Thermal Piloting: A Novel Approach for Sensor Localization in Data Center Monitoring
Bibliographic record
Abstract
Monitoring ambient air temperature is one of the important operations to ensure resilience and efficiency in large-scale data centers. However, deployment of a data center monitoring system requires recording the location of thousands of sensors which is a labor-intensive task if is done manually. Since Radio-Frequency (RF) based localization solutions in literature are inadequate in the multipath rich environment of data centers, we investigated the possibility of utilizing the measurements of the sensors in localizing themselves. The idea of thermal piloting is to correlate sensor measurements with the expected temperature values at their locations. It can be treated as a classification problem, in which the feature vector is formed by the temperature values at each sensor location across different cooling configurations. The training set is provided by Computational Fluid Dynamic (CFD) simulations. Since classical supervised learning techniques fail to account for the bijective relation between sensor indices and locations, we formulated an extra step based on the Maximum Weighted Bi-partite Matching (MWBM) problem. Experimental results show that the proposed methods can achieve an average localization error of 0.64 meters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".