MAZACORNET: Mobility aware zone based ant colony optimization routing for VANET
Bibliographic record
Abstract
Vehicular Ad hoc Networks (VANET) exhibit highly dynamic behavior with high mobility and random network topologies. The performance of Transmission Control Protocols (TCP) in such wireless ad hoc networks is plagued by a number of problems: frequent link failures, scalability, multi-hop data transmission and data loss. In this work, we make use of the vehicle's movement pattern, vehicle density, vehicle velocity and vehicle fading conditions to develop a hybrid, multi-path ant colony based routing algorithm, Mobility Aware Zone based Ant Colony Optimization Routing for VANET (MAZACORNET). that exhibits locality and scalability. We use ACO to find multiple routes between nodes in the network to aid in link failures. To achieve scalability we partition the network into multiple zones. We use proactive approach to find a route within a zone and reactive approach to find routes between zones using the local information stored in each zone thereby trying to reduce broadcasting and congestion. Our proposed algorithm makes effective use of the network bandwidth, is scalable and is robust to link failures. The results show that the algorithm works well for dense networks. The algorithm produces better delivery ratio and is scalable for zones beyond four. When compared to other existing VANET algorithms, the hybrid algorithm proved to be more efficient in terms of packet delivery ratio and end to end delay. To our knowledge this is the first ant based routing algorithm for VANET that uses the concept of zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".