Minimum Energy Data Gathering in Correlated Sensor Networks with Cooperative Transmission
Bibliographic record
Abstract
We consider combination of distributed source coding (DSC) and cooperative transmission techniques to improve energy efficiency in sensor networks. To start with we formulate the data gathering problem in correlated wireless sensor networks with cooperative multiple input and multiple output (MIMO) transmission at the physical layer and DSC at the application layer. Using the concepts of super and sub modularity on a lattice, we analytically quantify as how the optimal constellation size and the optimal number of cooperating nodes vary with respect to the correlation coefficient. In particular, we show that the optimal constellation size is an increasing function of the correlation coefficient. For the MIMO transmission case, the optimal number of cooperating nodes is a decreasing function of the correlation coefficient. We also prove that in a MIMO transmission based scheme the optimal constellation size adopted by each cooperating node is a decreasing function of the number of cooperating nodes. Also, it is shown that the optimal number of cooperating nodes is a decreasing function of the constellation size adopted by each cooperating node. Finally through our numerical results, it is shown that significant energy savings can be obtained if correlation in the network is exploited. Also, when the desired probability of error is small MIMO transmission can lead to large scale energy savings.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".