On the application of pipelining in aggregation convergecast scheduling
Bibliographic record
Abstract
We consider the problem of scheduling wireless transmissions in a sensor network to perform aggregation convergecast. In contrast to studies that attempt to shorten the schedule for completing a single data collection cycle, we aim to increase the frequency at which updates are collected (higher throughput). To achieve higher throughput, we use a form of pipelined concurrent collection of multiple data snapshots through the network. To attain high performance pipelining, we “expand” the time in which precedence constraints need to be satisfied such that they span over multiple schedule cycles. Our approach involves the unconventional approach of constructing the schedule before finalizing the exact form of the precedence constraints, i.e., before determining the data aggregation tree, which in turn requires that the schedule construction phase guarantees that every node can reach the sink. We compare our results using pipelining against a previously proposed algorithm that also uses pipelining, as well as against an algorithm that, although lacking pipelining, exhibits the ability to produce very short schedules. The results confirm the potential to achieve a substantial throughput increase at the cost of increased latency.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".