To address the uncertainty of radio frequency identification(RFID)data due to the existing missed readings and cross readings
and to mitigate the information gap between RFID data and the requirements of upstream applications
an enhanced data interpretation method is proposed
which is able to handle with cross RFID readings over uncertain RFID streams. A dynamic graph model is chosen
supplemented with the history of RFID readings
to efficiently capture the possible locations and containment for tagged objects. Then an entropy-based method is adopted to estimate the most likely location and containment for each object. The method with the synthetic RFID data is also evaluated. The experimental results show the accuracy and efficiency. The error rates for both inferences get within 10% for common RFID deployment. The inference time is no more than 1 s
as the number
of objects over 1.7×10
5
and the used memory less than 700 MB by employing likelihood threshold-based trimming.
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references
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