Rate distance and MST-based multiratecasting in wireless sensor networks
Bibliographic record
Abstract
In the multiratecasting problem in wireless sensor networks, source sensor should report to multiple destinations at different rates for each of them. Two existing localized solutions have drawbacks. One selects best neighbour serving the highest rated destination and is suboptimal when rates are close to each other. The other has high computational time for testing many subsets of neighbours. We present two new localized algorithms. MST-based multiratecast routing protocol (MSTRC) examines only one set partition of destinations at each forwarding step. A message split occurs when the locally-built minimum spanning tree (MST) over the current node and the set of destinations has multiple edges originated at the current node. Destinations spanned by each of these edges are grouped together, and for each of these subsets the best neighbor is selected as the next hop. In rate-over-distance first (RoDiF) algorithm, we repeatedly select neighbour with maximal sum of rate · (distance reduction) toward destinations with progress. We also add a novel face recovery mechanism to deal with void areas, when no neighbor provides positive progress toward destinations. It constructs MST of current node and destinations without progress via neighbors, and, for each set partition of destinations corresponding to an edge e in MST, traverses (with maximal rate among covered destinations) face containing e until a node closer to one of these destinations is found, to allow for greedy continuation, while the process repeats for the remaining destinations similarly. Our experimental results demonstrate that MSTRC and RoDiF are highly rate-efficient in all scenarios, and, unlike exiting solutions, are adaptive to destination rate deviations.
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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 | 0.002 |
| Open science | 0.002 | 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".