OPPORTUNISTIC CONTENT DISSEMINATION IN INTERMITTENT MULTI-HOP DEVICE-TO-DEVICE NETWORK
Bibliographic record
Abstract
Multi-hop device-to-device (D2D) transmission can offload traffic from cellular networks.The nodes in a multi-hop D2D network cannot constantly maintain connectivity for the entire network.A hybrid network that includes integrated cellular links and intermittent multi-hop D2D links is a novel architecture.To analyse content dissemination from one publisher to multiple subscribers in this new network scenario, a propagation model is established.First, nodes must be able to receive the requested content within the timeout and successful delivery probability (SDP) constraints.The transmission probability from end to end is analysed.The relay set search algorithm (RSSA), a Dijkstra-like algorithm, is proposed to determine the node set that can receive the requested content.Based on the RSSA, the problem is to find the minimum agent set required to propagate messages to all subscribers under the timeout and SDP constraints is analysed.A solution of the set cover problem (SCP) is employed to solve this issue.Our solution is evaluated based on realistic trace data and the results verify that our solution can achieve high SDP, low delay, and low computation complexity.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".