Queue‐based congestion detection and multistage rate control in event‐driven wireless sensor networks
Bibliographic record
Abstract
ABSTRACT Protocols for sensor networks have traditionally been designed using the best effort delivery model. However, there are many specific applications that need reliable transmissions. In event‐driven wireless sensor networks, the occurrence of an event may generate a large amount of data in a very short time. Among them, some critical urgent information needs to be transmitted reliably in a timely manner. In this scenario, congestion is inevitable because of the constraints in available resources. How to control the congestion is very important for the reliable transmission of urgent information. To address this problem, we propose a queue‐based congestion detection and a multistage rate control mechanism. In our proposed mechanism, not only the current queue length but also the queue fluctuation are adopted as indications of congestion. Each sensor node evaluates its congestion level locally and determines its congestion state with a state machine. We design a multistage rate adjustment mechanism for nodes to adjust their rates depending on their congestion states. We also distinguish high‐priority critical traffic from low‐priority non‐critical traffic. Extensive simulation results confirm the superior performance of our proposed protocol with respect to throughput, loss probability, and delay.Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".