Traffic Priority Based Adaptive and Responsive Congestion and Rate Control for Wireless Mesh Networks
Bibliographic record
Abstract
Congestion Control in Wireless Mesh Networks (WMNs) has been a widely studied context oriented utilization maximization problem. The traffic in WMNs has a wide range of variations (i.e. bandwidth, jitter, delay jitter sensitive applications) due to its use as the backbone network for accessing Internet. Eventually, traffic variation contributes to channel saturation and may bring up congestion due to contention caused by concurrent transmission, buffer overflows and time varying wireless channel condition. In this paper, we propose a distributed congestion control scheme for wireless mesh networks to ensure higher network throughput while avoiding congestion and maintaining inter-flow fairness where real time and non-real time traffic coexists. Our proposed technique handles congestion by restricting the available transmission rate of downstream nodes among the upstream nodes according to their flow demands based on three basic parameters: packet arrival rate, service rate and buffer occupancy. We also introduce prioritized queues in each node to treat real time and non-real time traffic differently. Therefore, congestion degree calculation, rate allocation and prioritizing traffic ensures higher network throughput and guaranteed delivery of real time traffic. Experiments conducted on ns-2 simulations demonstrate that our proposed algorithm can achieve significant improvements in both overall network throughput and inter-flow fairness for both non real time and delay bound traffic.
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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 | 0.004 |
| 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 | 0.000 | 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".