Smart Protocol for Communication in Mobile Ad Hoc Network of Vehicles
Bibliographic record
Abstract
In this paper we present a smart protocol for intervehicle communication based on mobile ad hoc networks. The protocol is capable of handling both broadcast as well as unicast messages for a particular node. The protocol implements controlled flooding in an efficient manner to reduce unnecessary communication overheads. The protocol avoids forming clusters and clusterheads by using the concept of selective forwarding. Thus it gets the advantages related to clusters without actually forming one itself. It effectively deals with blind flooding by introducing a comprehensive retransmission time delay in the nodes. This retransmission delay favors the nodes in the direction of the destination and restricts other nodes from transmitting the same message again. The proposed protocol avoids the use of routing tables which have a short life in mobile networks needing frequent updates. Instead the protocol relies on directing the messages to geographic locations which are forwarded by any available intermediary nodes. The protocol also provides techniques for handling network fragmentation which is a frequent problem for the vehicular networks. The protocol is implemented using NS2 and simulation results are compared with AODV.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".