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Abstract
Unreliability of the data streams generated by RFID readers is among the primary factors which limit the widespread adoption of the RFID technology. RFID data cleaning is, therefore, an essential task in the RFID middleware systems in order to reduce reading errors, and to allow these data streams to be used to make a correct interpretation and analysis of the physical world they are representing. In this paper we propose an adaptive sliding-window based approach called WSTD which is capable of efficiently coping with both environmental variation and tag dynamics. Our experimental results demonstrate the efficacy of the proposed approach.
Highlights
Radio frequency identification (RFID) is a technology that allows an object, a place or a person to be automatically identified with neither physical nor visual contact
Window Sub-Range Transition Detection (WSTD) uses binomial sampling concepts to calculate the appropriate window size and π-estimator to estimate the number of tags as proposed by sMoothing for Unreliable RFid data (SMURF)
From this observation we can conclude that the main difference between WSTD and SMURF is in the transition detection mechanism, and WSTD performs better than SMURF in the mobile environment
Summary
Radio frequency identification (RFID) is a technology that allows an object, a place or a person to be automatically identified with neither physical nor visual contact. Adopting the statistical approaches proposed in SMURF, we developed our own adaptive cleaning scheme for RFID data streams, called WSTD, with a more efficient transition detection mechanism. WSTD is able to adapt its window size to cope with fluctuations of the tag-reader performance due to changes in the environment, while relatively accurately detecting the transition points. This is an integral part of our ongoing work on developing multi-agent based RFID middleware systems [22,23].
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