New SAPFR protocol for WSNs with sensitive clusters
Bibliographic record
Abstract
In WSNs, accidents like traffic congestion and sensor node running out of energy may occur frequently. These accidents are usually geographically localized, resulting in some groups of nodes unusable temporarily or even forever, which are called sensitive clusters in this paper. Although the data collection methods have been intensively studied, how to dynamically optimize the routing to bypass the sensitive clusters is an interesting and open issue. A new distributed, location-based routing protocol, named sensitive artificial potential field routing (SAPFR), is proposed to deliver packets efficiently while bypassing the sensitive clusters adaptively. SAPFR can build the multihop route from a sensor node to the sink with high energy efficiency and power consumption balancing. In particular, SAPFR is highly adaptive to bypass the dynamic sensitive clusters. Simulation results show that the obtained routes proactively bypass the sensitive clusters and the transmission efficiency is improved as well. SAPFR provides a high routing success rate in the WSNs even with a large proportion of sensitive sensor nodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".