Modeling and characterization of transmission energy consumption in Machine-to-Machine networks
Bibliographic record
Abstract
In future, a massive number of devices are expected to communicate for pervasive monitoring and measurement, industrial automation, and home/building energy management. Nevertheless, such Machine-to-Machine (M2M) communications are prone to failure due to depletion of machines energy if the communication system is not designed properly. A key step in building energy-efficient protocols for large-scale M2M communications is to assess, model or characterize a network energy consumption profile. To meet this need, we develop a theoretical and numerical framework to evaluate the cumulative distribution function (CDF) of the total energy consumption by fully exploiting the properties of stochastic geometry. Unlike the other existing approaches, we model the transmission energy as a function of transmission power, packet size, and link affordable capacity that is a logarithmic function of experienced Signal to Interference plus Noise Ratio (SINR). Since it is very difficult, if not impossible, to derive a closed-form expression for the CDF, we derive numerically computable first- and second-order moments of energy consumption. Applying these moments we then propose Log-normal and Log-logistic distributions to approximate the CDF. Our simulation results show that Log-logistic almost precisely approximates the exact CDF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".