Particle swarm optimization protocol for clustering in wireless sensor networks: A realistic approach
Bibliographic record
Abstract
In Wireless Sensor Network (WSN), Clustering sensor nodes is an efficient topology control method to reduce energy consumption of the sensor nodes. Many link quality-based clustering techniques have been proposed in the literature. However, they assumed that each sensor node is equipped with a self-locating hardware such as GPS. Though this is a simple solution, the resulting cost renders that solution inefficient and unrealistic. Furthermore, several studies has shown that link quality in WSN is not correlated with distance. In addition to that, they used an energy model that is fundamentally flawed for modelling radio power consumption in sensor networks. They ignore the listening energy consumption, which is known to be the largest contributor to expended energy in WSN. Clustering is a Non-deterministic Polynomial (NP)-hard problem for a WSN. Particle Swarm Optimization (PSO) is a swarm intelligent approach that can be applied for finding fast and efficient solutions of such problem. In this paper, a PSO-based protocol is used to find the optimal set of cluster heads that maximize the network coverage, energy efficiency and link quality. The effect of using a realistic network and energy consumption model in cluster-based communication for WSN was investigated. Numerical simulations demonstrate the effectiveness of the proposed protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".