DiSEL: A Distance Based Slot Selection Protocol for Framed Slotted ALOHA RFID Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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