SlideOR: Online Opportunistic Network Coding in Wireless Mesh Networks
Why this work is in the frame
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Bibliographic record
Abstract
Opportunistic routing significantly increases unicast throughput in wireless mesh networks by effectively utilizing the wireless broadcast medium. With network coding, opportunistic routing can be implemented in a simple and practical way without resorting to a complicated scheduling protocol. Traditionally, due to the constraints of computational complexity, a protocol utilizing network coding needs to partition the data into multiple segments and encode only packets in the same segment. However, it is extremely challenging to decide the optimal time to move to the transmissions of the next segment, and existing designs all resort to different heuristic ideas that might harm network throughput. To address this problem, we propose <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlideOR</i> , a new protocol to encode source packets in overlapping sliding windows such that coded packets from one window position may be useful towards decoding the source packets inside another window position. Through extensive simulations, we show that <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SlideOR</i> outperforms the existing solutions and is amenable to much simpler implementation than solutions with complicated scheduling among multiple segments.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 it