On the fundamental capacity and lifetime limits of energy-constrained wireless sensor networks
Bibliographic record
Abstract
Energy constraints on sensor nodes pose significant challenges towards extending operational lifetimes and sustainable capacities of wireless sensor networks. We seek to answer two fundamental questions with respect to energy-constrained sensor networks. First, what is the operational lifetime of a particular wireless sensor network under the control of optimal power management schemes? With an adequate definition of operational lifetimes, our asymptotic analysis shows that, with fixed node densities, operational lifetime of sensor networks decreases in the order of 1/n as the number of initially deployed nodes n grows. Second, what is the impact of constrained energy levels on the maximum sustainable throughput in sensor networks? Even with renewable energy sources on each of the sensors (e.g., solar energy sources), our analysis concludes that the maximum sustainable throughput in energy-constrained sensor networks scales worse than the capacity based on interference among concurrent transmissions as long as the physical network size grows with n in the order greater than log n. In this case, when the number of nodes is sufficiently high, the energy-constrained network capacity dominates.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".