VSMURF: A Novel Sliding Window Cleaning Algorithm for RFID Networks

نویسندگان

  • He Xu
  • Weiwei Shen
  • Peng Li
  • Daniele Sgandurra
  • Ruchuan Wang
چکیده

Radio Frequency Identification (RFID) is one of the key technologies of the Internet ofThings (IoT) and is used inmany areas, such asmobile payments, public transportation, smart lock, and environment protection. However, the performance of RFID equipment can be easily affected by the surrounding environment, such as electronic productions and metal appliances. These can impose an impact on the RF signal, whichmakes the collection of RFID data unreliable. Usually, the unreliability of RFID source data includes three aspects: false negatives, false positives, and dirty data. False negatives are the key problem, as the probability of false positives and dirty data occurrence is relatively small.This paper proposes a novel sliding window cleaning algorithm calledVSMURF, which is based on the traditional SMURF algorithm which combines the dynamic change of tags and the value analysis of confidence. Experimental results show that VSMURF algorithm performs better in most conditions and when the tag’s speed is low or high. In particular, if the velocity parameter is set to 2m/epoch, our proposed VSMURF algorithm performs better than SMURF. The results also show that VSMURF algorithm has better performance than other algorithms in solving the problem of false negatives for RFID networks.

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عنوان ژورنال:
  • J. Sensors

دوره 2017  شماره 

صفحات  -

تاریخ انتشار 2017