Data relaying with optimal resource management in wireless sensor networks
Bibliographic record
Abstract
In this paper, the concern is with the management of data traffic after node deployment. To address some of the shortcomings of both single and multihop communication, this paper work with a hybrid model. The WSNet architecture is comprised of three classes of sensor nodes, sensors, relay nodes, and relay gateways. Sensors - gather and send data values to one relay nodes. Relay nodes - receive data values from sensors and/or other relay nodes and forward them to relay nodes or relay gateways. Relay gateways - receive data values from RN and send them directly (in one hop) to the base station, possibly using a dedicated channel. Our goal is to select paths along which data packets can be relayed until they reach one or more relay gateways while satisfying several constraints. These decisions take place at the application level, are computed by a central algorithm running at the base station, and rely on routing protocols to deliver the messages. A fixed topology of directly communicating nodes were assumed and a long term data communication plan based on knowing the amount of data generated by sensor nodes. The optimality of the solution obtained was guaranteed
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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".