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A self-learning Smart Trend-Traversal (STT) protocol for tag arbitration is proposed in this work, which effectively reduces the collision overhead occurred in large-scale RFID systems. The protocol dynamically issues queries according to the adaptively learned tag density and distribution; and therefore, it significantly reduces delay and energy consumption. The optimality of STT does not rely on any presumed network conditions, which is in sharp contrast to other available schemes and renders it a highly desirable and practical solution.
Date of Publication: November 2011