A Low-Maintenance Energy-Aware Clustering Algorithm for Wireless Ad-hoc Networks
Bibliographic record
Abstract
Clustering has often been used to impose structure in wireless ad hoc networks. In this work, we propose a modified lowest-ID clustering algorithm that tries to increase the stability of the created clusters. A stability factor is associated with nodes to improve the stability of clusters produced. The stability parameter is a measure of the time that a cluster head starts its leadership role. In our algorithm, nodes use periodic beacons as the only means of communications with its neighbors. The stability parameter is defined in one of the fields of the beacons. Nodes contend to become cluster head; the node with a lower ID and larger stability factor wins the contention. Since cluster heads have extra functionality and therefore consume more energy compared to the other nodes in the network, we propose an energy efficient load balancing mechanism on the created clusters based on their energy levels. To balance the energy consumption among the nodes, a cluster head retires after some time and hands over its role to another neighbor cluster head with higher energy levels. This is useful for prolonging the network lifetime. We demonstrate using simulations that our algorithm improves the average residual energy of the network as well as the stability of the clusters produced
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".