Toward a Practical Energy Conservation Mechanism With Assistance of Resourceful Mules
Bibliographic record
Abstract
As wireless sensor networks (WSNs) gradually move from specialized fields such as military and industry toward domains with general purposes, more and more sensors locate around our living areas. The reality that various wireless devices coexist in new circumstances encourages us to come up with new ideas to solve the extremely energy-constrained problem in WSNs. In this paper, we propose energy conservation with assistance of resourceful mules (ECARM), a mechanism that opportunistically utilizes resourceful mules (RMs) such as specifically designed powerful sensors or ubiquitously used laptops, tablet PCs, and smart phones to act as assistants and save energy for WSNs. We verify ECARM through extensive simulations written on the OMNET$\boldsymbol{++}$ platform. Single RM simulation shows that 43% sensors in an RM's communication range enjoy power reduction by decreasing their wake-up time to 16% at most. Multiple RM simulations illustrate that 86% sensors in the simulated network benefit from 14 RMs, and wake-up time of 56% sensors decrease to 50% below. We emphasize that ECARM can also be applied in duty-cycled WSNs that adopt schemes such as ContikiMAC and X-MAC. Simulation results demonstrate that the duty-cycling ratio of ContikiMAC is further decreased by at least 20.9% after the ECARM application.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".