Efficient and robust missing key tag identification for large-scale RFID systems
Bibliographic record
Abstract
Radio Frequency Identification (RFID) technology has been widely used to identify missing items. In many applications, rapidly pinpointing key tags that are attached to favorable or valuable items is critical. To realize this goal, interference from ordinary tags should be avoided, while key tags should be efficiently verified. Despite many previous studies, how to rapidly and dynamically filter out ordinary tags when the ratio of ordinary tags changes has not been addressed. Moreover, how to efficiently verify missing key tags in groups rather than one by one has not been explored, especially with varying missing rates. In this paper, we propose an Efficient and Robust missing Key tag Identification (ERKI) protocol that consists of a filtering mechanism and a verification mechanism. Specifically, the filtering mechanism adopts the Bloom filter to quickly filter out ordinary tags and uses the labeling vector to optimize the Bloom filter's performance when the key tag ratio is high. Furthermore, the verification mechanism can dynamically verify key tags according to the missing rates, in which an appropriate number of key tags is mapped to a slot and verified at once. Moreover, we theoretically analyze the parameters of the ERKI protocol to minimize its execution time. Extensive numerical results show that ERKI can accelerate the execution time by more than 2.14× compared with state-of-the-art solutions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".