Performance improvement of clustered mobile ad hoc networks using a CDMA single channel and based on admission control approach
Bibliographic record
Abstract
Abstract:- Clustering has been proven to be a promising approach for mimicking the operation of the fixed infrastructure and managing the resources in multi-hop networks. In this paper, we propose an Efficient Clustering Algorithm (ECA) in Mobile Ad hoc Networks based on the quality of service’s (QoS) parameters (cluster throughput/delay, packet loss rate). The goals are yielding low number of clusters, maintaining stable clusters, and minimizing the number of invocations for the algorithm. The performance changes greatly for small and large clusters and depends strongly on the formation and maintenance procedures of clusters which should operate with minimum overhead, allowing mobile nodes to join and leave without perturbing the membership of the cluster and preserving current cluster structure as much as possible. In this manner, while QoS does not perform well under high traffic load conditions, admission control becomes necessary in order to provide and support the QoS of existing members. Based on the results from the proposed analytical model, we implement a new admission control algorithm that provides the desired throughput and access delay performance in order to determine the number of members inside an ECA cluster that can be accommodated while satisfying the constraints imposed by the current applications. Through numerical analysis and simulations, we have studied the performance of our model and compared it with that of WCA. The results demonstrate the superior performance of the proposed model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".