A Matrix Completion Approach to Reduce Energy Consumption in Wireless Sensor Networks
Bibliographic record
Abstract
The main challenge faced by wireless sensor networks today is the problem of power consumption at the sensor nodes. Over time, researchers have developed different strategies to address this issue. Such strategies are strongly model dependent and/or application specific. In this work, we take a fresh look at the problem of power consumption in wireless sensor networks from a signal processing perspective. The main idea is simple. Sample only a subset of all the sensor nodes at a given instant and transmit them (this reduces both sampling and communication cost for all the nodes combined). At the central unit (sink) use smart mathematical tools (matrix completion algorithms) to estimate the data for the entire network. We have showed that, if about 1% reconstruction error is allowed, only 20% of the sensors need to sample and transmit at a given instant. This means on an average the life of the network is increased 5-fold. If more error reconstruction error is allowed, even lesser number of sensors need to be active at a given instant leading to more prolonged life 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 | 0.001 |
| 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".