Routing in heterogeneous vehicular networks using an adapted software defined networking approach
Bibliographic record
Abstract
Software Defined Networking (SDN) has been already used in recent literature to add flexibility and programmability to Vehicular Ad hoc Networks (VANETs). However, there are numerous open issues in implementing SDN for central control and management of VANETs. One open problem is how to adapt SDN for routing data from a source to a destination in VANETs. The main limitation of recent literature is that they don't consider the dynamic topology of VANETs when designing an SDN-enabled routing protocol. This limitation results in inefficient resource usage and congestion in VANETs. Moreover, there exist challenges on how a central SDN controller can contribute in efficient resource sharing and maintaining QoS in VANETs. In this paper, we use SDN controller to mitigate congestion of Vehicle-to-Vehicle communications while routing data on road segments. This is achieved by efficient utilization of VANET bandwidth on road segments. In contrast to recent contributions, the proposed SDN controller provides a novel routing mechanism that takes into account other existing routing paths which are already relaying data in VANET. New routing requests are addressed such that no road segment gets overloaded by multiple crossing routing paths. This approach incorporates load balancing and congestion prevention in the routing mechanism. We model the problem as a Weight Constrained Shortest Path Problem (WCSPP) and provide an efficient algorithm for a practical solution. Our simulations show QoS improvement, in terms of channel busy ratio, achieved by our proposal in comparison with recent related contributions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".