Enhancing IEEE 802.11 Random Backoff in Selfish Environments
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Bibliographic record
Abstract
Wireless access protocols currently deployed in mobile ad hoc networks use distributed contention resolution mechanisms for sharing the wireless channel. In such an environment, selfish hosts that fail to adhere to the medium access control (MAC) protocol may obtain an unfair share of the channel bandwidth at the expense of performance degradation of well-behaved hosts. We present a novel access method, called predictable random backoff (PRB), that is capable of mitigating the misbehavior of selfish hosts, particularly hosts that deliberately do not respect the random deferment of the transmission of their packets. PRB is based on minor modifications of the IEEE 802.11 binary exponential backoff (BEB) and forces each node to generate a predictable backoff interval. The key idea is to adjust, in a predictable manner, the lower bound of the contention window to enhance the per-station fairness in selfish environments. Hosts that do not follow the operation of PRB are therefore easily detected and isolated. We present an accurate analytical model to compute the system throughput using a 3-D Markov chain. We evaluate the performance of PRB under the normal case and in the presence of selfish hosts. Our results show that PRB and BEB similarly perform in the former case. Selfish hosts, however, achieve substantially higher throughput than well-behaved hosts under BEB. PRB, on the other hand, can effectively enhance IEEE 802.11 BEB by mitigating the impacts of these MAC selfish misbehaviors and guarantee a fair share of the wireless channel for well-behaved hosts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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