2 research outputs found
RFID data reliability optimizer based on two dimensions bloom filter
Radio Frequency Identification (RFID) is a flexible deployment technology that has
been adopted in many applications especially in supply chain management. It
provides several features such as to monitor, to identify and to track specific item
hidden in a large group of objects in a short range of time. RFID system uses radio
waves to perform wireless interaction to detect and read data from the tagged object.
However, RFID data streams contain a lot of false positive and duplicate readings.
Both types of readings need to be removed to ensure reliability of information
produced from the data streams. A small occurrence of false positive can change the
whole information, while duplicate readings unnecessarily occupied storage and
processing resources. Many approaches have been proposed to remove false positive
and duplicate readings, but they are done separately. These readings exist in the same
data stream and must be removed using a single mechanism only. In this thesis, an
efficient approach based on Bloom filters was proposed to remove both noisy and
duplicate data from the RFID data streams. The noise and duplicate filter algorithm
was constructed based on bloom filter. There are two bloom filters in one algorithm
where each filter holds function either to remove noise data and to recognize data as
correct reading from duplicate data reading. In order to test the algorithm, synthetic
data was generated by using Poisson distribution. The simulation results show that
our proposed approach outperformed other existing approaches in terms of data
reliability