Bibliographic record
Abstract
Maintaining low power consumption is critical in wireless ad hoc and sensor networks. With packet transmissions and retransmissions consuming much of the energy resources in wireless networks, it becomes important to minimize the number of transmissions associated with the end-to-end delivery of packets. Power-aware routing algorithms must balance the advantages and disadvantages of selecting to forward packets over shorter high-quality links against selecting longer and less reliable links. This paper proposes a new power-aware geographic routing technique that combines geographic greedy routing with probabilistic random walks to recover from local minima (i.e., cases when the forwarding node is not aware of any neighboring node providing "greedy" progress towards the destination). Building upon previous power-aware protocols without recovery mechanisms, our protocol uses simple distance metrics that combine information about the individual reception rates between node pairs and the relative forward progress candidate nodes provide towards the target destination. The combined metrics are used to make greedy choices (when at least one node provides progress) and probabilistic choices (when the packet recovers from a local minimum). Using simulations we show that power-aware routing significantly reduces the energy consumption in the network, and our probabilistic recovery mechanism can significantly increase the delivery rates with only a small decrease in energy efficiency.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".