Enhancements for clustering stability in mobile ad hoc networks
Bibliographic record
Abstract
In most MANET clustering protocols, the clusterhead nodes take on a special role in managing routing information. However, the frequent changes of the clusterheads affect the performance of all the protocols that rely on it. Due to the dynamic nature of the mobile nodes, their association and disassociation to and from clusters perturb the stability of the network and the problem becomes worse if these nodes are clusterheads. Eventually, the clustering stability in MANET would be significantly affected. To enhance the network stability, in this paper we introduce a new approach to reform the cluster, namely the Smooth and Efficient Re-Clustering (SERC) protocol. This approach is based on providing a secondary clusterhead (SCH) for each clusterhead which we call here primary clusterhead (PCH). This SCH, which is a regular member node, is identified and assigned by its PCH to be the future leader of the cluster. The SCH will be triggered to be the PCH when the former PCH can no longer be a clusterhead. Since the future clusterhead is known by the cluster members, the cluster leadership will be transferred smoothly and the cluster will be reformed immediately with no need to invoke the clustering algorithm. Also, since the member nodes are associated with the cluster with its subsequent clusterheads, the cluster looks stable to the other clusters. Hence, the smooth clusterhead transfer from a node to another aims at increasing the cluster residence time, which will sustain the stability of the network, decrease the clustering communication overhead and, minimize the time spent by each node to join or to reform a cluster.
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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 | 0.001 |
| 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".