Data collection using rendezvous points and mobile actor in wireless sensor networks
Bibliographic record
Abstract
In this paper we present a new data collection scheme using mobile actors in a large scale wireless sensor network (WSN). A mobile actor, or for convenience, M-actor is a mobile node that has powerful energy source, computation, and communication features. The mobile node is able to move freely through the sensor deployment, traversing through the radio transmission range of wireless sensor nodes to collect the sensed data. Once data from all sensors is collected, the M-actor returns to the base station to off-load the collected data. This paper makes two contributions. First, we present a heuristic to compute collection points, referred to as the rendezvous points (RPs). These points are computed such that full coverage is guaranteed. Second, the optimal path for the M-actor is modeled using a genetic algorithm (GA)based traveling salesman problem (TSP). The proposed scheme is evaluated through simulations. We demonstrate that the proposed scheme achieves significant improvement in reducing the tour length for the M-actor when compared with other schemes.
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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 | 0.001 | 0.001 |
| 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".