Secure AODV Routing Strategies in Smart Cities for Vehicular Communication
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANETs) have become prominent in the past few years for the transportation sector.Vehicular mobility poses a significant challenge for establishing private communications in VANETs.The classical Ad hoc On-Demand Distance Vector (AODV) routing protocol used in VANET assumes that all nodes are non-malicious.To address this matter, this paper proposes making AODV routing protocols more secure by using a privacy scheme in AODV for vehicle-to-vehicle communication.The AODV privacy scheme tries to keep the automobile network connected reliably and stably during communication with the secured transmission of messages and minimize the risk of unauthorized access to sensitive information from eavesdropper attacks.The proposed privacy secure AODV routing named PSAODV used pseudonym changes in vehicle communication to hide the target vehicle's location.A VANET simulator based on OMNET++ and SUMO are used for evaluating the PSAODV routing protocol.A simulation study was conducted that compared the PSAODV with SE-AOMDV, ECC-AODV, and AODV in fundamentals of efficiency and confidentiality.The analysis results showed that PSAODV routing demonstrates routing efficiency with privacy by diminishing the effect of eavesdropping of vehicles information based on various scenarios in urban cities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".