Progress and Location Based Localized Power Aware Routing for Ad Hoc and Sensor Wireless Networks
Bibliographic record
Abstract
In this article we propose several new progress based, localized, power, and cost aware algorithms for routing in ad hoc wireless networks. These algorithms attempt to minimize the total power and/or cost needed to route a message from source to destination. In localized algorithms, each node makes routing decisions solely on the basis of location of itself, its neighbors and destination. The new algorithms are based on the notion of proportional progress. A node currently holding the packet will forward it to a neighbor, closer to destination than itself, which minimizes the ratio of power and/or cost to reach that neighbor, and the progress made, measured as the reduction in distance to destination, or projection along the line to destination. First, we propose Power Progress, Iterative Power Progress, Projection Power Progress, and Iterative Projection Power Progress algorithms, where the proportional progress is defined in terms of power measure. The power metrics are then replaced by cost or power-cost metrics to define the proportional progress in terms of cost or power-cost measure, resulting in the cost and power-cost variants of the above algorithms. All the new proposed methods are implemented and their performances are compared with other competitive localized algorithms, shortest path. NC (Nearest Closer), and greedy schemes. The new power and cost localized schemes are conceptually simpler than existing schemes, and have similar or somewhat better performance in our experiments. Our localized schemes are shown to be competitive with globalized shortest weighted path based schemes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".