A Cooperative Detection Model Based on Artificial Neural Network for VANET QoS-OLSR Protocol
Bibliographic record
Abstract
In this paper, we address the problem of detecting misbehaving vehicles in Vehicular Ad-Hoc Network using VANET QoS-OLSR, Quality of Service-Optimized Link State Routing protocol. VANET QoS-OLSR is a clustering protocol that is able to increase the stability of the network while maintaining the QoS requirements. However, in this protocol, vehicles can misbehave either by under-speeding or over- speeding the road speed limits after clusters are formed. Such misbehavior leads to a widely disconnected network, which raises the need for a detection mechanism. The majority of the existing detection mechanisms are non-cooperative in the sense that they are based on unilateral judgments, which may be untrustworthy. Others employ cooperative detection scheme with evidence-based aggregation techniques such as the Dempster-Shafer (DS) which suffers from the (1) instability when observations come from dependent sources and (2) absence of learning mechanism. To overcome these limitations, we propose a cooperative method using Artificial Neural Network (ANN), which is able to (1) aggregate judgments and prevent the unilateral decisions, and (2) benefit from the previous detection experience by continuous learning. Simulation results show that our model improves the detection probability and reduces the false alarms rate.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".