R-TDBF: An Environmental Adaptive Method for RFID Redundant Data Filtering

Ziwen Cao, Degang Sun, Siye Wang, Yanfang Zhang, Yue Feng, Shang Jiang

Abstract: Radio Frequency Identification (RFID) technology plays an essential role in surveillance scenarios. However, redundant data hinders the efficient processing of data. The processing of RFID redundant data is of great importance to reduce the load of the RFID system and quickly detect the monitored tags. To address the issue, the research community introduced Bloom filtering technology into the RFID system. However, existing methods often use fixed thresholds and cannot adapt to complex environmental conditions. This work presents R-TDBF, a practical solution that enables data redundancy filtering in complex environments by rationally setting filtering thresholds. In addition, a signal strength threshold is also introduced in R-TDBF, which reduces the error caused by signal fluctuation. The experimental results show that the R-TDBF algorithm can filter redundant data well under different threshold conditions. Compared with the existing algorithms, our method has good practicality with an average reduction of 73.7% in the detection error rate.