Clustering concept and QoS constraints in dense mobile ad hoc networks
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
In mobile ad hoc networks, many routing protocols use broadcast mechanism to find route whereas one of the wireless network challenges is the bandwidth optimisation. This mechanism increases the control overhead and consumes bandwidth and energy. The overhead penalty increases with the density and the network size. Thus is important to reduce the number of participants in that mechanism. One of the used solutions consists of determining clusterheads nodes. In this paper, we propose an algorithm for choosing clusterheads (SRCAC). Each node in the network broadcasts its ID, status and election index. The election index is a combination of potentiality index and stability index. The potentiality index is a linear combination of the mobile resources like processor speed, RAM and ROM memories. The stability index shows the cluster life time. Then, we use SRCAC to build clusters. We suggest a backup clusterhead which could become the principal clusterhead when the first breaks down. To improve the reliability of interclusters communications, we build a mesh between gateway and distributed gateway. Moreover, we introduce Quality of Service (QoS) constraints in the ODMRP (On Demand Multicast Routing Protocol) mobility prediction version.We compare our algorithm with the WCA (Weighted Clustering Algorithm for Mobile Ad hoc Networks) algorithm in terms of clusterheads. The results show that our algorithm performs better than the WCA and, finally, the deterioration of the performances imposed by the constraints on this ODMRP version is unimportant.
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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 | 0.000 | 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