Semi-Beaconless Power and Cost Efficient Georouting with Guaranteed Delivery using Variable Transmission Radii for Wireless Sensor Networks
Bibliographic record
Abstract
We assume that sensors are aware of the positions of neighbors within a specific knowledge range, which is smaller than their maximum transmission range. We propose the GRoVar protocol (Geographic Routing with Variable transmission range) that extends the well-known GFG protocol [2], a combination of greedy forwarding and recovery, by applying variable transmission range and beaconless routing techniques. In our protocol, each node locally selects the best forwarding neighbor within its knowledge range, using power or other metric. If no neighbor is closer to the destination, the current node may incrementally increase its transmission range to find suitable candidates for the next hop, with the help of request messages. Face routing is applied when no forwarding neighbor is found after sending requests with the maximum transmission range. It also applies range increases until recovery is possible. We investigated different possibilities: a linear increase, doubling the range in each iteration or directly jumping to the maximum range possible. The comparison of energy usage for data transfer from source to sink shows that a significant saving in energy consumption can be achieved using the proposed method.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 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".