Selfishness detection for backoff algorithms in wireless networks
Bibliographic record
Abstract
Selfish nodes in an 802.11 network can gain unfair access to the wireless medium by modifying the backoff protocol, for example by choosing smaller backoff values more often than would be dictated by pure chance. Detecting this kind of misbehavior is far from obvious as it is not always possible to deduce the backoff values used by a node. We propose a new backoff scheme called XVBEB in which there are only two backoff values: 0 and CW. We describe how to deduce the backoff values used by an observed node using XVBEB based on observations of transmissions by nodes in the network and the collision timeline. Given a set of backoff values used by a XVBEB node, we describe how to conclude with a specified level of certainty whether the node is indeed adhering to the protocol. We also show that it would take much more effort to detect selfishness for 802.11 nodes following the standard backoff procedure within a comparable misbehaving framework.
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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 | 0.007 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".