Cross-layer cluster-based data dissemination for failure detection in MANETs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Node failures may be frequent in MANETs, but there can be many different causes for those failures. Nodes may lose power, crash, or simply move out of range of other nodes in the network. Identifying the root cause is complicated by a lack of fixed monitoring and analysis infrastructure. Past research has focused on monitoring using either ping, heartbeat, or gossip-based approaches, which can incur significant network wide overhead. This paper proposes a novel k-hop cluster based data dissemination scheme that can piggyback on routing messages for more efficient detection of failures including node disconnection. In this scheme, nodes forward their neighbour-hood observations to a per-cluster failure detector based on the observed spanning tree. Simulations show that detecting disconnected nodes using a cross-layer implementation of the data dissemination scheme is more efficient while an application layer implementation is faster. This effect is more pronounced in sparse networks.
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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 | 0.001 | 0.001 |
| 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