Energy Efficient Selection of Computing Elements in Wireless Sensor Networks
Bibliographic record
Abstract
A wide range of wireless sensor network applications are characterized by local processing of the sensed data and only meager data communication requirements. Indeed, because sensor nodes are battery powered and wireless communication bears a high energy cost, data transmission can be traded for on-the-node computation to extend node and network lifetime. Furthermore, the energy consumption can be reduced significantly by selecting and realizing the application on an appropriate processing element. In this article, we propose a new statistical technique for energy consumption estimation for a specific application on various platforms. We have empirically verified the methodology on various classes of embedded processors commonly used in sensor nodes. The methodology can also be applied to multiprocessor platforms. Our solution is not only capable to achieve high accuracy but also facilitates the application developer to evaluate different platforms without actually implementing the application on each of these platforms. Our experimental evaluation results for various platforms will help to understand the implications of using different processing elements and their effects on the lifetime of the network.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".