Receiver‐oriented load‐balancing and reliable routing in wireless sensor networks
Bibliographic record
Abstract
Abstract Routing protocols in wireless sensor networks (WSNs) typically employ a transmitter‐oriented approach in which the next hop node is selected based on neighbor or network information. This approach incurs a large overhead when the accurate neighbor information is needed for efficient and reliable routing. In this paper, a novel receiver‐oriented load‐balancing and reliable routing (RLRR) protocol is proposed. In RLRR, an intermediate node solicits next hop candidates, each of which is to respond with its own backoff time dubbed a temporal gradient (TG). In this way, the next hop is selected without any central coordination on a packet‐by‐packet basis. Thus, each node needs not maintain any neighbor information. The remaining energy level used to determine the TG is always accurate and up‐to‐date. Furthermore, neighbor nodes whose hop count is less than the soliciting node participate in the next‐hop selection process with loop‐free operation guarantee. Comprehensive simulations are carried out to show that RLRR achieves relatively longer network lifetime and higher reliability than other existing schemes. Copyright © 2007 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".