Experiences using Gateway-Enforced Rate-Limiting Techniques in Wireless Mesh Networks
Bibliographic record
Abstract
Gateway nodes in a wireless mesh network (WMN) bridge traffic between the mesh nodes and the public Internet. This makes them a suitable aggregation point for policy enforcement or other traffic-shaping responsibilities that may be required to support a scalable, functional mesh network. In this paper we evaluate two gateway-enforced rate-limiting mechanisms so as to avoid congestion and support network-level fairness: automated queue management (AQM) techniques that have previously been widely studied in the context of wired networks, and our gateway rate control (GRC) mechanism. We evaluate the performance of these two techniques through simulations of an 802.11-based multihop mesh network. Our experiments show that the conventional use of AQM techniques fails to provide effective congestion control as these mesh networks exhibit different congestion characteristics than wired networks. Specifically, in a wired network, packet losses under congestion occur at the router queue feeding the bottleneck link. By contrast, in a WMN, many such geographically dispersed points of contention may exist due to asymmetric views of the channel state between different mesh routers. As such, gateway rate-limiting techniques like AQM are ineffective as the gateway queue is not the only bottleneck. Our GRC protocol takes a different approach by rate limiting each active flow to its fair share, thus preserving enough capacity to allow the disadvantaged flows to obtain their fair share of the network throughput. The GRC technique can be further extended to provide quality of service (QoS) guarantees or enforce different notions of fairness.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".