Connection-Based Scheduling for Supporting Real-Time Traffic in Wireless Mesh Networks
Bibliographic record
Abstract
This paper studies real-time traffic scheduling in wireless mesh networks (WMNs). Optimum packet transmission scheduling providing strict latency guarantee for realtime traffic while efficiently utilizing the network resources in a WMN is difficult, and one of the main challenges is to coordinate temporal operations of the mesh access points (APs). In this paper a connection-based scheduling scheme is proposed. Scheduling decisions for connections with a larger number of hops are made first, and that for connections with a fewer number of hops are done by using the remaining resources. When scheduling packet transmissions for each connection, the transmitting time of the AP with the highest traffic load along the route of the connection is determined first. At each hop, the transmitting time of a packet is determined to minimize the latency to the upstream hop or from the downstream hop while keeping the total amount of required AP resources small. A connection-based optimization problem is formulated and solved with an objective to minimize the total amount of required AP resources, subject to the latency requirement of the connection. Numerical results show that the proposed scheduling scheme achieves close-to-optimum performance at both the connection and packet levels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".