Priority-based congestion control in multi-path and multi-hop wireless sensor networks
Bibliographic record
Abstract
In wireless sensor networks (WSNs), congestion will cause packet loss which in turn wastes energy and reduces the lifetime of WSNs, and therefore congestion in WSNs must be controlled or avoided in either a fairness or a weighted-fairness way. It is very important to achieve weighted fairness for many WSN applications, and this problem becomes more complicated when the data flow is forwarded to multiple routing paths. In this paper we propose a joint priority-based algorithm (JPA) that eliminates congestion and achieves weighted fairness in multi-path and multi-hop wireless sensor networks. Weighted fairness is achieved when the source node with high source priority (SP) sends more packets than the one with low SP in response to congestion. JPA defines a new variable, joint priority (JP) for each node and link, as the expected value of SP. The JP of a node or link indicates the arithmetic means of SP of source nodes whose data flow passes through that particular node or link, and the sending rate of each node is adjusted based on the value of JP when congestion occurs. The JPA algorithm is simulated and evaluated in different scenarios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".