Implementation and emulation of distributed clustering protocols for wireless sensor networks
Bibliographic record
Abstract
Clustering is an effective technique for improving the energy efficiency and prolonging the network lifetime of a Wireless Sensor Network (WSN). Although it has been widely investigated, most of the studies are based simplified channel conditions and simple computer simulations using tools such as NS-2 and OPNET. It is highly desired that the algorithms proposed for WSN are studied in real network environment and using practical devices so that the obtained results provide a better guideline for real applications. In this paper, we study the implementation of distributed clustering protocols in WSNs. The performance of two popular schemes, HEED and HIDCA protocols, are studied using an emulation approach. It is further verified through our experimental work using Mica2 sensor devices from the Crossbow Technology Inc. Our results demonstrate the working of clustering algorithms in practical small scale networked sensor systems. It also confirms the superior performance of the HEED algorithm over HIDCA in terms of power consumption and network lifetime.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".