Fast and Reliable Tag Search in Large-Scale RFID Systems: A Probabilistic Tree-based Approach
Bibliographic record
Abstract
Searching for a particular group of tags in an RFID system is a key service in such important Internet-of-Things applications as inventory management. When the system scale is large with a massive number of tags, deterministic search can be prohibitively expensive, and probabilistic search has been advocated, seeking a balance between reliability and time efficiency. Given a failure probability [1/(O(K))], where K is the number of tags, state-of-the-art solutions have achieved a time cost of O(K log K) through multi-round hashing and verification. Further improvement however faces a critical bottleneck of repetitively verifying each individual target tag in each round. In this paper, we present a novel Tree-based Tag Search (TTS) that approaches O (K) through batched verification. TTS smartly hashes multiple tags into each internal tree node and adaptively controls the node degrees. It conducts bottom-up search to verify tags group by group with the number of groups decreasing rapidly. We derive the optimal hash code length and node degrees to accommodate hash collisions, and demonstrate the superiority of TTS through both theoretical analysis and extensive simulations. In particular, we show that, with increasing reliability demand and system size, TTS achieves an even higher performance gain, making it a highly scalable solution.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".