Associativity-based adaptive weighted clustering for large-scale mobile ad hoc networks
Why this work is in the frame
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Bibliographic record
Abstract
We propose and analyze a distributed adaptive clustering algorithm for large-scale ad hoc networks. The algorithm calculates a stability weight for each node based on its power and spatial and temporal stability. The nodes having the highest stability weight get elected as clusterheads. Frequent clusterhead change is minimized by cautious invocation of re-clustering. Frequency of control messages is adapted to the mobility pattern of clustermembers. The algorithm balances load across clusterheads and adapts hop-distance to the network density by keeping the cluster size around an optimum value. It reduces the overall communication complexity by minimizing the control traffic overhead and by eliminating the ripple effect of re-clustering. We analyze effectiveness of the algorithm through simulations.
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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 | 0.000 |
| 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 it