An Analytical Study of Low Energy Monitoring Networks for Large-Scale Data Centers
Bibliographic record
Abstract
Environmental monitoring using wireless sensors plays a key role in detecting hotspots or over-cooling conditions in data centers (DCs). However, monitoring a large enterprise or cloud DCs requires the deployment of thousands of sensors distributively with an operational time over months or years. Low Energy Monitoring Network (LEMoNet) is a two-tier Bluetooth Low Energy (BLE) based protocol for DC monitoring that leverages multi-gateway packet reception in its top tier to mitigate the unreliable BLE communication in the low tier. In this paper, we develop an analytical model to study the scalability and energy efficiency of LeMoNet in large-scale DCs. The accuracy of the model is validated through extensive event-driven simulations. Evaluation results show that LEMoNet can achieve high reliability in a network of 4800 nodes at a duty cycle of 15 sec (or equivalently, at an aggregated traffic load of 66Kbps per advertisement channel).
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".