ENHANCED ROUTING METRIC FOR LOAD-BALANCING IN WIRELESS MESH NETWORKS
Bibliographic record
Abstract
Wireless mesh networks (WMNs) have been drawing significant attention in recent years due to their flexibility in providing extensive wireless backbone. WMNs typically consist of mesh routers and mesh clients with each node operating not only as a host but also as a router. Due to the traffic patterns in WMNs, load-balancing becomes an important issue and may degrade the performance of the entire network. This paper proposes a routing metric known as Weighted Cumulative Expected Transmission Time with Load-Balancing (WCETT-LB) for wireless mesh networks. WCETTT-LB enhances the basic Weighted Cumulative Expected Transmission Time (WCETT) by incorporating load-balancing into the routing metric. Unlike existing schemes, WCETT-LB implements load-balancing at mesh routers. WCETT-LB provides a congestion-aware routing and traffic splitting mechanism to achieve global load-balancing in the network. By conducting an extensive simulation experiments, the result shows that WCETT-LB outperforms the existing routing metrics in load-balancing in terms of achieving high packet delivery ratio, low average end-to-end delay and low average congestion level in wireless mesh networks. The qualitative and quantitative analysis also show the significance of the proposed scheme.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".