Cardinality Estimation in RFID Systems with Multiple Readers
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Bibliographic record
Abstract
Radio frequency identification (RFID) is an emerging technology for automatic object identification. An RFID system consists of a set of readers and several objects, with each object equipped with a small chip, called a tag. In this paper, we consider the anonymous cardinality estimation problem in an RFID system consisting of several readers. To achieve complete system coverage and increase the accuracy of measurement, multiple readers with overlapping interrogation zones are deployed. We study the problem under two different circumstances. First, we assume that the readers cannot perform interrogations synchronously. This models the case when the readers are not equipped with accurate clocks or synchronization imposes a high overhead. Under such condition, we propose an asynchronous exclusive estimator to estimate the number of tags that are exclusively located in the zone of a selected reader. By using this estimator, we propose an asynchronous multiple-reader cardinality estimation (A-MRCE) algorithm. In the second scenario, we assume that readers can perform interrogations synchronously. We propose a synchronous exclusive estimator and a synchronous multiple-reader cardinality estimation (S-MRCE) algorithm to estimate the total number of tags. For the exclusive estimators, we show that they are asymptotically unbiased and we derive upper bounds on the variance of error. We validate our analytical model via simulations. Results show that although the A-MRCE algorithm enjoys the asynchronous operation of the readers, it performs worse than the S-MRCE algorithm in terms of estimation error. Compared to the enhanced zero-based (EZB) and lottery frame (LoF) algorithms, the variance of the estimation error for both A-MRCE and S-MRCE algorithms increases linearly with the number of readers, while it increases exponentially for EZB and LoF algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it