WSN16-1: A Weighted Clustering Algorithm Using Local Cluster-heads Election for QoS in MANETs
Bibliographic record
Abstract
In this paper, we propose a new distributed weighted clustering algorithm with local cluster-heads election (WCA-L) based on an on-demand distributed clustering algorithm for multi-hop packet radio networks. The multi-hop packet radio networks, also named mobile ad hoc networks (MANETs) have a dynamic topology due to the mobility of their nodes. This mobility makes the challenge harder for routing protocol. Moreover, the well known routing protocols are not able to offer QoS that is why we need to manage MANETs. Such task can be done using clustering techniques but the association and dissociation of nodes to and from clusters perturb the stability of the network topology, and hence reconfiguration of the system is often unavoidable. However, it is vital to keep the topology stable as long as possible. The nodes called cluster-heads form a dominant set and determine the topology and its stability. Simulation experiments are conducted to evaluate the stability of the dominant set in terms of updates of the dominant set, handovers of a node between two clusters and the QoS in terms of packet delivery rate and overhead provided by both our algorithm (WCA-L) and the weighted clustering algorithm (WCA), which does not consider prediction and local election. Results show that our algorithm performs better than WCA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".