Load-Balanced Routing in Wireless Networks: State Information Accuracy Using OLSR
Bibliographic record
Abstract
To support QoS routing, accurate state should be available and manageable. But due to bandwidth constraints, communication costs, high loss rate and the dynamic topology of wireless networks, obtaining and keeping up-to-date state information is a very complex task. A commonly used QoS metric is router queue length, used as a load metric in a number of load-balancing routing protocols. In this paper, we explore how to accurately propagate information about a router's queue length in a network that runs the optimized link state routing (OLSR) protocol. We report the quantification of state information accuracy under different traffic rates. The results show that state information is inaccurate, especially under high traffic rates. Tuning the OLSR protocol parameters has no noticeable impact on inaccuracy levels. Based on our initial analysis, we propose two additional techniques to collect queue length information as an attempt to reduce inaccuracies. We compare the different techniques against the basic OLSR, no additional improvements were observed. The results raise questions as to how load-balanced routing should be done in the face of non-negligible inaccuracies in the load metric.
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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 | 0.016 |
| 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 | 0.002 | 0.004 |
| 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".