5.2, while the Time Bloom Filter (TBF) score was only 0.03, which indicates that the TDBF algorithm can achieve a lower false negative rate, lower false positive rate, and higher data compression rate. Furthermore, in a dynamic scenario, the TDBF algorithm can filter out valid data according to the actual scenario requirements.
Highlights
Radio Frequency Identification (RFID) is a wireless communication technology that has the advantages of low cost, low power consumption, and easy deployment
A typical RFID system consists of three parts: an RFID tag, which is attached to the object to be identified and can be read from up to several feet away and does not need to be within the direct line of sight of the reader; a reader, which is responsible for generating interactive information with the tag, such as reading data from the tag or modifying tag information; and software, such as the middleware that collects and processes the readings from readers and transforms raw data into meaningful information for the application [3]
An RFID data stream contains a large amount of redundant data, which often has no practical value and reduces the operating efficiency of the system
Summary
Radio Frequency Identification (RFID) is a wireless communication technology that has the advantages of low cost, low power consumption, and easy deployment. An RFID reader collects RFID data streams on the tags, keeps them within its detection range, and sends them to the server, and, as a result, a massive amount of data is generated, most of it is redundant and useless These redundant data increase network latency and take up valuable system storage space. The contributions of this paper are as follows: (a) propose an algorithm called TimeDistance Bloom Filter (TDBF) to eliminate redundant RFID data in multiple dimensions, (b) propose a novel filter efficiency measurement formula for RFID data, (c) perform experimental analysis of the proposed algorithm and its performance in static and dynamic scenarios, and (d) strike a balance between the need for real-time performance and accuracy when filtering the data in a monitoring scenario.
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