Optimizing Cluster Head Selection in Mobile Ad Hoc Networks: A Connectivity Probability Approach Using Poisson Distribution and Residual Energy
Bibliographic record
Abstract
The selection of an optimal cluster head (CH) node, acting as an intermediary between the base station and ancillary nodes, is a pivotal challenge in mobile Ad Hoc networks, significantly impacting network performance and efficiency.This study introduces a novel approach for CH node selection in Mobile Ad Hoc Networks (MANETs), aiming to bolster network efficiency.This approach selects the head of a nodes cluster based on the Connectivity Probability (CP), derived from the Poisson probability and each node's residual energy.The Poisson distribution, governed by a key parameter, lambda, serves to determine the likelihood of a specific outcome.In the context of this study, lambda signifies the average distance between any given node and the base station, measured relative to their communication range.Considering the direct influence of a node's residual energy on connection probability, the product of these two parameters is computed to ascertain CP.Nodes demonstrating the highest CP values are considered prime candidates for CH selection.The efficacy of the proposed algorithm was evaluated against established protocols, namely the LEACH and R-LEACH.Simulation results suggest that the proposed algorithm significantly improves network performance, extending the network lifetime by over 6 times and 3 times that of LEACH and R-LEACH, respectively.It also enhances network stability by approximately 7 times compared to LEACH and over 2 times that of R-LEACH.Furthermore, the algorithm notably improves throughput, exhibiting an increase by approximately 7 times relative to both protocols.In conclusion, the proposed algorithm offers a promising strategy for CH node selection in MANETs, potentially benefitting a wide gamut of applications.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".