A Quality of Service Driven Approach for Clustering in Mobile Ad hoc Networks Based on Metrics Adaptation: Looking Beyond Clustering
Bibliographic record
Abstract
Recently, research topics are focusing on clustering approaches for Ad hoc networks due to their effectiveness in building a virtual backbone formed by a set of suitableclusterheads (CH) to guarantee the communications acrossclusters. In this paper, we propose a clustering approach to elect suitable nodes’ representatives and to store minimum topology information by reducing the propagation of routing information which facilitates the spatial reuse of resource and increase the system capacity. The clusters must adapt dynamically to the environment changes, we also propose a distributed maintenance procedure that allows managing nodes’ adhesion, nodes’ handoff and CHs’ re-election. Based on our analytical model used to estimate the quality of service (QoS) parameters, we implement an admission control algorithm to determine the number of members inside a cluster that can be accommodated while satisfying the constraints imposed by the current applications. This might effectively drive congestion avoidance on the CH andinterclusters load-balancing to achieve better network resource utilization. The obtained results will help us to readjust the clustering algorithm metrics in order to provide better maintenance and QoS guarantees depending on the used applications. Through numerical analysis and simulations, we have studied the performance of our model and compared it with that of other existing algorithms. The results demonstrate better performance in terms of number of clusters, number of handoffs, number of transitions (state change) on CHs, QoS parameters, load balancing and scalability. We also observed how the connectivity and the stability are maximized when the number of nodes increases in presence of the mobility.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".