Novel modulo based Aloha anti-collision algorithm for 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
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.
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