A Novel Static Cluster-Based Hierarchical Protocol for Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor networks have recently gained a lot of attention from the scientific community due to their very wide spectrum of applications. In such networks, the sensor nodes have limited resources. These constraints impose many challenges to the design of related protocols. Especially, routing protocols should be energy-efficient for the prolonged network lifetime. The LEACH protocol is the most popular energy-efficient hierarchical clustering protocol for WSNs that was proposed for reducing power consumption. However, LEACH suffers from several drawbacks such as the non uniform distribution of Cluster Head nodes, the possibility of choosing a low energy node as Cluster Head, etc. In this paper, an attempt is made to overcome this shortcoming by introducing a new hierarchical clustering protocol, called SCHP (Static Cluster-based Hierarchical Protocol). The SCHP protocol is based on a static cluster creation and an optimal cluster head selection. Simulation results show that the proposal guarantees better performance than the LEACH Protocol that is considered as the baseline in the literature. We used many metrics, as packet loss rate, end-to-end delay, and energy consumption to evaluate the efficiency of our proposal. We show also that the SCHP protocol can improve the network lifetime.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".