A Pipelined-forwarding, Routing-integrated and effectively-Identifying MAC for large-scale WSN
Bibliographic record
Abstract
This paper presents the design of a duty-cycling MAC, called PRI-MAC (Pipelined-forwarding, Routing-integrated, and effectively-Identifying MAC), for large-scale wireless sensor networks. PRI-MAC divides the whole network into grades around the sink node. The higher grade a node is in, logically the further away it is from the sink which is in the lowest grade. Staggered sleep-wakeup schedules are established between any two adjacent grades such that data can be forwarded in a pipelined fashion, largely reducing the packet delivery latency to meet the real-time transmission requirement. Meanwhile, the routing function is seamlessly integrated into PRI-MAC, which reduces the protocol overhead and increases the network scalability. Furthermore, each node utilizes a randomly-generated integer as its identifier only when it is involved in a data transmission, instead of allocating a unique address for each sensor node. The performance of PRI-MAC is evaluated in comparison with PW-MAC by OPNET, in terms of the packet delivery latency, energy efficiency, and throughput.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".