Anonymous Cardinality Estimation in RFID Systems with Multiple Readers
Bibliographic record
Abstract
In this paper, we study the anonymous cardinality estimation problem in radio frequency identification (RFID) systems. To preserve privacy and anonymity, each tag only transmits a portion of its ID to the reader when it is being queried. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. The cardinality estimation problem is to estimate the total number of tags (or the tag population) in an RFID system. We first propose an exclusive estimator to estimate the number of tags that are exclusively located in the interrogation zone of a selected reader. We then present a multiple-reader tag estimation (MRTE) algorithm that can accurately estimate the tag population using the measurement from different readers and the exclusive estimator. The accuracy of our proposed algorithm and the approximation are validated via simulations. We compare our proposed MRTE algorithm with the enhanced zero-based (EZB) and maximum a posteriori tag estimation (MPTE) algorithms. Although the mean of the estimation error for all three algorithms approaches zero under certain circumstances, the variance of the estimation error for MRTE algorithm increases linearly with the number of readers while it increases exponentially for EZB and MPTE algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".