DRIFT: Differentiated RF Power Transmission for Wireless Sensor Network deployment in the smart grid
Bibliographic record
Abstract
Smart grid calls for low-cost, fine-grained and long-lasting monitoring solutions, to be able to provide reliable service to customers, enhance situational awareness capabilities and refine the operation of the grid and the microgrids, in addition to enabling prompt utility reaction to emergencies. Wireless Sensor Networks (WSNs) are promising candidates for monitoring the smart grid, given their capability to cover a large geographic region at low-cost. However, they do not provide long-lasting operation capability due to the limited battery lifetime of the sensors. Particularly, when sensors are deployed in hard-to-reach or hazardous environments, replacing the batteries of the sensors increase the cost of monitoring significantly. In the literature, energy-efficient protocols and ambient energy harvesting have been proposed to extend the lifetime of the sensors while neither of those offer a concrete solution for the smart grid. In this context, recent advances in Radio Frequency (RF) energy harvesting offers a unique solution to make WSNs operationally ready for smart grid monitoring tasks. Studies on RF energy harvesting have focused on uniform power delivery to all sensors, however it becomes essential to differentiate between critical zones and less critical zones in smart grid monitoring tasks. In this paper, we propose the Differentiated RF Power Transmission (DRIFT) scheme which is based on an Integer Linear Programming (ILP) model that maximizes the power received by the high priority sensor nodes. We compare the performance of DRIFT with a path length minimizing approach, namely Sustainable wireless Rechargeable Sensor network (SuReSense). We show that DRIFT is able to provide more power to high priority nodes than SuReSense. We also show that there is a tradeoff between power maximization and path length minimization.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".