Novel modulo based Aloha anti-collision algorithm for RFID systems
Bibliographic record
Abstract
RFID (Radio frequency Identification) has become an efficient way to identify, track and/or trace objects and people. Its importance has motivated scientists and researchers to examine the challenges that are slowing its expeditious deployment in various applications. RFID collision is a major challenge imposed by the wireless links shared among a reader and the many tags in the interrogation zone. In most proposed anti-collision algorithms, tags reply randomly to time slots chosen by the reader. Since two or more tags may choose the same slot, this Random Access (RA) causes garbled data at the reader side; therefore, the identification process fails. In this paper, we propose a new anti-collision algorithm that adopts a novel method for eliminating the theory of RA to enhance system efficiency and to reduce both the number of rounds between reader and tag and the number of collided/empty slots over existing algorithms. In this algorithm, tags use modulo function to choose tag owned time slot. Another advantage of this method is that the reader estimates the next frame size and compares it with the previously selected frame sizes that are saved in the reader to ensure there is no redundancy. The performance of the algorithm is simulated and compared with existent ALOHA family 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".