Efficient relay deployment for controlling connectivity in delay tolerant mobile networks
Bibliographic record
Abstract
In mobile networks, node movements may lead to a situation called network partition where an end-to-end path may never exist because the network is divided into several isolated subnetworks. Deploying stationary relays introduces new transmission opportunities leading to the improvement of network connectivity and performance. The majority of the proposed solutions concentrated on deploying the minimum number of relays in the network. However, relay deployment should also be resilient regarding relay node failures. In this paper, we show how the relay deployment problem can be modelled as a k-element connectivity problem in which multiple relay-disjoint paths are deployed to connect isolated subnetworks. To solve this problem, we present three heuristic algorithms targeting at finding the minimum number of relays to form k-element connected networks. Our experiments using synthetic and real data showed that the proposed greedy algorithm is 2 or 3 orders of magnitude faster and never worse than the other two algorithms.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".