Preventing a DoS threat in vehicular ad-hoc networks using adaptive group beaconing
Bibliographic record
Abstract
In vehicular ad-hoc networks (VANETs), vehicles exchange position information with their neighbors through periodic beacon messages. In dense areas, a massive number of messages may saturate the network and prevent nodes from acquiring the proper resources to exchange their messages. As the vehicle density increases, the network will be at risk of initiating a denial-of-service attack (DoS) of its own, which will prevent the utilization of beacon messages as intended and affect other critical applications. To resolve such potential threats, adaptive beaconing can adjust a vehicle's beacon rate based on detected signal congestion and traffic conditions. In VANETs, high mobility and rapid change in vehicles' locations present challenges in detecting traffic congestion and providing secure and accurate position information. In this paper, we present a novel approach to sending beacon messages based on neighborhood awareness and road traffic density. The proposed solution also allows authority managements to reduce the number of periodic messages to provide quality of service (QoS) for specific applications and vehicles. Simulation showed network performance improvement and a reduced number of beacon messages in dense areas without compromising the level of awareness that will support QoS provisioning and allow bandwidth availability for critical applications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".