A Machine Learning Auxiliary Approach for the Distributed Dense RFID Readers Arrangement Algorithm
Bibliographic record
Abstract
This paper is an extended version of the work published. Radio-frequency identification (RFID) is widespread in industries such as supply-chain management and logistics due to its low-cost feature. In many real-world problems, one often needs to leverage a considerable amount of RFID readers to cover a large area. Many graph-based dense RFID readers system anti-collision algorithms were proposed to address the collision problems. However, state-of-the-art collision avoidance algorithms are centralized algorithms. In a dense RFID system, the graphs generated by the centralized algorithms could be very complicated. Therefore, a centralized algorithm increases the computational workload of the central server. We proposed a distributed anti-collision algorithm based on the idea of a centralized collision avoidance algorithm called MWISBAII. In our later research, we found that due to the lack of global information, there is a gap between the performance of our distributed algorithm and the centralized MWISBAII. To narrow this gap, we introduced machine learning into the proposed algorithm. The machine learning model is an empirical model that mitigates the deficiency of the lack of global information. The experimental results show that the proposed distributed algorithm with machine learning can get almost the same performance as the centralized MWISBAII in different experimental settings.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".