A new energy efficient approach by separating data collection and data report in wireless sensor networks
Bibliographic record
Abstract
A sensor network consists of a large number of distributed wireless sensors which are equipped with low power wireless transceivers. Network lifetime, scalability, and load balancing are important requirements for many data gathering sensor network applications. Network clustering is an effective approach for achieving these goals wherein sensors are grouped into multiple clusters. Multihop data transmission may provide efficient energy conservation further. A sensor in a cluster may work as a cluster head for data gathering, aggregation, and report to the base station (BS). These tasks can also be performed by distinct sensors in the same cluster. In this paper, we clarify some characteristics of multi-hop data transmission. Furthermore, we propose a new data gathering approach for single-hop transmission wherein both the data gathering and the aggregation are performed by the same sensor in a cluster but the report to the BS may be done by a different sensor. The simulation results show that the proposed algorithm achieves better performance than other existing algorithms.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".