A new routing metric for real-time applications in smart cities
Bibliographic record
Abstract
Many interconnected smart devices manage and control different areas in cities using information and communication technologies. Such networked devices, exchanging information through real-time applications, are characterized by their high mobility, which require developing optimized routing metrics. In the literature, several solutions are proposed to solve such an information routing problem. Most of them use many network parameters in routing metrics calculation, such as the node position, the node speed, the link quality and the network density. However, the existing routing solutions may require combining simultaneously the end to end delay, the packet loss and the distance. This adds more efficiency in data transmission since the realtime applications require no packet loss and less delay. In this paper, we consider these parameters to propose a mathematical modeling of new multicriteria routing metric. Subsequently, we solve it with three different methods: exact method, A star method and A star with obstacles method. The simulation results show the efficiency of the A star method in terms of response time and iteration number.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".