An Energy-Efficient Cooperative Algorithm for Data Estimation in Wireless Sensor Networks
Why this work is in the frame
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Bibliographic record
Abstract
In wireless sensor networks (WSN), nodes operate on batteries and network's lifetime depends on energy consumption of the nodes. Consider the class of sensor networks where all nodes sense a single phenomenon at different locations and send messages to a fusion center (FC) in order to estimate the actual information. In classical systems all data processing tasks are done in the FC and there is no processing or compression before transmission. In the proposed algorithm, network is divided into clusters and data processing is done in two parts. The first part is performed in each cluster at the sensor nodes after local data sharing and the second part will be done at the fusion center after receiving all messages from clusters. Local data sharing results in more efficient data transmission in terms of number of bits. We also take advantage of having the same copy of data at all nodes of each cluster and suggest a virtual multiple-input multiple-output (V-MIMO) architecture for data transmission from clusters to the FC. A Virtual-MIMO network is a set of distributed nodes each having one antenna. By sharing their data among themselves, these nodes turn into a classical MIMO system. In the previously proposed cooperative/virtual MIMO architectures there has not been any data processing or compression in the conference phase. We modify the existing V-MIMO algorithms to suit the specific class of sensor networks that is of our concern. We use orthogonal space-time block codes (STBC) for MIMO part and by simulation show that this algorithm saves considerable energy compared to classical systems.
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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 | 0.001 |
| 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 it