Optimal Design of LEMoNet for Environmental Monitoring of Data Centers
Bibliographic record
Abstract
Half of the electrical power in today’s data centers (DCs) is consumed by cooling units, but much is wasted due to over-cooled or under-utilized servers. Environmental monitoring using Data Center Wireless Sensor Networks (DCWSNs) plays a central role in detecting and mitigating hotspots or over-cooling conditions. These networks often include thousands of sensors and require a reliable and power-efficient communication protocol. The Low Energy Monitoring Network (LEMoNet) is a two-tier DCWSN protocol that features Bluetooth Low Energy (BLE) for sensor communication in the first tier and leverages multi-gateway packet receptions in its second tier. This research studies the performance of LEMoNet in different DC topologies through the development of an analytical model. The model solves a multi-objective optimization problem to calculate two design parameters. The first parameter is the optimal number of gateways and an optimal location for each one. The second parameter is an optimal transition (TX) power for each sensor, subject to the application requirements. Evaluation results show that the optimal design includes one gateway on every other row. Also, the model reduces 65% of the sensors’ power consumption via TX power optimization while Packet Reception Rate (PRR) is kept up at the level of 95%.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".