An efficient approach for data transmission in power-constrained wireless sensor network
Bibliographic record
Abstract
Technological advances have made Wireless Sensor Networks (WSN) more reliable, low cost, and widely used in a variety of applications. As WSN nodes are low power battery operated with limited lifespan, optimizing their resources (sensing, channel use, computing) is an essential task for the success of applications implemented over these networks. Even though this task could be challenging for large WSN, its impact in terms of network survivability and economic benefits can be significant. In this paper, we will focus on optimizing radio data transmission and evaluate its impact on power saving. We will analyze the data collected from a large WSN, more than 20,000 nodes, used for water meter readings in the City of Moncton. Two data driven approaches, reduction and prediction-based, are evaluated and compared. Then we propose a system for data collection and transmission that improves power consumption, in comparison with the existing one, without affecting the effectiveness of the application in terms of water consumption monitoring and detection of leaks. Experimental results show substantial power savings and significant increases in battery lifespan.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.001 |
| 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".