DiSEL: A Distance Based Slot Selection Protocol for Framed Slotted ALOHA RFID Systems
Bibliographic record
Abstract
This paper introduces a new medium access control (MAC) protocol for passive Radio Frequency Identification (RFID) systems. The protocol is designed as an enhancement to framed slotted ALOHA MAC protocols in which tags randomly select a slot number on a given frame size. As shown in this paper, the completely random slot selection in the framed slotted ALOHA systems is not the optimum approach to the slot selection problem. To minimize the collision probability, our protocol, named Distance Based Slot Selection (DiSEL), uses a cross- layer approach for tags to select the most appropriate time slot in a given frame. A tag in DiSEL uses the maximum and minimum received power levels of the reader-tag communications to choose a slot number. A resonant boosting network to increase the received RF power granularity and an efficient rectifier to convert the RF signal into DC introduced for the power level measurements at the tags. We test DiSEL under various tag deployment and density scenarios and show that DiSEL decreases the tag collision probability in both random uniform and evenly spaced dense tag deployments.
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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 | 0.003 |
| 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 | 0.002 |
| 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".