An Enhanced MPR-Based Solution for Flooding of Broadcast Messages in OLSR Wireless ad hoc Networks
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Bibliographic record
Abstract
In an Optimized Link State Routing (OLSR)-based mobile wireless network, optimizing the flooding of broadcast messages is a challenging task due to node's mobility and bandwidth resource consumption. To complement existing solutions to this problem, the Multi-Point Relays (MPR) selection has recently been advocated as a promising technique that has an additional feature of reducing the number of redundant re-transmission occurring in the network. This paper continuous on the investigation of an existing MPR-based solution, arguing that by considering a cost factor as an additional decision parameter in selecting the MPR nodes, the enhanced MPR selection algorithm leads to less packet loss in the network. Simulation experiments are presented to validate the stated goal, using the average packet loss ratio as the performance metric.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 it