Mobility-aware pro-active low energy (MAPLE) clustering in ad hoc mobile wireless networks
Bibliographic record
Abstract
We propose a framework for mobility-aware pro-active low energy (MAPLE) clustering in ad hoc mobile wireless networks. Most of the clustering approaches proposed in the literature primarily focus on the algorithmic aspects of clustering without considering the practical implementation issues, and these are often reactive in nature. The proposed approach addresses the problem of clustering in a medium-access control framework and enables pro-active and energy-efficient clustering exploiting the node mobility information. In particular, for pro-active clustering, we introduce a method to exploit the link level information to estimate the mobility pattern of the wireless nodes and the cost of using a wireless link in terms of required transmission power. A channel reservation technique is used to reduce the number of contentions among the nodes while accessing the channel during cluster formation. Simulation results show that the proposed framework results in superior clustering performance in terms of control overhead, average number of link failures and load distribution compared to other clustering approaches proposed in the literature, such as the LCC-LID (least cluster change lowest ID)-based clustering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".