Self-optimizing cooperative caching in autonomic Wireless Mesh Networks
Bibliographic record
Abstract
Wireless mesh networks (WMNs) have become a network of choice to provide broadband wireless Internet connectivity where wired infrastructure is uneconomical or impractical to deploy. Their support for inexpensive broadband Internet services has made them even more attractive and increased users' demand. Even with their abundant computational resources in the form of mesh routers (MRs), WMNs suffer from bottleneck effect at the Internet Gateways (IGWs) due to the nature of the traffic pattern which is often towards or away from the Internet. Inspired by the autonomic networking paradigm, we propose a self-optimizing cooperative caching solution for wireless mesh networks. The solution gradually improves data accessibility by alleviating the IGW(s) bottleneck effect using cross layer design optimizations. Our simulation results show that the proposed solution has good performance in terms of reducing gateway load and improving the packet delivery ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".