Decentralized RFID Coverage Algorithms With Applications for the Reader Collisions Avoidance Problem
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
We aim in this paper at eliminating data and reader redundancies in Radio Frequency Identification (RFID) reader networks. These redundancies have negative impact on the performance of an RFID reader network and in analyzing the readings of the network. We meet our objectives by introducing decentralized RFID coverage [reader collision avoidance (RCA)] algorithm. The RFID coverage problem consists of two subproblems: 1) the tag reporting problem, which aims at assigning to each tag in the network a reader responsible for reporting its data and 2) the redundant readers elimination problem, which aims at minimizing the number of readers in the network while preserving the tags coverage. We introduce two deterministic decentralized RFID coverage algorithms called orientation-based coverage and iterated orientation-based coverage (IOB-COVERAGE). The first algorithm runs in one communication round, whereas the latter runs in O(n) rounds, where n is the number of readers in the network. These algorithms are the first decentralized RFID coverage algorithms that use reader-to-reader communications only. We later introduce an algorithm that solves the RCA algorithm, called IOB-(RCA+COV). The algorithm is a minor modification of IOB-COVERAGE. We formally prove the correctness of our algorithms, and we use detailed simulation experiments to study their performance.
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