A Localized Adaptive Strategy to Calculate the Backoff Interval in Contention-Based Vehicular Networks
Bibliographic record
Abstract
The dynamic nature of vehicular networks with their fast changing topology poses several challenges to setup communication between vehicles. Packet collisions are considered to be the main source of data loss in contention-based vehicular networks. Retransmission of collided packets is done several times until an acknowledgment of successful reception is received or the maximum number of retries is reached. The retransmission delay is drawn randomly from an interval, called the backoff interval. A good choice of the backoff interval reduces the number of collisions and the waiting periods of data packets, which increases the throughput and decreases the energy consumption. An optimal backoff interval could be obtained if global network information spread in the network in a short time. However, this is practically not achievable which motivates the efficient utilization of local information to approach the optimal performance. In this paper, we propose a localized adaptive strategy that calculates the backoff interval for unicast applications in vehicular networks. The new strategy uses fuzzy logic to adapt the backoff interval to the fast changing vehicular environment using only local information. We present four schemes of that strategy that differ in their behavior and the number of inputs. We compare the proposed schemes with other known schemes, binary exponential backoff, backoff algorithm, and an optimal scheme, in terms of throughput, fairness, and energy consumption. Results show that by proper tuning of the fuzzy parameters and rules, one of the proposed schemes outperform the other schemes, and approach the optimal results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".