QoS Support in Delay Tolerant Vehicular Ad Hoc Networks
Bibliographic record
Abstract
In this paper, we propose a new intersection-based geographical routing protocol, called delay tolerant routing protocol (DTRP) that adapts to the changes in the local topology within city environments. DTRP is based on an effective selection of road intersections through which a packet must pass to reach the gateway to the Internet. The selection, in such delay tolerant VANETs, is made in a way that maximizes the connectivity probability of the route between mobile nodes and the gateway while maintaining a threshold for the end-to-end delay and the hop count within the network. To achieve this, we formulate the QoS routing problem mathematically as a constrained optimization problem. Specifically, analytical expressions for the connectivity probability as well as the delay and hop count of a route in a two-way road scenario are derived. Then, we propose a genetic algorithm to solve the optimization problem. Numerical and simulation results show that the proposed approach gives optimal or near-optimal solutions and improves significantly the VANETs performance when compared with several prominent routing protocols, such as GPSR, GPCR and OLSR.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| 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".