Tag Modulation Silencing: Design and Application in RFID Anti-Collision Protocols
Why this work is in the frame
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Bibliographic record
Abstract
Reliable and energy-efficient reading of Radio Frequency IDentification (RFID) tags is of utmost importance, especially in mobile and dense tag settings. We identify tag collisions as a main source of inefficiency in terms of wasting both medium access control (MAC) frame slots and reader's energy. We propose modulation silencing (MS), a reader-tag interaction framework to limit the effect of tag collisions. Utilizing relatively simple circuitry at the tag, MS enhances the performance of existing anti-collision protocols by allowing readers to terminate collision slots once a decoding violation is detected. With shorter collision slots, we revisit the performance metrics and introduce a new generalized time efficiency metric and an optimal frame selection formula that takes into consideration the MS effects. Through analytical solutions and extensive simulations, we show that the use of MS results in significant performance gains under various scenarios.
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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