Cross-layer flow control in lightly-loaded multi-hop ad hoc networks
Bibliographic record
Abstract
The throughput in multi-hop ad hoc networks (MANETs) is highly dependent on the sending rate and the route length from the source node to the destination. Sending packets at the optimal rate for a given route length maximizes throughput in the network, whereas slightly increasing the sending rate over the optimal value may decrease throughput by up to 55%. This paper presents a novel cross-layer technique for flow control in lightly-loaded MANETs. The technique allows applications to send packets at the rate that maximizes throughput for a given route length. To achieve this, the routing layer notifies interested applications about routing changes, and the applications adaptively modify their sending rates based on the new route length to the destination. In static and mobile networks, this technique outperforms UDP-based flows with a fixed sending rate and doubles the throughput of TCP for networks with up to 2 concurrent flows.
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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".