An Efficient Traffic-Load and Link-Interference Aware Routing Metric for Multi Radio Multi Channel Wireless Mesh Networks Based on Link’s Effective Capacity Estimation
Bibliographic record
Abstract
Routing metrics proposed for Wireless Mesh Networks (WMNs) has various concerns like hop count, packet transmission delay, power consumption, congestion control, load balance and message collision. The routing metric of expected effective capacity (EEC) proposed in this paper guarantees to a select a path providing maximum throughput and minimum delay. A forwarding link constituting routing path is characterized by its quality, capacity, traffic demand and the degree of intervention experienced due to inter-flow and intra-flow interference. Thus the bandwidth actually attainable on a link for a flow is affected by those link properties. Our proposed metric computes the attainable bandwidth for a flow over a path which actually reflects congestion, node delay and traffic pressure on the desired path. Experiments conducted on ns-2 simulations demonstrate that our proposed routing metric can achieve significant improvements in overall network throughput, minimize end-to-end delay and able to distribute network load.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".